From cf7f67a146d4355806d35f960dc02a4c23f91b63 Mon Sep 17 00:00:00 2001 From: Agus <56895847+plaguss@users.noreply.github.com> Date: Wed, 29 Nov 2023 17:16:02 +0100 Subject: [PATCH] docs: add end2end example on creating a basic text-classification dataset (#4208) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # Description This PR includes 2 features towards the #4178 issue. - A tutorial for the creation of a `FeedbackDataset` for `text-classification`. - A new script has been added to run the notebooks automatically, via end2end.yml workflow. Closes #4179 and #4220 **Type of change** (Remember to title the PR according to the type of change) - [ ] Documentation update **How Has This Been Tested** (Please describe the tests that you ran to verify your changes.) - [ ] `sphinx-autobuild` (read [Developer Documentation](https://docs.argilla.io/en/latest/community/developer_docs.html#building-the-documentation) for more details) **Checklist** - [ ] I added relevant documentation - [x] I followed the style guidelines of this project - [x] I did a self-review of my code - [ ] I made corresponding changes to the documentation - [ ] My changes generate no new warnings - [ ] I filled out [the contributor form](https://tally.so/r/n9XrxK) (see text above) - [ ] I have added relevant notes to the `CHANGELOG.md` file (See https://keepachangelog.com/) --------- Co-authored-by: Agustin Piqueres Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: davidberenstein1957 Co-authored-by: kursathalat <86690946+kursathalat@users.noreply.github.com> Co-authored-by: sdiazlor Co-authored-by: Sara Han <127759186+sdiazlor@users.noreply.github.com> --- .github/workflows/check-repo-files.yml | 10 + .github/workflows/end2end-examples.yml | 98 ++ .github/workflows/package.yml | 22 + docs/_source/_static/images/llms/fb-model.svg | 3 +- .../tutorials/end2end/base/workflow.drawio | 153 ++ .../tutorials/end2end/base/workflow.png | Bin 0 -> 60822 bytes .../tutorials/end2end/base/workflow.svg | 4 + .../end2end/base/workflow_add_records.png | Bin 0 -> 95494 bytes .../end2end/base/workflow_add_records.svg | 4 + .../end2end/base/workflow_assign_records.png | Bin 0 -> 95025 bytes .../end2end/base/workflow_assign_records.svg | 4 + .../end2end/base/workflow_configure_users.svg | 4 + .../end2end/base/workflow_create_dataset.png | Bin 0 -> 95797 bytes .../end2end/base/workflow_create_dataset.svg | 4 + .../end2end/base/workflow_metadata.png | Bin 0 -> 94603 bytes .../end2end/base/workflow_metadata.svg | 4 + .../end2end/base/workflow_metrics.png | Bin 0 -> 95156 bytes .../end2end/base/workflow_metrics.svg | 4 + .../end2end/base/workflow_sort_and_filter.png | Bin 0 -> 95269 bytes .../end2end/base/workflow_sort_and_filter.svg | 4 + .../workflow_suggestions_and_responses.png | Bin 0 -> 94588 bytes .../workflow_suggestions_and_responses.svg | 4 + .../end2end/base/workflow_train_models.png | Bin 0 -> 94870 bytes .../end2end/base/workflow_train_models.svg | 4 + .../base/workflow_users_and_workspaces.png | Bin 0 -> 95015 bytes .../base/workflow_users_and_workspaces.svg | 4 + .../end2end/base/workflow_vectors.png | Bin 0 -> 94908 bytes .../end2end/base/workflow_vectors.svg | 4 + .../argilla-annotation-ui-responses.png | Bin 0 -> 274934 bytes .../argilla-annotation-ui-suggestions.png | Bin 0 -> 259875 bytes .../argilla-annotation-ui-vectors.png | Bin 0 -> 293955 bytes .../argilla-ui-page-metadata.png | Bin 0 -> 287184 bytes ...feedback-dataset-text-classification-1.png | Bin 0 -> 65253 bytes .../filter-by-metadata.PNG | Bin 0 -> 45579 bytes .../text-classification/filter-by-status.PNG | Bin 0 -> 19744 bytes .../text-classification/filter_by_fields.PNG | Bin 0 -> 36374 bytes .../end2end/text-classification/group.PNG | Bin 0 -> 15092 bytes .../text-classification/group_personal.PNG | Bin 0 -> 49119 bytes .../text-classification/individual.PNG | Bin 0 -> 74928 bytes docs/_source/conceptual_guides/data_model.md | 9 + .../configurations/user_management.md | 4 +- .../practical_guides/create_dataset.md | 4 +- .../practical_guides/practical_guides.md | 2 + .../end2end_examples/add-metadata-003.ipynb | 1030 ++++++++++++++ .../add-suggestions-and-responses-005.ipynb | 582 ++++++++ .../end2end_examples/add-vectors-004.ipynb | 622 +++++++++ .../end2end_examples/assign-records-002.ipynb | 700 ++++++++++ .../configure-users-and-workspaces-000.ipynb | 757 ++++++++++ .../end2end_examples/create-dataset-001.ipynb | 1235 +++++++++++++++++ .../filter-and-query-008.ipynb | 679 +++++++++ .../end2end_examples/train-model-006.ipynb | 462 ++++++ .../end2end_examples/use-metrics-007.ipynb | 57 + .../tutorials/tutorials.md | 59 +- scripts/end2end_examples.py | 133 ++ .../client/feedback/utils/assignment.py | 4 +- 55 files changed, 6665 insertions(+), 8 deletions(-) create mode 100644 .github/workflows/end2end-examples.yml create mode 100644 docs/_source/_static/tutorials/end2end/base/workflow.drawio create mode 100644 docs/_source/_static/tutorials/end2end/base/workflow.png create mode 100644 docs/_source/_static/tutorials/end2end/base/workflow.svg create mode 100644 docs/_source/_static/tutorials/end2end/base/workflow_add_records.png create mode 100644 docs/_source/_static/tutorials/end2end/base/workflow_add_records.svg create mode 100644 docs/_source/_static/tutorials/end2end/base/workflow_assign_records.png create mode 100644 docs/_source/_static/tutorials/end2end/base/workflow_assign_records.svg create mode 100644 docs/_source/_static/tutorials/end2end/base/workflow_configure_users.svg create mode 100644 docs/_source/_static/tutorials/end2end/base/workflow_create_dataset.png create mode 100644 docs/_source/_static/tutorials/end2end/base/workflow_create_dataset.svg create mode 100644 docs/_source/_static/tutorials/end2end/base/workflow_metadata.png create mode 100644 docs/_source/_static/tutorials/end2end/base/workflow_metadata.svg create mode 100644 docs/_source/_static/tutorials/end2end/base/workflow_metrics.png create mode 100644 docs/_source/_static/tutorials/end2end/base/workflow_metrics.svg create mode 100644 docs/_source/_static/tutorials/end2end/base/workflow_sort_and_filter.png create mode 100644 docs/_source/_static/tutorials/end2end/base/workflow_sort_and_filter.svg create mode 100644 docs/_source/_static/tutorials/end2end/base/workflow_suggestions_and_responses.png create mode 100644 docs/_source/_static/tutorials/end2end/base/workflow_suggestions_and_responses.svg create mode 100644 docs/_source/_static/tutorials/end2end/base/workflow_train_models.png create mode 100644 docs/_source/_static/tutorials/end2end/base/workflow_train_models.svg create mode 100644 docs/_source/_static/tutorials/end2end/base/workflow_users_and_workspaces.png create mode 100644 docs/_source/_static/tutorials/end2end/base/workflow_users_and_workspaces.svg create mode 100644 docs/_source/_static/tutorials/end2end/base/workflow_vectors.png create mode 100644 docs/_source/_static/tutorials/end2end/base/workflow_vectors.svg create mode 100644 docs/_source/_static/tutorials/end2end/text-classification/argilla-annotation-ui-responses.png create mode 100644 docs/_source/_static/tutorials/end2end/text-classification/argilla-annotation-ui-suggestions.png create mode 100644 docs/_source/_static/tutorials/end2end/text-classification/argilla-annotation-ui-vectors.png create mode 100644 docs/_source/_static/tutorials/end2end/text-classification/argilla-ui-page-metadata.png create mode 100644 docs/_source/_static/tutorials/end2end/text-classification/feedback-dataset-text-classification-1.png create mode 100644 docs/_source/_static/tutorials/end2end/text-classification/filter-by-metadata.PNG create mode 100644 docs/_source/_static/tutorials/end2end/text-classification/filter-by-status.PNG create mode 100644 docs/_source/_static/tutorials/end2end/text-classification/filter_by_fields.PNG create mode 100644 docs/_source/_static/tutorials/end2end/text-classification/group.PNG create mode 100644 docs/_source/_static/tutorials/end2end/text-classification/group_personal.PNG create mode 100644 docs/_source/_static/tutorials/end2end/text-classification/individual.PNG create mode 100644 docs/_source/tutorials_and_integrations/tutorials/feedback/end2end_examples/add-metadata-003.ipynb create mode 100644 docs/_source/tutorials_and_integrations/tutorials/feedback/end2end_examples/add-suggestions-and-responses-005.ipynb create mode 100644 docs/_source/tutorials_and_integrations/tutorials/feedback/end2end_examples/add-vectors-004.ipynb create mode 100644 docs/_source/tutorials_and_integrations/tutorials/feedback/end2end_examples/assign-records-002.ipynb create mode 100644 docs/_source/tutorials_and_integrations/tutorials/feedback/end2end_examples/configure-users-and-workspaces-000.ipynb create mode 100644 docs/_source/tutorials_and_integrations/tutorials/feedback/end2end_examples/create-dataset-001.ipynb create mode 100644 docs/_source/tutorials_and_integrations/tutorials/feedback/end2end_examples/filter-and-query-008.ipynb create mode 100644 docs/_source/tutorials_and_integrations/tutorials/feedback/end2end_examples/train-model-006.ipynb create mode 100644 docs/_source/tutorials_and_integrations/tutorials/feedback/end2end_examples/use-metrics-007.ipynb create mode 100644 scripts/end2end_examples.py diff --git a/.github/workflows/check-repo-files.yml b/.github/workflows/check-repo-files.yml index 52ecf4c4fd..d97939398b 100644 --- a/.github/workflows/check-repo-files.yml +++ b/.github/workflows/check-repo-files.yml @@ -6,6 +6,9 @@ on: pythonChanges: description: "True if some files in python code have changed" value: ${{ jobs.check-repo-files.outputs.pythonChanges }} + end2endChanges: + description: "True if some files in python code have changed" + value: ${{ jobs.check-repo-files.outputs.end2endChanges }} buildChanges: description: "True if some files affecting the build have changed" value: ${{ jobs.check-repo-files.outputs.buildChanges }} @@ -17,6 +20,7 @@ jobs: outputs: pythonChanges: ${{ steps.path_filter.outputs.pythonChanges }} buildChanges: ${{ steps.path_filter.outputs.buildChanges }} + end2endChanges: ${{ steps.path_filter.outputs.end2endChanges }} steps: - name: Checkout Code 🛎 uses: actions/checkout@v3 @@ -30,6 +34,12 @@ jobs: - 'tests/**' - 'pyproject.toml' - 'setup.py' + end2endChanges: + - 'src/**' + - 'pyproject.toml' + - 'setup.py' + - 'scripts/end2end_examples.py' + - 'docs/_source/tutorials_and_integrations/tutorials/feedback/end2end_examples/**' buildChanges: - 'src/**' - 'frontend/**' diff --git a/.github/workflows/end2end-examples.yml b/.github/workflows/end2end-examples.yml new file mode 100644 index 0000000000..05b22e5a89 --- /dev/null +++ b/.github/workflows/end2end-examples.yml @@ -0,0 +1,98 @@ +name: Run end2end sdk examples + +on: + workflow_call: + inputs: + runsOn: + required: false + type: string + default: extended-runner + searchEngineDockerImage: + description: "The name of the Docker image of the search engine to use." + default: docker.elastic.co/elasticsearch/elasticsearch:8.8.2 + required: false + type: string + searchEngineDockerEnv: + description: "The name of the Docker image of the search engine to use." + default: '{"discovery.type": "single-node", "xpack.security.enabled": "false"}' + required: false + type: string +env: + # Increase this value to reset cache if etc/example-environment.yml has not changed + CACHE_NUMBER: 5 + +jobs: + # Runs depending on the result from the check-repo-files.yml + call-check-repo-files: + uses: ./.github/workflows/check-repo-files.yml + + end2end-examples: + name: end2end notebook examples, FeedbackDataset for text-classification + runs-on: ${{ inputs.runsOn }} + services: + search_engine: + image: ${{ inputs.searchEngineDockerImage }} + ports: + - 9200:9200 + env: ${{ fromJson(inputs.searchEngineDockerEnv) }} + defaults: + run: + shell: bash -l {0} + steps: + - name: Checkout Code 🛎 + uses: actions/checkout@v3 + - name: Setup Conda Env 🐍 + uses: conda-incubator/setup-miniconda@v2 + with: + miniforge-variant: Mambaforge + miniforge-version: latest + use-mamba: true + activate-environment: argilla + - name: Get date for conda cache + id: get-date + run: echo "::set-output name=today::$(/bin/date -u '+%Y%m%d')" + shell: bash + - name: Cache Conda env + uses: actions/cache@v3 + id: cache + with: + path: ${{ env.CONDA }}/envs + key: conda-${{ runner.os }}--${{ runner.arch }}--${{ steps.get-date.outputs.today }}-${{ hashFiles('environment_dev.yml') }}-${{ env.CACHE_NUMBER }} + - name: Update environment + if: steps.cache.outputs.cache-hit != 'true' + run: mamba env update -n argilla -f environment_dev.yml + - name: Cache pip 👜 + uses: actions/cache@v3 + with: + path: ~/.cache/pip + key: ${{ runner.os }}-pip-${{ env.CACHE_NUMBER }}-${{ hashFiles('pyproject.toml') }} + - name: Set huggingface hub credentials + if: github.ref == 'refs/heads/main' || github.ref == 'refs/heads/develop' || startsWith(github.ref, 'refs/heads/releases') + run: | + echo "HF_HUB_ACCESS_TOKEN=${{ secrets.HF_HUB_ACCESS_TOKEN }}" >> "$GITHUB_ENV" + echo "Enable HF access token" + - name: Set Argilla search engine env variable + if: startsWith(inputs.searchEngineDockerImage, 'docker.elastic.co') + run: | + echo "ARGILLA_SEARCH_ENGINE=elasticsearch" >> "$GITHUB_ENV" + echo "Configure elasticsearch engine" + - name: Set Argilla search engine env variable + if: startsWith(inputs.searchEngineDockerImage, 'opensearchproject') + run: | + echo "ARGILLA_SEARCH_ENGINE=opensearch" >> "$GITHUB_ENV" + echo "Configure opensearch engine" + - name: Launch Argilla Server + env: + ARGILLA_ENABLE_TELEMETRY: 0 + run: | + pip install -e . + python -m argilla server database migrate + python -m argilla server database users create_default + uvicorn argilla.server.app:app --port 6900 --host 0.0.0.0 & + - name: Run end2end examples 📈 + env: + ARGILLA_ENABLE_TELEMETRY: 0 + HF_HUB_ACCESS_TOKEN: ${{ secrets.HF_HUB_ACCESS_TOKEN }} + run: | + pip install papermill + python scripts/end2end_examples.py diff --git a/.github/workflows/package.yml b/.github/workflows/package.yml index e5a6dd4b22..79de713d88 100644 --- a/.github/workflows/package.yml +++ b/.github/workflows/package.yml @@ -74,6 +74,28 @@ jobs: pytestArgs: tests/unit secrets: inherit + run_end2end_tests: + strategy: + matrix: + include: + - searchEngineDockerImage: docker.elastic.co/elasticsearch/elasticsearch:8.8.2 + searchEngineDockerEnv: '{"discovery.type": "single-node", "xpack.security.enabled": "false"}' + coverageReport: coverage-elasticsearch-8.8.2 + runsOn: extended-runner + - searchEngineDockerImage: opensearchproject/opensearch:2.4.1 + searchEngineDockerEnv: '{"discovery.type": "single-node", "plugins.security.disabled": "true"}' + coverageReport: coverage-opensearch-2.4.1 + runsOn: ubuntu-latest + name: Run end2end tests + uses: ./.github/workflows/end2end-examples.yml + needs: check_repo_files + if: needs.check_repo_files.outputs.end2endChanges == 'true' + with: + runsOn: ${{ matrix.runsOn }} + searchEngineDockerImage: ${{ matrix.searchEngineDockerImage }} + searchEngineDockerEnv: ${{ matrix.searchEngineDockerEnv }} + secrets: inherit + run_unit_test_with_extra_engines: strategy: matrix: diff --git a/docs/_source/_static/images/llms/fb-model.svg b/docs/_source/_static/images/llms/fb-model.svg index 0c7751e622..b893f39df8 100644 --- 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Configure dataset
Configure dataset...



assign records
to team
assign records...



metadata
metadata...



vectors
vectors...



suggestions
and
responses
suggestions...



Add records
Add records...



sort and filter
sort and filter...



metrics
metrics...



train models
train models...



configure users and workspaces
configure users a...
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Configure dataset
Configure dataset...



assign records
to team
assign records...



metadata
metadata...



vectors
vectors...



suggestions
and
responses
suggestions...



Add records
Add records...



sort and filter
sort and filter...



metrics
metrics...



train models
train models...



configure users and workspaces
configure users a...
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Configure dataset
Configure dataset...



assign records
to team
assign records...



metadata
metadata...



vectors
vectors...



suggestions
and
responses
suggestions...



Add records
Add records...



sort and filter
sort and filter...



metrics
metrics...



train models
train models...



configure users and workspaces
configure users a...
Text is not SVG - cannot display
\ No newline at end of file diff --git a/docs/_source/_static/tutorials/end2end/base/workflow_configure_users.svg b/docs/_source/_static/tutorials/end2end/base/workflow_configure_users.svg new file mode 100644 index 0000000000..832859d619 --- /dev/null +++ b/docs/_source/_static/tutorials/end2end/base/workflow_configure_users.svg @@ -0,0 +1,4 @@ + + + +



Configure dataset
Configure dataset...



assign records
to team
assign records...



metadata
metadata...



vectors
vectors...



suggestions
and
responses
suggestions...



Add records
Add records...



sort and filter
sort and filter...



metrics
metrics...



train models
train models...



configure users and workspaces
configure users a...
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Configure dataset
Configure dataset...



assign records
to team
assign records...



metadata
metadata...



vectors
vectors...



suggestions
and
responses
suggestions...



Add records
Add records...



sort and filter
sort and filter...



metrics
metrics...



train models
train models...



configure users and workspaces
configure users a...
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Configure dataset
Configure dataset...



assign records
to team
assign records...



metadata
metadata...



vectors
vectors...



suggestions
and
responses
suggestions...



Add records
Add records...



sort and filter
sort and filter...



metrics
metrics...



train models
train models...



configure users and workspaces
configure users a...
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    Configure dataset
    Configure dataset...



    assign records
    to team
    assign records...



    metadata
    metadata...



    vectors
    vectors...



    suggestions
    and
    responses
    suggestions...



    Add records
    Add records...



    sort and filter
    sort and filter...



    metrics
    metrics...



    train models
    train models...



    configure users and workspaces
    configure users a...
    Text is not SVG - cannot display
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    Configure dataset
    Configure dataset...



    assign records
    to team
    assign records...



    metadata
    metadata...



    vectors
    vectors...



    suggestions
    and
    responses
    suggestions...



    Add records
    Add records...



    sort and filter
    sort and filter...



    metrics
    metrics...



    train models
    train models...



    configure users and workspaces
    configure users a...
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    Configure dataset
    Configure dataset...



    assign records
    to team
    assign records...



    metadata
    metadata...



    vectors
    vectors...



    suggestions
    and
    responses
    suggestions...



    Add records
    Add records...



    sort and filter
    sort and filter...



    metrics
    metrics...



    train models
    train models...



    configure users and workspaces
    configure users a...
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    Configure dataset
    Configure dataset...



    assign records
    to team
    assign records...



    metadata
    metadata...



    vectors
    vectors...



    suggestions
    and
    responses
    suggestions...



    Add records
    Add records...



    sort and filter
    sort and filter...



    metrics
    metrics...



    train models
    train models...



    configure users and workspaces
    configure users a...
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    Configure dataset
    Configure dataset...



    assign records
    to team
    assign records...



    metadata
    metadata...



    vectors
    vectors...



    suggestions
    and
    responses
    suggestions...



    Add records
    Add records...



    sort and filter
    sort and filter...



    metrics
    metrics...



    train models
    train models...



    configure users and workspaces
    configure users a...
    Text is not SVG - cannot display
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    Configure dataset
    Configure dataset...



    assign records
    to team
    assign records...



    metadata
    metadata...



    vectors
    vectors...



    suggestions
    and
    responses
    suggestions...



    Add records
    Add records...



    sort and filter
    sort and filter...



    metrics
    metrics...



    train models
    train models...



    configure users and workspaces
    configure users a...
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+44,19 @@ A **Record** represents an individual feedback data point within a dataset. It c Argilla allows for multiple concurrent annotators, seamlessly gathering feedback from many labelers. Each **Response** represents the input provided by a labeler in response to specific questions within a dataset. It includes the labeler's identification, the feedback value itself, and a status indicating whether the response has been submitted or discarded. These responses form the foundation of the collected feedback data, capturing the diverse perspectives and insights of the labelers. #### Suggestions + **Suggestions** enhance the feedback collection process by providing machine-generated feedback to labelers. Suggestions serve as automated decision-making aids, leveraging rules, models, or language models (LLMs) to accelerate the feedback process. With Suggestions, each record can be equipped with multiple machine-generated recommendations (one per question). These suggestions can act as weak signals, seamlessly combining with human feedback to enhance the efficiency and accuracy of the feedback collection workflow. By leveraging the power of automated suggestions, labelers can make more informed decisions, resulting in a more streamlined, partially automated, and effective feedback collection process. +#### Metadata + +**Metadata** will hold extra information that you want your record to have. For example, if it belongs to the training or the test dataset, a quick fact about something regarding that specific record. Feel free to use it as you need! + +#### Vector + +**Vectors** can be added to a record to represent the semantic meaning of a **field** or a combination of them. They can be used within the UI or the SDK to search the most or least similar records within your dataset. + ## Other datasets diff --git a/docs/_source/getting_started/installation/configurations/user_management.md b/docs/_source/getting_started/installation/configurations/user_management.md index 20e91ffe87..fda2696c99 100644 --- a/docs/_source/getting_started/installation/configurations/user_management.md +++ b/docs/_source/getting_started/installation/configurations/user_management.md @@ -35,7 +35,7 @@ An admin user can only access the workspaces it has been assigned to and cannot - **Annotation**: It can annotate datasets in the assigned workspaces via the Argilla UI. - **Feedback**: It can provide feedback with the Argilla UI. -## Annotator +### Annotator An annotator user is limited to accessing only the datasets assigned to it within the workspace. It has two specific permissions: @@ -113,7 +113,7 @@ argilla users create So for example, to create a new user with `admin` role and member of `ws1` and `ws2` workspaces, you can run the following command: ```bash -argilla users create --username new-user --first-name New --last-name User --role admin --workspace ws1 --workspace ws2 +argilla users create --username new-user --password new-password --first-name New --last-name User --role admin --workspace ws1 --workspace ws2 ``` ::: diff --git a/docs/_source/practical_guides/create_dataset.md b/docs/_source/practical_guides/create_dataset.md index eedf0ebcc1..f30ecabf34 100644 --- a/docs/_source/practical_guides/create_dataset.md +++ b/docs/_source/practical_guides/create_dataset.md @@ -5,7 +5,9 @@ ```{include} /_common/feedback_dataset.md ``` -The Feedback Task datasets allow you to combine multiple questions of different kinds, so the first step will be to define the aim of your project and the kind of data and feedback you will need to get there. With this information, you can start configuring a dataset and formatting records using the Python SDK. +The Feedback Task datasets allow you to combine multiple questions of different kinds, so the first step will be to define the aim of your project and the kind of data and feedback you will need to get there. With this information, you can start configuring a dataset and formatting records using the Python SDK. The image underneath provides a general step-by-step overview. For some end-to-end examples, you can have a look at our [tutorials page](/tutorials_and_integrations/tutorials/tutorials.md). + +![workflow](/_static/tutorials/end2end/base/workflow_create_dataset.svg) This guide will walk you through all the elements you will need to configure to create a `FeedbackDataset` and add records to it. diff --git a/docs/_source/practical_guides/practical_guides.md b/docs/_source/practical_guides/practical_guides.md index c04692cdc3..b4f8dca3b0 100644 --- a/docs/_source/practical_guides/practical_guides.md +++ b/docs/_source/practical_guides/practical_guides.md @@ -52,4 +52,6 @@ Fine-tune an LLM or other models with the feedback collected from Argilla. ``` ```` +![workflow](/_static/tutorials/end2end/base/workflow.svg) + ![Feedback Dataset snapshot](/_static/images/llms/snapshot-feedback-demo.png) \ No newline at end of file diff --git a/docs/_source/tutorials_and_integrations/tutorials/feedback/end2end_examples/add-metadata-003.ipynb b/docs/_source/tutorials_and_integrations/tutorials/feedback/end2end_examples/add-metadata-003.ipynb new file mode 100644 index 0000000000..fb6353e5ea --- /dev/null +++ b/docs/_source/tutorials_and_integrations/tutorials/feedback/end2end_examples/add-metadata-003.ipynb @@ -0,0 +1,1030 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "# Add Metadata Properties to `FeedbackDataset`" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "In this part of our tutorial series for `FeedbackDatasets`, we will show how to use metadata properties to enhance the dataset. We will pull the dataset, add various metadata properties and upload it back. You can have a look at the previous tutorials for [creating the dataset](./create-dataset-001.ipynb) and [configuring the users and workspaces](./configure-users-and-workspaces-000.ipynb). Feel free to check out the [practical guides](../../../../practical_guides/practical_guides.md) page for more in-depth information.\n", + "\n", + "Utilizing metadata properties is an effective means of enriching your dataset, enabling [effective filtering and sorting](./filter-and-query-008.ipynb). They are the pieces of information for each record that are accessible both in the UI and via SDK. Argilla provides various types of metadata properties, allowing you to tailor their use based on the requirements of your specific project. \n", + "\n", + "![workflow](../../../../_static/tutorials/end2end/base/workflow_metadata.svg)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Table of Contents\n", + "\n", + "1. [Load a FeedbackDataset](#Pull-the-Dataset)\n", + " 1. [From Argilla](#From-Argilla) \n", + " 2. [From the HuggingFace Hub](#From-HuggingFace-Hub)\n", + "2. [Add metadata properties](#Add-Metadata-Properties)\n", + " 1. [TermsMetadataProperty](#TermsMetadataProperty)\n", + " 2. [IntegerMetadataProperty](#IntegerMetadataProperty)\n", + " 3. [FloatMetadataProperty](#FloatMetadataProperty)\n", + "3. [Filter and Sort by Metadata](#Filter-by-Metadata)\n", + "4. [Push the dataset](#Push-the-Dataset)\n", + " 1. [To Argilla](#To-Argilla)\n", + " 2. [To HuggingFace Hub](#To-HuggingFace-Hub)\n", + "5. [Conclusion](#Conclusion)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Running Argilla\n", + "\n", + "For this tutorial, you will need to have an Argilla server running. There are two main options for deploying and running Argilla:\n", + "\n", + "**Deploy Argilla on Hugging Face Spaces:** If you want to run tutorials with external notebooks (e.g., Google Colab) and you have an account on Hugging Face, you can deploy Argilla on Spaces with a few clicks:\n", + "\n", + "[![deploy on spaces](https://huggingface.co/datasets/huggingface/badges/raw/main/deploy-to-spaces-lg.svg)](https://huggingface.co/new-space?template=argilla/argilla-template-space)\n", + "\n", + "For details about configuring your deployment, check the [official Hugging Face Hub guide](https://huggingface.co/docs/hub/spaces-sdks-docker-argilla).\n", + "\n", + "**Launch Argilla using Argilla's quickstart Docker image**: This is the recommended option if you want [Argilla running on your local machine](../../../../getting_started/quickstart.md). Note that this option will only let you run the tutorial locally and not with an external notebook service.\n", + "\n", + "For more information on deployment options, please check the Deployment section of the documentation.\n", + "\n", + "

    \n", + "\n", + "Tip\n", + "\n", + "This tutorial is a Jupyter Notebook. There are two options to run it:\n", + "\n", + "- Use the Open in Colab button at the top of this page. This option allows you to run the notebook directly on Google Colab. Don't forget to change the runtime type to GPU for faster model training and inference.\n", + "- Download the .ipynb file by clicking on the View source link at the top of the page. This option allows you to download the notebook and run it on your local machine or on a Jupyter notebook tool of your choice.\n", + "
    " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, let's install our dependencies and import the necessary libraries:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install argilla\n", + "!pip install datasets" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import argilla as rg\n", + "from argilla._constants import DEFAULT_API_KEY\n", + "import statistics\n", + "import random" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to run this notebook we will need some credentials to push and load datasets from `Argilla` and `🤗 Hub`, let's set them in the following cell:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "parameters" + ] + }, + "outputs": [], + "source": [ + "# Argilla credentials\n", + "api_url = \"http://localhost:6900\" # \"https://.hf.space\"\n", + "api_key = DEFAULT_API_KEY # admin.apikey\n", + "# Huggingface credentials\n", + "hf_token = \"hf_...\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Log in to argilla:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/davidberenstein/Documents/programming/argilla/argilla/src/argilla/client/client.py:154: UserWarning: Default user was detected and no workspace configuration was provided, so the default 'argilla' workspace will be used. If you want to setup another workspace, use the `rg.set_workspace` function or provide a different one on `rg.init`\n", + " warnings.warn(\n", + "/Users/davidberenstein/Documents/programming/argilla/argilla/src/argilla/client/client.py:182: UserWarning: You're connecting to Argilla Server 1.19.0 using a different client version (1.21.0-dev).\n", + "This may lead to potential compatibility issues during your experience.\n", + "To ensure a seamless and optimized connection, we highly recommend aligning your client version with the server version.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "rg.init(api_url=api_url, api_key=api_key)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Enable Telemetry\n", + "We gain valuable insights from how you interact with our tutorials. To improve ourselves in offering you the most suitable content, using the following lines of code will help us understand that this tutorial is serving you effectively. Though this is entirely anonymous, you can choose to skip this step if you prefer. For more info, please check out the Telemetry page.\n", + "\n", + "```python\n", + "from argilla.utils.telemetry import tutorial_running\n", + "\n", + "tutorial_running()\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pull the Dataset\n", + "\n", + "As we uploaded the dataset we created in the [previous tutorial](#previous_tutorial) to both Argilla and HuggingFace Hub, we can pull the dataset from either of them. Let us see how we can pull the dataset from both.\n", + "\n", + "### From Argilla\n", + "\n", + "We can pull the dataset from Argilla by using the `from_argilla` method. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "dataset_remote = rg.FeedbackDataset.from_argilla(\"end2end_textclassification\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### From HuggingFace Hub\n", + "\n", + "We can also pull the dataset from HuggingFace Hub. Similarly, we can use the `from_huggingface` method to pull the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Parsing records: 100%|██████████| 1000/1000 [00:00<00:00, 1669.20it/s]\n" + ] + } + ], + "source": [ + "dataset = rg.FeedbackDataset.from_huggingface(\"argilla/end2end_textclassification\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
    \n", + "\n", + "Note \n", + "\n", + "The dataset pulled from HuggingFace Hub is an instance of `FeedbackDataset` whereas the dataset pulled from Argilla is an instance of `RemoteFeedbackDataset`. The difference between the two is that the former is a local one and the changes made on it stay locally. On the other hand, the latter is a remote one and the changes made on it are directly reflected on the dataset on the Argilla server, which can make your process faster.\n", + "\n", + "
    " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us briefly examine what our dataset looks like. It is a dataset that consists of data items with the field `text` that is yet to be annotated." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'text': \"Wall St. Bears Claw Back Into the Black (Reuters) Reuters - Short-sellers, Wall Street's dwindling\\\\band of ultra-cynics, are seeing green again.\"}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset[0].fields" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add Metadata Properties\n", + "\n", + "Metadata properties are a great way to enhance the dataset for filtering and sorting, both in the UI and SDK. Argilla offers different types of metadata properties that you can make use of according to your needs for the specific project you are working on. For more information on metadata properties, please check out the [documentation](../../../../practical_guides/create_dataset.md#define-metadata).\n", + "\n", + "### TermsMetadataProperty\n", + "\n", + "The `TermsMetadaProperty` is a metadata property that can be used to filter the metadata of a record based on a list of possible terms or values." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "terms_metadata_property = rg.TermsMetadataProperty(\n", + " name=\"group\", title=\"Annotation Group\", values=[\"group-1\", \"group-2\", \"group-3\"]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that the `name` will be the name of the metadata property shown internally. `title` is again the name of the metadata property displayed in the UI. `values` will be the list of possible values for the metadata property. To add it to the dataset, we can simply use the `add_metadata_property` method." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "TermsMetadataProperty(name='group', title='Annotation Group', visible_for_annotators=True, type='terms', values=['group-1', 'group-2', 'group-3'])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset.add_metadata_property(terms_metadata_property)\n", + "dataset_remote.add_metadata_property(terms_metadata_property)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us add the metadata to individual records as well. For demonstration purposes here, we can create a list of groups and randomly select one to assign to each record. As we normally do, we should be careful to divide records into groups in equal numbers.\n", + "\n", + "We firstly learn how many records we have in the dataset and how many groups we have defined in our `TermsMetadataProperty`. Then, we compile a list of group names, whose length is equivalent to the number of records." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "length_dataset = len(dataset)\n", + "num_groups = len(terms_metadata_property.values)\n", + "\n", + "groups = [\n", + " f\"group-{i}\"\n", + " for i in range(1, num_groups + 1)\n", + " for _ in range(\n", + " length_dataset // num_groups + (1 if i <= length_dataset % num_groups else 0)\n", + " )\n", + "]\n", + "groups_local = groups.copy()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we choose one group name randomly, assign it to the record and remove it from the list.\n", + "\n", + "The adding process slightly differs among the `FeedbackDataset` and `RemoteFeedbackDataset`. For the former, we can simply add metadata by assigning the value. For the latter, we should use the `update_records` method. Let us see how we can do it for both." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# For the local dataset\n", + "for record in dataset.records:\n", + " random_record = random.choice(groups_local)\n", + " record.metadata[\"group\"] = random_record\n", + " groups_local.remove(random_record)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/agus/github_repos/argilla-io/argilla/src/argilla/client/feedback/dataset/local/dataset.py:535: UserWarning: `update_records` method only works for `FeedbackDataset` pushed to Argilla. If your are working with local data, you can just iterate over the records and update them.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "# For the remote dataset (if you run the previous cell, you should create the groups again)\n", + "modified_records = [record for record in dataset_remote.records]\n", + "\n", + "for record in modified_records:\n", + " random_record = random.choice(groups)\n", + " record.metadata[\"group\"] = random_record\n", + " groups.remove(random_record)\n", + "\n", + "dataset_remote.update_records(modified_records)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### IntegerMetadataProperty\n", + "The second type of metadata property that Argilla offers is the `IntegerMetadataProperty`, which can be used to filter the metadata of a record based on a range of integer values. While the `name` and `title` are the same as the `TermsMetadataProperty`, we do not have a `values` parameter. Instead, we have `min` and `max` parameters that define the range of the integer values. Let us create an `IntegerMetadataProperty` and add it to our dataset.\n", + "\n", + "A metadata property that consists of the longest and shortest data items in our list would be useful for us. To add this, let us first find out what the longest and shortest data items are." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "item_list = [items.fields[\"text\"] for items in dataset]\n", + "max_length_string = len(max(item_list, key=len))\n", + "min_length_string = len(min(item_list, key=len))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now create our metadata property for item length with the shortest and longest data item lengths and add it to our dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "IntegerMetadataProperty(name='length', title='Length of the text', visible_for_annotators=True, type='integer', min=100, max=862)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "integer_metadata_property = rg.IntegerMetadataProperty(\n", + " name=\"length\",\n", + " title=\"Length of the text\",\n", + " min=min_length_string,\n", + " max=max_length_string,\n", + ")\n", + "\n", + "dataset.add_metadata_property(integer_metadata_property)\n", + "dataset_remote.add_metadata_property(integer_metadata_property)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us add the metadata to individual records. We will be adding the length of the data item to the metadata of each record. Again, we do it differently for `FeedbackDataset` and `RemoteFeedbackDataset`." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# For local dataset\n", + "for record in dataset.records:\n", + " record.metadata[\"length\"] = len(record.fields[\"text\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# For remote dataset\n", + "modified_records = [record for record in dataset_remote.records]\n", + "\n", + "for record in modified_records:\n", + " record.metadata[\"length\"] = len(record.fields[\"text\"])\n", + "\n", + "dataset_remote.update_records(modified_records)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### FloatMetadataProperty\n", + "\n", + "The third and last type of metadata property that we can employ for our datasets is the `FloatMetadataProperty`. This metadata property can be used to filter the metadata of a record based on a range of float values. The `name` and `title` parameters are the same as the other metadata properties. Similar to the `IntegerMetadataProperty`, we have `min` and `max` parameters that define the range of the float values. \n", + "\n", + "While working with text data, we might want to evaluate the text length and filter our dataset by item lengths. For this purpose, we can make use of the standard deviation of item lengths along with the mean item length. With them, we can keep or work on the items whose lengths are between a certain standard deviation from the mean. We can create a `FloatMetadataProperty` with the min value as the mean minus the standard deviation and the max value as the mean plus the standard deviation. Let us first calculate the mean and standard deviation of the item lengths." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "item_len_list = [len(items.fields[\"text\"]) for items in dataset]\n", + "std = round(statistics.stdev(item_len_list), 3)\n", + "mean = statistics.mean(item_len_list)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we know what the mean and standard deviation of the item lengths are, let us create a `FloatMetadataProperty` and add it to our dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "FloatMetadataProperty(name='length_std', title='Standard deviation of the length of the text', visible_for_annotators=True, type='float', min=139.096, max=361.398)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "float_metadata_property = rg.FloatMetadataProperty(\n", + " name=\"length_std\",\n", + " title=\"Standard deviation of the length of the text\",\n", + " min=mean - std,\n", + " max=mean + std,\n", + ")\n", + "\n", + "dataset.add_metadata_property(float_metadata_property)\n", + "dataset_remote.add_metadata_property(float_metadata_property)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have the float metadata property added to our dataset, let us add the metadata to individual records. We will be again adding the length of the data item. However, this time, we will add it as a float and only to the records whose lengths are between the mean minus the standard deviation and the mean plus the standard deviation. Let us see how we can do it for both `FeedbackDataset` and `RemoteFeedbackDataset`." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# For local dataset\n", + "for record in dataset.records:\n", + " record_len = float(len(record.fields[\"text\"]))\n", + " if record_len <= mean + std and record_len >= mean - std:\n", + " record.metadata[\"length_std\"] = record_len" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# For remote dataset\n", + "modified_records = [record for record in dataset_remote.records]\n", + "\n", + "for record in modified_records:\n", + " record_len = float(len(record.fields[\"text\"]))\n", + " if record_len <= mean + std and record_len >= mean - std:\n", + " record.metadata[\"length_std\"] = record_len\n", + "\n", + "dataset.update_records(modified_records)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The resulting dataset is now a dataset with metadata properties that can be used for filtering and sorting." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "FeedbackDataset(\n", + " fields=[TextField(name='text', title='Text', required=True, type=, use_markdown=False)]\n", + " questions=[LabelQuestion(name='label', title='Label', description=None, required=True, type=, labels=['World', 'Sports', 'Business', 'Sci/Tech'], visible_labels=None)]\n", + " guidelines=Classify the articles into one of the four categories.)\n", + " metadata_properties=[TermsMetadataProperty(name='group', title='Annotation Group', visible_for_annotators=True, type='terms', values=['group-1', 'group-2', 'group-3']), IntegerMetadataProperty(name='length', title='Length of the text', visible_for_annotators=True, type='integer', min=100, max=862), FloatMetadataProperty(name='length_std', title='Standard deviation of the length of the text', visible_for_annotators=True, type='float', min=139.096, max=361.398)])\n", + ")" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    +       " in <module>:1                                                                                    \n",
    +       "                                                                                                  \n",
    +       " 1 dataset_remote                                                                               \n",
    +       "   2                                                                                              \n",
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Below, you can see an example, where we filtered by our metadata `Annotation Group`, `Length of the text` and `Standard Deviation` properties, so that from the 1000 records, we only got 242. Note that if they were set to `visible_for_annotators=False`, it would only appear for users with the `admin` or `owner` role.\n", + "\n", + "> This can also be very useful to assign records to your team members in a common workspace. Please refer to how to [assign records](./assign-records-002.ipynb) for more information." + ] + }, + { + "attachments": { + "filter-by-metadata.PNG": { + "image/png": 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+ } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![filter-by-metadata.PNG](attachment:filter-by-metadata.PNG)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we will make the same filtering but using the `filter_by` method provided in the Python SDK. Thus, we will will need to combine the three filters. In addition, each metadata is a different type, so we will need to use `TermsMetadataFilter` for the annotation group, `IntegerMetadataFilter` for the length of the text, and `FloatMetadataFilter` for the standard deviation. We will be using the following parameters:\n", + "\n", + "| | Description | TermsMetadataFilter | IntegerMetadataFilter | FloatMetadataFilter |\n", + "|-------|-------|-------|-------|-------|\n", + "| *name* | name of the metadata property | group | length | length_std |\n", + "| *ge* | values greater than or equal | no-required | 0 | 204 |\n", + "| *le* | values lower than or equal | no-required | 282 | 290 |\n", + "| *values* | values searched | group-1 and group-2 | no-required | no-required |" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> In the case of Integer and Float filters at least one of `ge` or `le` should be provided." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "242\n" + ] + } + ], + "source": [ + "filtered_records = dataset_remote.filter_by(\n", + " metadata_filters=[\n", + " rg.TermsMetadataFilter(\n", + " name=\"group\",\n", + " values=[\"group-1\", \"group-2\"]\n", + " ),\n", + " rg.IntegerMetadataFilter(\n", + " name=\"length\",\n", + " le=282\n", + " ),\n", + " rg.FloatMetadataFilter(\n", + " name=\"length_std\",\n", + " ge=204, \n", + " le=290\n", + " ), \n", + " ]\n", + ")\n", + "\n", + "print(len(filtered_records))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we can also sort them by the metadata properties and when they were updated." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from argilla import SortBy\n", + "\n", + "filtered_dataset = dataset_remote.filter_by(\n", + " metadata_filters=[\n", + " rg.TermsMetadataFilter(\n", + " name=\"group\",\n", + " values=[\"group-1\", \"group-2\"]\n", + " ),\n", + " rg.IntegerMetadataFilter(\n", + " name=\"length\",\n", + " le=282\n", + " ),\n", + " rg.FloatMetadataFilter(\n", + " name=\"length_std\",\n", + " ge=204, \n", + " le=290\n", + " ),\n", + " ],\n", + " response_status=[\"discarded\"]\n", + ").sort_by(\n", + " [\n", + " SortBy(field=\"updated_at\", order=\"desc\"),\n", + " SortBy(field=\"metadata.group\", order=\"asc\")\n", + " ]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Push the Dataset\n", + "\n", + "We have now enhanced our dataset in terms of metadata properties and we can push it again. Note that you do not have to upload the improved version of the dataset to Argilla again. As you can see on your Argilla UI, the dataset already has the metadata properties we added.\n", + "\n", + "### To Argilla\n", + "\n", + "If you have opted to pull the dataset from HuggingFace and want to push to Argilla, you can simply use the `push_to_argilla` method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "try:\n", + " remote_dataset = rg.FeedbackDataset.from_argilla(\"end2end_textclassification_with_metadata\")\n", + " remote_dataset.delete()\n", + "except:\n", + " pass\n", + "remote_dataset = dataset.push_to_argilla(\"end2end_textclassification_with_metadata\")\n", + "remote_dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can see that the dataset on Argilla has the metadata properties we added." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Screenshot of Argilla UI](../../../../_static/tutorials/end2end/text-classification/argilla-ui-page-metadata.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### To HuggingFace Hub\n", + "\n", + "If you have opted to pull the dataset from HuggingFace Hub, you can push the dataset to HuggingFace Hub by using the `push_to_huggingface` method. Do not forget to create a model card as well, which will make the dataset more readable and understandable for the users.\n", + "\n", + "To be able to upload your dataset to the Hub, you must be logged in to the Hub. The following cell will log us with our previous token.\n", + "\n", + "If we don't have one already, we can obtain it from [here](https://huggingface.co/docs/hub/security-tokens) (remember to set the write access)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from huggingface_hub import login\n", + "\n", + "login(token=hf_token)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "We only need to call the `push_to_huggingface` method to push the dataset to HuggingFace Hub. If we have a dataset with the same name on the Hub, this method will update the existing one." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "#papermill_description=push-dataset-to-huggingface \n", + "dataset.push_to_huggingface(\"argilla/end2end_textclassification_with_metadata\", generate_card=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "In this tutorial, we have seen how to use metadata properties to enhance our dataset. We have first seen how we can pull the dataset from either Argilla or HuggingFace Hub. Then, we have seen how to add `TermsMetadataProperty`, `IntegerMetadataProperty` and `FloatMetadataProperty` to our dataset, and use them to filter and sort. We have also seen how `RemoteFeedbackDataset` works and how to push the dataset to HuggingFace Hub. To add vector representations of records in order for semantic search, you can checkout the [next tutorial](./add-vectors-004.ipynb). For more detailed info on how to utilize various tools, please refer to our [practical guides](../../../../practical_guides/practical_guides.md)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/_source/tutorials_and_integrations/tutorials/feedback/end2end_examples/add-suggestions-and-responses-005.ipynb b/docs/_source/tutorials_and_integrations/tutorials/feedback/end2end_examples/add-suggestions-and-responses-005.ipynb new file mode 100644 index 0000000000..ba7e58f9cf --- /dev/null +++ b/docs/_source/tutorials_and_integrations/tutorials/feedback/end2end_examples/add-suggestions-and-responses-005.ipynb @@ -0,0 +1,582 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Add `Responses` and `Suggestions` to `FeedbackDataset` \n", + "\n", + "In this part of the end-to-end tutorial series, we will see how we can update the records of our dataset with the responses and suggestions. You can refer to previous tutorials for [creating the dataset](./create-dataset-001.ipynb), [configuring the users and workspaces](./configure-users-and-workspaces-000.ipynb) or [adding metadata](./add-metadata-003.ipynb). Feel free to check out the [practical guides](../../../../practical_guides/practical_guides.md) page for more in-depth information.\n", + "\n", + "In Argilla, `responses` are the answers that the annotators give to the questions that we ask them. If we have a dataset that has been annotated already, we can add these gold responses to our dataset as responses. This comes in handy in that we will not have to annotate the dataset again. On the other hand, `suggestions` are the model predictions that we show to our annotators in the UI during the annotation process. This way, the annotation process will become much faster and easier for the annotators. \n", + "\n", + "![workflow](../../../../_static/tutorials/end2end/base/workflow_suggestions_and_responses.svg)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Table of Contents\n", + "\n", + "1. [Pull the Dataset](#Pull-the-Dataset)\n", + " 1. [From Argilla](#From-Argilla)\n", + " 2. [From HuggingFace Hub](#From-HuggingFace-Hub)\n", + " 3. [Pull the Original Dataset](#Pull-the-Original-Dataset)\n", + "2. [Add Responses](#Add-Responses)\n", + "3. [Add Suggestions](#Add-Suggestions)\n", + "4. [Push the Dataset](#Push-the-Dataset)\n", + " 1. [To Argilla](#To-Argilla)\n", + " 2. [To HuggingFace Hub](#To-HuggingFace-Hub)\n", + "5. [Conclusion](#Conclusion)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Running Argilla\n", + "\n", + "For this tutorial, you will need to have an Argilla server running. There are two main options for deploying and running Argilla:\n", + "\n", + "**Deploy Argilla on Hugging Face Spaces:** If you want to run tutorials with external notebooks (e.g., Google Colab) and you have an account on Hugging Face, you can deploy Argilla on Spaces with a few clicks:\n", + "\n", + "[![deploy on spaces](https://huggingface.co/datasets/huggingface/badges/raw/main/deploy-to-spaces-lg.svg)](https://huggingface.co/new-space?template=argilla/argilla-template-space)\n", + "\n", + "For details about configuring your deployment, check the [official Hugging Face Hub guide](https://huggingface.co/docs/hub/spaces-sdks-docker-argilla).\n", + "\n", + "**Launch Argilla using Argilla's quickstart Docker image**: This is the recommended option if you want [Argilla running on your local machine](../../../../getting_started/quickstart.md). Note that this option will only let you run the tutorial locally and not with an external notebook service.\n", + "\n", + "For more information on deployment options, please check the Deployment section of the documentation.\n", + "\n", + "
    \n", + "\n", + "Tip\n", + "\n", + "This tutorial is a Jupyter Notebook. There are two options to run it:\n", + "\n", + "- Use the Open in Colab button at the top of this page. This option allows you to run the notebook directly on Google Colab. Don't forget to change the runtime type to GPU for faster model training and inference.\n", + "- Download the .ipynb file by clicking on the View source link at the top of the page. This option allows you to download the notebook and run it on your local machine or on a Jupyter notebook tool of your choice.\n", + "
    " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, let's install our dependencies and import the necessary libraries:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install argilla\n", + "!pip install datasets transformers" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import argilla as rg\n", + "from datasets import load_dataset\n", + "from transformers import pipeline\n", + "from datetime import datetime\n", + "from argilla._constants import DEFAULT_API_KEY" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to run this notebook we will need some credentials to push and load datasets from `Argilla` and `🤗 Hub`, let's set them in the following cell:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "parameters" + ] + }, + "outputs": [], + "source": [ + "# Argilla credentials\n", + "api_url = \"http://localhost:6900\" # \"https://.hf.space\"\n", + "api_key = DEFAULT_API_KEY # admin.apikey\n", + "# Huggingface credentials\n", + "hf_token = \"hf_...\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Log in to argilla:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rg.init(api_url=api_url, api_key=api_key)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Enable Telemetry\n", + "\n", + "We gain valuable insights from how you interact with our tutorials. To improve ourselves in offering you the most suitable content, using the following lines of code will help us understand that this tutorial is serving you effectively. Though this is entirely anonymous, you can choose to skip this step if you prefer. For more info, please check out the Telemetry page.\n", + "\n", + "```python\n", + "from argilla.utils.telemetry import tutorial_running\n", + "\n", + "tutorial_running()\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pull the Dataset\n", + "\n", + "As we uploaded the dataset that we created in the [previous tutorial](#previous_tutorial) to both Argilla and HuggingFace Hub, we can pull the dataset from either of them. Let us see how we can pull the dataset from both.\n", + "\n", + "### From Argilla\n", + "\n", + "We can pull the dataset from Argilla by using the `from_argilla` method. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/davidberenstein/Documents/programming/argilla/argilla/src/argilla/client/client.py:154: UserWarning: Default user was detected and no workspace configuration was provided, so the default 'argilla' workspace will be used. If you want to setup another workspace, use the `rg.set_workspace` function or provide a different one on `rg.init`\n", + " warnings.warn(\n", + "/Users/davidberenstein/Documents/programming/argilla/argilla/src/argilla/client/client.py:182: UserWarning: You're connecting to Argilla Server 1.19.0 using a different client version (1.21.0-dev).\n", + "This may lead to potential compatibility issues during your experience.\n", + "To ensure a seamless and optimized connection, we highly recommend aligning your client version with the server version.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "dataset_remote = rg.FeedbackDataset.from_argilla(\"end2end_textclassification\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### From HuggingFace Hub\n", + "\n", + "We can also pull the dataset from HuggingFace Hub. Similarly, we can use the `from_huggingface` method to pull the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = rg.FeedbackDataset.from_huggingface(\"argilla/end2end_textclassification\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
    \n", + "\n", + "Note \n", + "\n", + "The dataset pulled from HuggingFace Hub is an instance of `FeedbackDataset` whereas the dataset pulled from Argilla is an instance of `RemoteFeedbackDataset`. The difference between the two is that the former is a local one and the changes made on it stay locally. On the other hand, the latter is a remote one and the changes made on it are directly reflected on the dataset on the Argilla server, which can make your process faster.\n", + "\n", + "
    " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us briefly examine what our dataset looks like. It is a dataset that consists of data items with the field `text` that is yet to be annotated." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'text': \"Wall St. Bears Claw Back Into the Black (Reuters) Reuters - Short-sellers, Wall Street's dwindling\\\\band of ultra-cynics, are seeing green again.\"}" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset[0].fields" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Pull the Original Dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we will be using the gold labels in the original dataset as the responses, we can pull the original dataset from HuggingFace Hub. We can do this by using the `load_dataset` method." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "dataset_org = load_dataset(\"ag_news\", split=\"train[:1000]\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The labels in the original dataset are in the form of integers while we need to present them to the annotators in the form of strings. Therefore, we will create a dictionary that maps the integer labels to their string counterparts." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "id2label = {0: \"World\", 1: \"Sports\", 2: \"Business\", 3: \"Sci/Tech\"}\n", + "mapped_labels = [id2label[label] for label in dataset_org[\"label\"]]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add `Responses`\n", + "\n", + "Now that we have the original dataset and the dataset that we created in the previous tutorial, we can add the responses. The process is slightly different for the local FeedbackDataset and the RemoteFeedbackDataset. Let us see how we can do it for both.\n", + "\n", + "### For the local `FeedbackDataset`\n", + "\n", + "For the local instance of the dataset, we can iterate over the data items and add the responses for each item." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for index, record in enumerate(dataset.records):\n", + " record.responses = [\n", + " {\n", + " \"values\":{\n", + " \"label\":{\n", + " \"value\": mapped_labels[index],\n", + " }\n", + " }\n", + " }\n", + " ]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### For the `RemoteFeedbackDataset`\n", + "\n", + "For the remote instance of the dataset, we can create a list of the records in the dataset and make the changes on the list. Then, we can update the dataset by the `update_records` method." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    \n" + ], + "text/plain": [ + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "modified_records = [record for record in dataset_remote.records]\n", + "\n", + "for index, record in enumerate(modified_records):\n", + " record.responses = [\n", + " {\n", + " \"values\":{\n", + " \"label\":{\n", + " \"value\": mapped_labels[index],\n", + " }\n", + " },\n", + " \"inserted_at\": datetime.now(),\n", + " \"updated_at\": datetime.now(),\n", + " }\n", + " ]\n", + "\n", + "dataset_remote.update_records(modified_records)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add `Suggestions`\n", + "\n", + "As we have mentioned, suggestions are the predictions by any model of your preference to be added as suggested responses to our dataset. In this tutorial, we will be using the `cointegrated/rubert-tiny-bilingual-nli` model from the [HuggingFace Hub](https://huggingface.co/cointegrated/rubert-tiny-bilingual-nli) to obtain our model predictions. To obtain the predictions, we will use the `pipeline` method from the `transformers` library, which makes it easy to use models for inference. Let us first load the model. To give us our zero-shot model, we also create a list of the labels that we want to predict." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "model_name = \"cointegrated/rubert-tiny-bilingual-nli\"\n", + "classifier = pipeline(\"zero-shot-classification\", model=model_name)\n", + "candidate_labels = [\"World\", \"Sports\", \"Business\", \"Sci/Tech\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Again, the process is slightly different for the local `FeedbackDataset` and the `RemoteFeedbackDataset`. Let us see how we can do it for both." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### For the local `FeedbackDataset`\n", + "\n", + "For the local instance of the dataset, we can iterate over the data items and add the suggestions for each item by classifying the text with the model at the same time. Additionally, we would like to add the model as the `agent` of the suggestions for future reference." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "for record in dataset.records:\n", + " record.suggestions = [\n", + " {\n", + " \"question_name\": \"label\",\n", + " \"value\": classifier(record.fields[\"text\"], candidate_labels)[\"labels\"][0],\n", + " \"agent\": model_name \n", + " }\n", + " ]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### For the `RemoteFeedbackDataset`\n", + "\n", + "For the remote instance of the dataset, instead of creating a new `modified_records` list, we will use the same list with the responses added above. Again, we will be getting the model predictions from the `pipeline` method as we are iterating over the records. Similarly, we would like to add the model as the `agent` of the suggestions for future reference. We will use the `update_records` method to update the dataset at the end." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "modified_records = [record for record in dataset_remote.records]\n", + "\n", + "for record in modified_records:\n", + " record.suggestions = [\n", + " {\n", + " \"question_name\": \"label\",\n", + " \"value\": classifier(record.fields[\"text\"], candidate_labels)[\"labels\"][0],\n", + " \"agent\": model_name \n", + " }\n", + " ]\n", + "\n", + "dataset_remote.update_records(modified_records)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that we have split the process of adding responses and suggestions into two for demonstration purposes here. However, you can do both at the same time by iterating over the records only once. In that case, you will need to call the `update_records` method only once for the remote dataset." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Push the Dataset\n", + "\n", + "Now that we have added the responses and suggestions to our dataset, we can push the dataset to Argilla. We can do this by using the `push_to_argilla` method. Please note that you do not have to push the dataset to Argilla if you are using the `RemoteFeedbackDataset` as the changes are directly reflected on the dataset on the Argilla server.\n", + "\n", + "### To Argilla\n", + "\n", + "If you have opted to pull the dataset from HuggingFace and want to push to Argilla, you can simply use the `push_to_argilla` method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " remote_dataset = rg.FeedbackDataset.from_argilla(\"end2end_textclassification_with_suggestions_and_responses\")\n", + " remote_dataset.delete()\n", + "except Exception:\n", + " pass\n", + "remote_dataset = dataset.push_to_argilla(\"end2end_textclassification_with_suggestions_and_responses\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us go to Argilla and look at the dataset we created. When you first open the dataset, you should the message \"You have no pending records\" as we have uploaded all the responses and there is no pending record left. As seen below, if you go to the `Submitted` tab, you can see that all the records are submitted with the exact labels we have uploaded above. \n", + "\n", + "![ui-responses](../../../../_static/tutorials/end2end/text-classification/argilla-annotation-ui-responses.png)\n", + "\n", + "As all the records are submitted, we no longer see the suggestion for each one of the records. To demonstrate how they are seen, you can see the Argilla UI below where the record is not submitted yet and the suggestion (`Sports` in this case) is shown to the annotator.\n", + "\n", + "![ui-suggestions](../../../../_static/tutorials/end2end/text-classification/argilla-annotation-ui-suggestions.png)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### To HuggingFace Hub\n", + "\n", + "If you would like to push the dataset you created to the HuggingFace Hub, you can simply use the `push_to_huggingface` method to upload it. Do not forget to create a model card as well, which will make the dataset more readable and understandable for the users.\n", + "\n", + "To be able to upload your dataset to the Hub, you must be logged in to the Hub. The following cell will log us with our previous token.\n", + "\n", + "If we don't have one already, we can obtain it from [here](https://huggingface.co/docs/hub/security-tokens) (remember to set the write access)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from huggingface_hub import login\n", + "\n", + "login(token=hf_token)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We only need to call the `push_to_huggingface` method to push the dataset to HuggingFace Hub. If we have a dataset with the same name on the Hub, this method will update the existing one." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#papermill_description=push-dataset-to-huggingface\n", + "dataset.push_to_huggingface(\"argilla/end2end_textclassification_with_suggestions_and_responses\", generate_card=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "In this tutorial, we have seen how we can add responses and suggestions to our dataset. Adding responses to your dataset given that you already have the annotated labels is a great way to save time and effort for your project. Similarly, adding suggestions to your dataset will make the annotation process much faster and easier for your annotators. We have seen how we can employ these tools for both `FeedbackDataset` and `RemoteFeedbackDataset`. Now, with the dataset we obtained, we can move on to [training our model](./train-model-006.ipynb) and [computing the metrics](./use-metrics-007.ipynb). For more detailed info on how to utilize various tools, please refer to our [practical guides](../../../../practical_guides/practical_guides.md)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "argilla", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/_source/tutorials_and_integrations/tutorials/feedback/end2end_examples/add-vectors-004.ipynb b/docs/_source/tutorials_and_integrations/tutorials/feedback/end2end_examples/add-vectors-004.ipynb new file mode 100644 index 0000000000..1286f34d25 --- /dev/null +++ b/docs/_source/tutorials_and_integrations/tutorials/feedback/end2end_examples/add-vectors-004.ipynb @@ -0,0 +1,622 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Add Vectors to `FeedbackDataset`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This tutorial is part of a series in which we will get to know the `FeedbackDataset`. In this step, we will show how to set the vector settings and add the vectors, which allow the similarity search in the UI and PythonSDK. You can also take a look at our previous tutorials for [configuring the users and workspaces](./configure-users-and-workspaces-000.ipynb) and [creating a dataset](./create-dataset-001.ipynb) for a comprehensive overview of the complete workflow. Feel free to check out the [practical guides](../../../../practical_guides/practical_guides.md) page for more in-depth information and the [semantic search](../../../../practical_guides/annotation_workflows/semantic_search.md) page for more information about the similarity search.\n", + "\n", + "Vectors are the embeddings of the text pieces created by a language model. By means of embeddings, we can mathematically represent the text pieces in a vector space. As a result of this representation, we can calculate how similar or different two or more text pieces are. Argilla offers a great opportunity to use this similarity search in the UI and PythonSDK. If you want to see a comprehensive overview of embeddings, you can refer to [this page](https://www.turing.com/kb/guide-on-word-embeddings-in-nlp)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![workflow](../../../../_static/tutorials/end2end/base/workflow_vectors.svg)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Table of Contents\n", + "\n", + "1. [Pull the Dataset](#Pull-the-Dataset)\n", + " 1. [From Argilla](#From-Argilla)\n", + " 2. [From HuggingFace Hub](#From-HuggingFace-Hub)\n", + "2. [Add Vector Settings](#Add-Vector-Settings)\n", + "3. [Add Vectors](#Add-Vectors)\n", + " 1. [To local FeedbackDataset](#To-local-FeedbackDataset)\n", + " 2. [To RemoteFeedbackDataset](#To-RemoteFeedbackDataset)\n", + "4. [Find Similar Records](#Find-Similar-Records)\n", + "5. [Push the Dataset](#Push-the-Dataset)\n", + " 1. [To Argilla](#To-Argilla)\n", + " 2. [To HuggingFace Hub](#To-HuggingFace-Hub)\n", + "6. [Conclusion](#Conclusion)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Running Argilla\n", + "\n", + "For this tutorial, you will need to have an Argilla server running. There are two main options for deploying and running Argilla:\n", + "\n", + "**Deploy Argilla on Hugging Face Spaces:** If you want to run tutorials with external notebooks (e.g., Google Colab) and you have an account on Hugging Face, you can deploy Argilla on Spaces with a few clicks:\n", + "\n", + "[![deploy on spaces](https://huggingface.co/datasets/huggingface/badges/raw/main/deploy-to-spaces-lg.svg)](https://huggingface.co/new-space?template=argilla/argilla-template-space)\n", + "\n", + "For details about configuring your deployment, check the [official Hugging Face Hub guide](https://huggingface.co/docs/hub/spaces-sdks-docker-argilla).\n", + "\n", + "**Launch Argilla using Argilla's quickstart Docker image**: This is the recommended option if you want [Argilla running on your local machine](../../../../getting_started/quickstart.md). Note that this option will only let you run the tutorial locally and not with an external notebook service.\n", + "\n", + "For more information on deployment options, please check the Deployment section of the documentation.\n", + "\n", + "
    \n", + "\n", + "Tip\n", + "\n", + "This tutorial is a Jupyter Notebook. There are two options to run it:\n", + "\n", + "- Use the Open in Colab button at the top of this page. This option allows you to run the notebook directly on Google Colab. Don't forget to change the runtime type to GPU for faster model training and inference.\n", + "- Download the .ipynb file by clicking on the View source link at the top of the page. This option allows you to download the notebook and run it on your local machine or on a Jupyter notebook tool of your choice.\n", + "
    " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, let's install our dependencies and import the necessary libraries:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install argilla\n", + "!pip install datasets" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import argilla as rg\n", + "from argilla._constants import DEFAULT_API_KEY\n", + "from sentence_transformers import SentenceTransformer" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to run this notebook we will need some credentials to push and load datasets from `Argilla` and 🤗`hub`, let's set them in the following cell:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "tags": [ + "parameters" + ] + }, + "outputs": [], + "source": [ + "# Argilla credentials\n", + "api_url = \"http://localhost:6900\" # \"https://.hf.space\"\n", + "api_key = DEFAULT_API_KEY # admin.apikey\n", + "# Huggingface credentials\n", + "hf_token = \"hf_...\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Log to argilla:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rg.init(\n", + " api_url=api_url,\n", + " api_key=api_key\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Enable Telemetry" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We gain valuable insights from how you interact with our tutorials. To improve ourselves in offering you the most suitable content, using the following lines of code will help us understand that this tutorial is serving you effectively. Though this is entirely anonymous, you can choose to skip this step if you prefer. For more info, please check out the [Telemetry](../../reference/telemetry.md) page." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from argilla.utils.telemetry import tutorial_running\n", + "\n", + "tutorial_running()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pull the Dataset\n", + "\n", + "As we uploaded the dataset we created in the [previous tutorial](./create-dataset-001.ipynb) to both Argilla and HuggingFace Hub, we can pull the dataset from either of them. Let us see how we can pull the dataset from both.\n", + "\n", + "### From Argilla\n", + "\n", + "We can pull the dataset from Argilla by using the `from_argilla` method. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset_remote = rg.FeedbackDataset.from_argilla(\"end2end_textclassification\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### From HuggingFace Hub\n", + "\n", + "We can also pull the dataset from HuggingFace Hub. Similarly, we can use the `from_huggingface` method to pull the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = rg.FeedbackDataset.from_huggingface(\"argilla/end2end_textclassification\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
    \n", + "\n", + "Note \n", + "\n", + "The dataset pulled from HuggingFace Hub is an instance of `FeedbackDataset` whereas the dataset pulled from Argilla is an instance of `RemoteFeedbackDataset`. The difference between the two is that the former is a local one and the changes made on it stay locally. On the other hand, the latter is a remote one and the changes made on it are directly reflected on the dataset on the Argilla server, which can make your process faster.\n", + "\n", + "
    " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us briefly examine what our dataset looks like. It is a dataset that consists of data items with the field `text` that is yet to be annotated." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'text': \"Wall St. Bears Claw Back Into the Black (Reuters) Reuters - Short-sellers, Wall Street's dwindling\\\\band of ultra-cynics, are seeing green again.\"}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset[0].fields" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add Vector Settings\n", + "\n", + "As the first step of adding vectors, we need to add the vector settings to the dataset. This way, we will be able to access a particular vector setting both from the UI and PythonSDK. Whether you work with a local dataset or a remote dataset, the process is the same. Let us first create the vector setting." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "vector_settings = rg.VectorSettings(\n", + " name=\"sentence_embedding\",\n", + " title=\"Sentence Embedding\",\n", + " dimensions=384\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In vector settings, `name` will be the name shown in the records and we will access each vector setting by its name. `title` will be the name shown in the UI. And `dimension` will define how many dimensions each vector will have. This number must be equal to the number of dimensions of the vectors, otherwise, an error will be raised. We set it to 384 as the model we will use in later steps has 384 dimensions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset.add_vector_settings(vector_settings)\n", + "dataset_remote.add_vector_settings(vector_settings)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add Vectors\n", + "\n", + "Now that we make sure that our dataset has the vector settings, we can move on to adding the vectors to each record. First, we will need a tool to create the embeddings of the text items so that we can have the vectors. In this tutorial, we will use the [paraphrase-MiniLM-L3-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L3-v2) model to create the embeddings. You can use any model of your choice. Please make sure that the dimensionality of the model you use is the same as the dimensionality of the vector setting you created.\n", + "\n", + "Let us first load the model." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "model = SentenceTransformer(\"TaylorAI/bge-micro\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And test the encoded vectors on a sample text to make sure that the dimensionality is correct." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "384" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sentence = \"Argentina and Uruguay are South American countries.\"\n", + "len(model.encode(sentence))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As our model is ready to create the embeddings, we can now create vectors for each record in the dataset. The process of adding vectors is just slightly different for local `FeedbackDataset` and `RemoteFeedbackDataset`. We will see how to add vectors to both.\n", + "\n", + "### To local `FeedbackDataset`\n", + "\n", + "To add vectors to a local `FeedbackDataset`, we will iterate over the records we have in the dataset. For each record, we encode it with the sentence transformers model and we assign the encoded vector to the `vector` field of the record. " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "texts = [record.fields[\"text\"] for record in dataset.records]\n", + "embeddings = model.encode(texts)\n", + "\n", + "for record, embedding in zip(dataset.records, embeddings):\n", + " record.vectors[\"sentence_embedding\"] = embedding.tolist()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Please note that the vector you will add should be a list of float values. If you have a numpy array, you can convert it to a list by using the `tolist()` method, as we did in the previous cell.\n", + "\n", + "### To `RemoteFeedbackDataset`\n", + "\n", + "To add vectors to a `RemoteFeedbackDataset`, we first need to create a list of the records we have in the dataset. We will make the changes on this list similar to what we did for the local dataset. Then, with the `update_records` method, we will update the records in the dataset on the Argilla server." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "modified_records = [record for record in dataset_remote.records]\n", + "texts = [record.fields[\"text\"] for record in modified_records]\n", + "embeddings = model.encode(texts)\n", + "\n", + "for record, embedding in zip(modified_records, embeddings):\n", + " record.vectors[\"sentence_embedding\"] = embedding.tolist()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And, let us update the records." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset.update_records(modified_records)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Find Similar Records\n", + "\n", + "Now that we have the vectors for each record, let us see the semantic search in action. We can accomplish this via both the UI and PythonSDK. To find similar records in the UI, please refer to the [push the dataset](#Push-the-Dataset) section, where we upload the improved dataset to Argilla server and easily do semantic search. In this section, we will see how to do it within the PythonSDK.\n", + "\n", + "To find similar records, we will use the `find_similar_records` method. We need to feed this method with the vector name we want to use for the search. For the particular vector, we have two options: we can either use a record by the `record` parameter or we can define a new vector by the `vector` parameter. You can limit the number of records you want to get by the `max_results` parameter. " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "similar_records = dataset.find_similar_records(\n", + " vector_name=\"sentence_embedding\",\n", + " record=dataset[0],\n", + " max_results=5\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `similar_records` object we created contains the 5 top records similar to the first record in the dataset. Let us see what these records are." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[\"Wall St. Bears Claw Back Into the Black NEW YORK (Reuters) - Short-sellers, Wall Street's dwindling band of ultra-cynics, are seeing green again.\",\n", + " \"Strategies for a Sideways Market (Reuters) Reuters - The bulls and the bears are in this\\\\together, scratching their heads and wondering what's going to\\\\happen next.\",\n", + " \"Strategies for a Sideways Market WASHINGTON (Reuters) - The bulls and the bears are in this together, scratching their heads and wondering what's going to happen next.\",\n", + " 'Wall Street to Open Little Changed NEW YORK (Reuters) - Wall Street is seen opening little changed on Monday as crude prices remain high, but insurers may dip on worries about their potential liabilities after a hurricane struck Florida on Friday.',\n", + " \"Chrysler's Bling King After a tough year, Detroit's troubled carmaker is back -- thanks to a maverick designer and a car that is dazzling the hip-hop crowd\"]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[record[0].fields[\"text\"] for record in similar_records]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On the other hand, we can feed the `find_similar_records` method with a vector we define. Let us first create an embedding with the sentence transformers model and then find the records similar to this vector." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "text = \"Baseball: Smoltz sizzles as Braves force deciding game HOUSTON, United States : JD Drew singled to drive in a run in the top of the ninth inning and lift the Atlanta Braves to a 6-5 win over Houston and force the Astros into a fifth and deciding game.\"\n", + "\n", + "text_vector = model.encode(text).tolist()\n", + "\n", + "similar_records = dataset_remote.find_similar_records(\n", + " vector_name=\"sentence_embedding\",\n", + " value=text_vector,\n", + " max_results=5\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see the ids of the records that are similar to the vector we defined." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['record-746', 'record-459', 'record-642', 'record-716', 'record-643']" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[record[0].external_id for record in similar_records]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Push the Dataset\n", + "\n", + "The dataset now has the vector settings and each record is assigned a vector. We can do semantic search in the UI by pushing the dataset to the Argilla server. We can also push the dataset to HuggingFace Hub to use it in other projects. Let us see how we can push the dataset to both. Note that you do not need to push the dataset if you have opted for pulling the dataset from Argilla server. Since it is a `RemoteFeedbackDataset`, the changes you make on it are directly reflected on the dataset on the Argilla server.\n", + "\n", + "### To Argilla\n", + "\n", + "If you have opted to pull the dataset from HuggingFace and want to push to Argilla, you can simply use the `push_to_argilla` method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " remote_dataset = rg.FeedbackDataset.from_argilla(\"end2end_textclassification_with_vectors\")\n", + " remote_dataset.delete()\n", + "except:\n", + " pass\n", + "remote_dataset = dataset.push_to_argilla(\"end2end_textclassification_with_vectors\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![workflow](../../../../_static/tutorials/end2end/text-classification/argilla-annotation-ui-vectors.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, it is possible to do semantic search in the UI. As you can see above, you need to click the `Find Similar` button on the right top of the record to see similar records. You have the opportunity to limit the number of results as well as define your research as the most or least similar records. You can also see the similarity score of each record to the record you are searching for." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### To HuggingFace Hub\n", + "\n", + "You can push the dataset to HuggingFace Hub by using the `push_to_huggingface` method. Do not forget to create a model card as well, which will make the dataset more readable and understandable for the users.\n", + "\n", + "To be able to upload your dataset to the Hub, you must be logged in to the Hub. The following cell will log us with our previous token.\n", + "\n", + "If we don't have one already, we can obtain it from [here](https://huggingface.co/docs/hub/security-tokens) (remember to set the write access)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from huggingface_hub import login\n", + "\n", + "login(token=hf_token)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We only need to call the `push_to_huggingface` method to push the dataset to HuggingFace Hub. If we have a dataset with the same name on the Hub, this method will update the existing one." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#papermill_description=push-dataset-to-huggingface\n", + "dataset.push_to_huggingface(\"argilla/end2end_textclassification_with_vectors\", generate_card=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "In this tutorial, we have seen how to add vectors to the dataset and do semantic search in the UI and PythonSDK. Vectors are a great way to restrict your dataset based on the similarity among the records. We have also seen how to push the dataset to Argilla and HuggingFace Hub. In the next tutorial, we will see how to [add suggestions and responses](./add-suggestions-and-responses-005.ipynb) to our dataset." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "argilla-markdown", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/_source/tutorials_and_integrations/tutorials/feedback/end2end_examples/assign-records-002.ipynb b/docs/_source/tutorials_and_integrations/tutorials/feedback/end2end_examples/assign-records-002.ipynb new file mode 100644 index 0000000000..519f72ed5d --- /dev/null +++ b/docs/_source/tutorials_and_integrations/tutorials/feedback/end2end_examples/assign-records-002.ipynb @@ -0,0 +1,700 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Assign records to your team\n", + "\n", + "This tutorial is part of a series in which we will get to know the `FeedbackDataset`. In this step, we will show the complete workflow to assign the records of our TextClassification dataset to a team. You can have a look at the previous tutorials for [creating the dataset](./create-dataset-001.ipynb) and [configuring the users and workspaces](./configure-users-and-workspaces-000.ipynb). Feel free to check out the [practical guides](../../../../practical_guides/practical_guides.md) page for more in-depth information." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![workflow](../../../../_static/tutorials/end2end/base/workflow_assign_records.svg)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Table of contents\n", + "\n", + "1. [Load a FeedbackDataset](#Pull-the-Dataset)\n", + " 1. [From Argilla](#From-Argilla) \n", + " 2. [From the HuggingFace Hub](#From-HuggingFace-Hub)\n", + "2. [Full Overlap](#Full-Overlap)\n", + "3. [Controlled Overlap](#Controlled-Overlap)\n", + " 1. [Working with Groups](#Working-with-Groups)\n", + " 2. [Working with a List of Users](#Working-with-a-List-of-Users)\n", + "4. [Conclusions](#conclusions)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Running Argilla\n", + "\n", + "For this tutorial, you will need to have an Argilla server running. There are two main options for deploying and running Argilla:\n", + "\n", + "**Deploy Argilla on Hugging Face Spaces:** If you want to run tutorials with external notebooks (e.g., Google Colab) and you have an account on Hugging Face, you can deploy Argilla on Spaces with a few clicks:\n", + "\n", + "[![deploy on spaces](https://huggingface.co/datasets/huggingface/badges/raw/main/deploy-to-spaces-lg.svg)](https://huggingface.co/new-space?template=argilla/argilla-template-space)\n", + "\n", + "For details about configuring your deployment, check the [official Hugging Face Hub guide](https://huggingface.co/docs/hub/spaces-sdks-docker-argilla).\n", + "\n", + "**Launch Argilla using Argilla's quickstart Docker image**: This is the recommended option if you want [Argilla running on your local machine](../../../../getting_started/quickstart.md). Note that this option will only let you run the tutorial locally and not with an external notebook service.\n", + "\n", + "For more information on deployment options, please check the Deployment section of the documentation.\n", + "\n", + "
    \n", + "\n", + "Tip\n", + "\n", + "This tutorial is a Jupyter Notebook. There are two options to run it:\n", + "\n", + "- Use the Open in Colab button at the top of this page. This option allows you to run the notebook directly on Google Colab. Don't forget to change the runtime type to GPU for faster model training and inference.\n", + "- Download the .ipynb file by clicking on the View source link at the top of the page. This option allows you to download the notebook and run it on your local machine or on a Jupyter notebook tool of your choice.\n", + "
    " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, let's install our dependencies and import the necessary libraries:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install argilla\n", + "!pip install datasets" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import argilla as rg\n", + "from argilla._constants import DEFAULT_API_KEY" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to run this notebook we will need some credentials to push and load datasets from `Argilla` and `🤗 Hub`, let's set them in the following cell:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ca4fd7ae-7e31-405e-84c1-974828a903bd", + "metadata": { + "editable": true, + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "completed" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "parameters" + ] + }, + "outputs": [], + "source": [ + "# Argilla credentials\n", + "api_url = \"http://localhost:6900\" # \"https://.hf.space\"\n", + "api_key = DEFAULT_API_KEY # admin.apikey\n", + "# Huggingface credentials\n", + "hf_token = \"hf_...\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Log in to argilla:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rg.init(api_url=api_url, api_key=api_key)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Enable Telemetry\n", + "We gain valuable insights from how you interact with our tutorials. To improve ourselves in offering you the most suitable content, using the following lines of code will help us understand that this tutorial is serving you effectively. Though this is entirely anonymous, you can choose to skip this step if you prefer. For more info, please check out the Telemetry page.\n", + "\n", + "```python\n", + "from argilla.utils.telemetry import tutorial_running\n", + "\n", + "tutorial_running()\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pull the Dataset\n", + "\n", + "As we uploaded the dataset we created in the [previous tutorial](#previous_tutorial) to both Argilla and HuggingFace Hub, we can pull the dataset from either of them. Let us see how we can pull the dataset from both.\n", + "\n", + "### From Argilla\n", + "\n", + "We can pull the dataset from Argilla by using the `from_argilla` method. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = rg.FeedbackDataset.from_argilla(\"end2end_textclassification\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### From HuggingFace Hub\n", + "\n", + "We can also pull the dataset from HuggingFace Hub. Similarly, we can use the `from_huggingface` method to pull the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = rg.FeedbackDataset.from_huggingface(\"argilla/end2end_textclassification\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
    \n", + "\n", + "Note \n", + "\n", + "The dataset pulled from HuggingFace Hub is an instance of `FeedbackDataset` whereas the dataset pulled from Argilla is an instance of `RemoteFeedbackDataset`. The difference between the two is that the former is a local one and the changes made on it stay locally. On the other hand, the latter is a remote one and the changes made on it are directly reflected on the dataset on the Argilla server, which can make your process faster.\n", + "\n", + "
    " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us briefly examine what our dataset looks like. It is a dataset that consists of data items with the field `text` that is yet to be annotated." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset[0].fields" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Full Overlap" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Feedback dataset supports multiple annotation for each record, allowing for straightforward aggregation and utilization. Simply ensure that your dataset is available in a shared workspace accessible to all team members. For detailed guidance on setting this up, refer to our comprehensive example on [configuring users and workspaces](./configure-users-and-workspaces-000.ipynb)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Controlled Overlap" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you aim to specify a fixed number of annotations per record, it's advisable to implement a controlled overlap. To facilitate this, we offer a variety of options and workflows tailored to meet your needs." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Working with Groups\n", + "\n", + "This section will focus on describe all the steps to assign records to predefined groups of annotators." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1. Get the users and records" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, we will set our groups and the records. In this example, we'll be considering a scenario where we are collaborating with two teams, each consisting of five members and a thousand records that need to be annotated. This will be the first time we work with this teams, so we will only be providing their names.\n", + "\n", + "> Note: The names of your members should be written in lower case." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set our teams\n", + "groups = {\n", + " \"group_a\": [\"emily\", \"carlos\", \"aisha\", \"dimitry\", \"chloe\"],\n", + " \"group_b\": [\"liam\", \"hina\", \"samuel\", \"maya\", \"luca\"]\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Get the records\n", + "records = [record for record in dataset]\n", + "print('Number of records:', len(records))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2. Assign the records" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will use the `assign_records` function to assign the annotations. We will specify the following parameters:\n", + "\n", + " - `users`: This will be our dictionary with the groups.\n", + " - `records`: The list of 1000 records.\n", + " - `overlap`: We will set the overlap value to 1 (also known as zero overlap), indicating that the teams will annotate the samples independently, without any overlap. Since each group comprises five members, ultimately, each record will receive 5 annotations, as all members from the same team will be annotating the same records.\n", + " - `shuffle`: To avoid any kind of bias, we will set it to `True`, so the samples are shuffled before the assignment.\n", + "\n", + "As we already mention, we have not yet worked with these annotators, so their users in Argilla will be automatically created.\n", + "\n", + " > Note: The username and first name will be the names provided, the default password is \"12345678\" and they will have the role of \"annotator\"." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from argilla.client.feedback.utils import assign_records\n", + "\n", + "assignments = assign_records(\n", + " users=groups,\n", + " records=records,\n", + " overlap=1,\n", + " shuffle=True\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Thus, the output is structured as a dictionary. In this assignments dictionary, the groups serve as keys, and each key maps to another dictionary with the members and a list of their assigned records." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3. Push the assigned records" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we will need to push the dataset to their corresponding workspaces according to the assignments. For this purpose, we provide the `assign_workspaces` function to do it easily, which uses two arguments:\n", + "\n", + "- `assignments`: This is the assignments dictionary from the previous step.\n", + "- `workspace_type`: In the case of working with groups, we have two options. `group`, which allow us to have a common workspace for each group, where all team members can access the same dataset. And `group_personal`, which allow us to create a personal workspace for each member regardless of the team to which they belong to." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Option with `group`: We assign the workspaces and then we push the dataset with the corresponding records in each workspace." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from argilla.client.feedback.utils import assign_workspaces\n", + "\n", + "wk_group = assign_workspaces(\n", + " assignments=assignments,\n", + " workspace_type=\"group\"\n", + ")\n", + "wk_group" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for group, users in assignments.items():\n", + " dataset = rg.FeedbackDataset.for_text_classification(\n", + " labels=[\"World\", \"Sports\", \"Business\", \"Sci/Tech\"],\n", + " guidelines=\"Classify the articles into one of the four categories.\",\n", + " )\n", + " assigned_records = next(iter(assignments[group].values()))\n", + " dataset.add_records(assigned_records)\n", + " try:\n", + " remote_dataset = dataset.from_argilla(name=\"text_classification\", workspace=group)\n", + " remote_dataset.delete()\n", + " except Exception:\n", + " remote_dataset = dataset.push_to_argilla(name=\"text_classification\", workspace=group)" + ] + }, + { + "attachments": { + "group.PNG": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![group.PNG](attachment:group.PNG)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Option with `group_personal`: We assign the workspaces and then we push the dataset with the corresponding records in each personal workspace." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from argilla.client.feedback.utils import assign_workspaces\n", + "\n", + "wk_group_personal = assign_workspaces(\n", + " assignments=assignments,\n", + " workspace_type=\"group_personal\"\n", + ")\n", + "wk_group_personal" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for group, users in assignments.items():\n", + " for user in users.keys():\n", + " dataset = rg.FeedbackDataset.for_text_classification(\n", + " labels=[\"World\", \"Sports\", \"Business\", \"Sci/Tech\"],\n", + " guidelines=\"Classify the articles into one of the four categories.\",\n", + " )\n", + " assigned_records = users[user]\n", + " dataset.add_records(assigned_records)\n", + " try:\n", + " remote_dataset = dataset.from_argilla(name=\"text_classification\", workspace=user)\n", + " remote_dataset.delete()\n", + " except Exception:\n", + " remote_dataset = dataset.push_to_argilla(name=\"text_classification\", workspace=user)" + ] + }, + { + "attachments": { + "group_personal.PNG": { + "image/png": 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yAACAQgQyAACAQgQyAACAQgQyAACAQgQyAACAQgQyAACAQgQyAACAQgQyAACAQgQyAACAQgQyAACAQgQyAACAQgQyAACAQgQyAACAQgQyAACAQgQyAACAQgQyAACAQgQyAACAQgQyAACAQgQyAACAQgQyAACAQgQyAACAQgQyAACAQgQyAACAQgQyAACAQn43nU6DUkoppZRSSqmHL0fIAAAAChHIAAAAChHIAAAAChHIAAAAChHIAAAAChHIAAAAChHIAAAAChHIAAAAChHIAAAAChHIAAAAChHIAAAAChHIAAAAChHIAAAAChHIAAAAChHIAAAAChHIAAAAChHIAAAAChHIAAAACvndbDYLSimllFJKKaUevgQypZRSSimllCpUAplSSimllFJKFSqBTCmllFJKKaUKlUCmlFJKKaWUUoVKIFNKKaWUUkqpQiWQKaWUUkoppVSRmoX/AWZdjvfU6nE6AAAAAElFTkSuQmCC" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![group_personal.PNG](attachment:group_personal.PNG)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A last option if working with no overlap, would be to use a common workspace, so that the annotators will filter the records using the [metadata](./add-metadata-003.ipynb):\n", + "\n", + "```python\n", + "# Add the metadata to the existing records\n", + "modified_records = []\n", + "for group, users in assignments.items():\n", + " assigned_records = next(iter(assignments[group].values()))\n", + " for record in assigned_records:\n", + " record.metadata[\"groups\"] = group\n", + " modified_records.append(record)\n", + "\n", + "# Push the dataset with the modified records\n", + "dataset = rg.FeedbackDataset.for_text_classification(\n", + " labels=[\"World\", \"Sports\", \"Business\", \"Sci/Tech\"],\n", + " guidelines=\"Classify the articles into one of the four categories.\",\n", + " metadata_properties=[rg.TermsMetadataProperty(name=\"groups\")]\n", + ")\n", + "dataset.add_records(modified_records)\n", + "remote_dataset = dataset.push_to_argilla(name=\"end2end_textclassification_metadata_assignment\", workspace=\"admin\")\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Working with a List of Users\n", + "\n", + "This section will address the workflow to assign records to a list of users." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1. Get the users and records\n", + "\n", + "In this scenario, we will continue working with the same annotators as previously, but this time without forming groups. They will individually annotate a total of one thousand records.\n", + "\n", + "> Note: If you have skipped the previous section, you can simply write a list of users." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Get only those users with the role \"annotator\"\n", + "users = [user for user in rg.User.list() if user.role == \"annotator\"]\n", + "print('Number of users:', len(users))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Get the records\n", + "records = [record for record in dataset]\n", + "print('Number of records:', len(records))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2. Assign the records" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will use the `assign_records` function to assign the annotations. We will specify the following parameters:\n", + "\n", + " - `users`: This will be our list of users.\n", + " - `records`: The list of 1000 records.\n", + " - `overlap`: We will set the overlap value to 2, as we want to have at least 2 annotations per record.\n", + " - `shuffle`: To avoid any kind of bias, we will set it to `True`, so the samples are shuffled before the assignment.\n", + "\n", + " > Note: If a user in Argilla is created, the username and first name will be the names provided, the default password is \"12345678\" and they will have the role of \"annotator\"." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from argilla.client.feedback.utils import assign_records\n", + "\n", + "assignments = assign_records(\n", + " users=users,\n", + " records=records,\n", + " overlap=2,\n", + " shuffle=True\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this case, the output is structured as a dictionary with the user as a key and a list of their assigned records as values. And, each annotator will have 200 records assigned as the overlap was set to 2." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3. Push the assigned records" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we will need to push the dataset to their private workspaces according to the assignments. For this purpose, we will use again the `assign_workspaces` function with the following arguments:\n", + "\n", + "- `assignments`: This is the assignments dictionary from the previous step.\n", + "- `workspace_type`: We will set it to `individual`, so that each user will have the corresponding dataset in its personal workspace." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from argilla.client.feedback.utils import assign_workspaces\n", + "\n", + "wk_individual = assign_workspaces(\n", + " assignments=assignments,\n", + " workspace_type=\"individual\"\n", + ")\n", + "wk_individual" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for username, records in assignments.items():\n", + " dataset = rg.FeedbackDataset.for_text_classification(\n", + " labels=[\"World\", \"Sports\", \"Business\", \"Sci/Tech\"],\n", + " guidelines=\"Classify the articles into one of the four categories.\",\n", + " )\n", + " dataset.add_records(records)\n", + " try:\n", + " remote_dataset = dataset.from_argilla(name=\"text-classification\", workspace=username)\n", + " remote_dataset.delete()\n", + " except Exception: \n", + " remote_dataset = dataset.push_to_argilla(name=\"text-classification\", workspace=username)" + ] + }, + { + "attachments": { + "individual.PNG": { + "image/png": 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+ } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![individual.PNG](attachment:individual.PNG)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
    \n", + "Note\n", + "\n", + "As when we work with groups, it is also possible to use a common workspace if working with no overlap, where each annotator will filter its records using the [metadata](./add-metadata-003.ipynb):\n", + "\n", + "```python\n", + "#Add the metadata to the existing records\n", + "modified_records = []\n", + "for username, records in assignments.items():\n", + " for record in records:\n", + " record.metadata[\"annotator\"] = username\n", + " modified_records.append(record)\n", + "# Push the dataset with the modified records\n", + "dataset = rg.FeedbackDataset.for_text_classification(\n", + " labels=[\"World\", \"Sports\", \"Business\", \"Sci/Tech\"],\n", + " guidelines=\"Classify the articles into one of the four categories.\",\n", + " metadata_properties=[rg.TermsMetadataProperty(name=\"annotator\")]\n", + " )\n", + "dataset.add_records(modified_records)\n", + "remote_dataset = dataset.push_to_argilla(name=\"text_classification20\", workspace=\"admin\")\n", + "```\n", + "
    " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "To sum up, we have shown how to assign records with full and controlled overlap. In scenarios involving controlled overlap, we explored the methods for distributing records among groups of annotators and a specified list of users through the use of `assign_records` and `assign_workspaces`. Additionally, we examined the technique for transferring these allocated records to their respective workspaces." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/_source/tutorials_and_integrations/tutorials/feedback/end2end_examples/configure-users-and-workspaces-000.ipynb b/docs/_source/tutorials_and_integrations/tutorials/feedback/end2end_examples/configure-users-and-workspaces-000.ipynb new file mode 100644 index 0000000000..1395a862e9 --- /dev/null +++ b/docs/_source/tutorials_and_integrations/tutorials/feedback/end2end_examples/configure-users-and-workspaces-000.ipynb @@ -0,0 +1,757 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "fc0c600b", + "metadata": { + "editable": true, + "papermill": { + "duration": 0.046655, + "end_time": "2023-11-15T10:59:17.308341", + "exception": false, + "start_time": "2023-11-15T10:59:17.261686", + "status": "completed" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "# Configuring `Users` and `Workspaces`\n", + "\n", + "This tutorial is part of a series in which we will get to know the `FeedbackDataset`. In this step, we will show how to configure `Users` and `Workspaces`. If you need additional context, consult [user management](../../../../getting_started/installation/configurations/user_management.md) and [workspace management](../../../../getting_started/installation/configurations/workspace_management.md).\n", + "\n", + "![workflow](../../../../_static/tutorials/end2end/base/workflow_users_and_workspaces.svg)" + ] + }, + { + "cell_type": "markdown", + "id": "e557a44e-26fb-4b52-91ec-2b49d550a5cd", + "metadata": { + "papermill": { + "duration": 0.011949, + "end_time": "2023-11-15T10:59:17.340523", + "exception": false, + "start_time": "2023-11-15T10:59:17.328574", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Table of Contents\n", + "\n", + "1. [Configure a FeedbackDataset](#create-feedbackdataset)\n", + "2. [Add records](#add-records)\n", + " 1. [From a Hugging Face dataset](#create-feedbackdataset-datasets)\n", + " 2. [From a pandas.DataFrame](#create-feedbackdataset-pandas)\n", + "3. [Save a FeedbackDataset](#save-the-dataset)\n", + " 1. [Push our FeedbackDataset to Argilla](#push-to-argilla)\n", + " 2. [Push our FeedbackDataset to the Hugging Face hub](#push-to-hf-hub)\n", + "4. [Conclusions](#conclusions)" + ] + }, + { + "cell_type": "markdown", + "id": "566b7b82", + "metadata": { + "papermill": { + "duration": 0.007744, + "end_time": "2023-11-15T10:59:17.355040", + "exception": false, + "start_time": "2023-11-15T10:59:17.347296", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Running Argilla\n", + "\n", + "For this tutorial, you will need to have an Argilla server running. There are two main options for deploying and running Argilla:\n", + "\n", + "**Deploy Argilla on Hugging Face Spaces:** If you want to run tutorials with external notebooks (e.g., Google Colab) and you have an account on Hugging Face, you can deploy Argilla on Spaces with a few clicks:\n", + "\n", + "[![deploy on spaces](https://huggingface.co/datasets/huggingface/badges/raw/main/deploy-to-spaces-lg.svg)](https://huggingface.co/new-space?template=argilla/argilla-template-space)\n", + "\n", + "For details about configuring your deployment, check the [official Hugging Face Hub guide](https://huggingface.co/docs/hub/spaces-sdks-docker-argilla).\n", + "\n", + "**Launch Argilla using Argilla's quickstart Docker image**: This is the recommended option if you want [Argilla running on your local machine](../../../../getting_started/quickstart.md). Note that this option will only let you run the tutorial locally and not with an external notebook service.\n", + "\n", + "For more information on deployment options, please check the Deployment section of the documentation.\n", + "\n", + "
    \n", + "\n", + "Tip\n", + "\n", + "This tutorial is a Jupyter Notebook. There are two options to run it:\n", + "\n", + "- Use the Open in Colab button at the top of this page. This option allows you to run the notebook directly on Google Colab. Don't forget to change the runtime type to GPU for faster model training and inference.\n", + "- Download the .ipynb file by clicking on the View source link at the top of the page. This option allows you to download the notebook and run it on your local machine or on a Jupyter notebook tool of your choice.\n", + "
    " + ] + }, + { + "cell_type": "markdown", + "id": "2ec89efe-11a2-46ac-a9af-467d2628f7fd", + "metadata": { + "papermill": { + "duration": 0.004557, + "end_time": "2023-11-15T10:59:17.364909", + "exception": false, + "start_time": "2023-11-15T10:59:17.360352", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "First let's install our dependencies and import the necessary libraries:" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "bbbd3c38-1e3c-4d7f-97e6-22dd3b668fc8", + "metadata": { + "editable": true, + "papermill": { + "duration": 3.857186, + "end_time": "2023-11-15T10:59:21.228277", + "exception": false, + "start_time": "2023-11-15T10:59:17.371091", + "status": "completed" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Looking in indexes: https://pypi.org/simple, https://dmrepository.datamaran.com:8443/repository/dmPYTHON/simple\n", + "Requirement 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opensearch-py, fastapi, elasticsearch8, brotli-asgi, alembic\n", + " Attempting uninstall: urllib3\n", + " Found existing installation: urllib3 2.0.4\n", + " Uninstalling urllib3-2.0.4:\n", + " Successfully uninstalled urllib3-2.0.4\n", + " Attempting uninstall: typing-extensions\n", + " Found existing installation: typing_extensions 4.7.1\n", + " Uninstalling typing_extensions-4.7.1:\n", + " Successfully uninstalled typing_extensions-4.7.1\n", + " Attempting uninstall: backoff\n", + " Found existing installation: backoff 2.2.1\n", + " Uninstalling backoff-2.2.1:\n", + " Successfully uninstalled backoff-2.2.1\n", + "Successfully installed Mako-1.3.0 aiofiles-22.1.0 aiosqlite-0.19.0 alembic-1.9.4 backoff-1.11.1 bcrypt-4.0.1 brotli-1.1.0 brotli-asgi-1.2.0 cryptography-41.0.5 ecdsa-0.18.0 elastic-transport-8.10.0 elasticsearch8-8.7.0 fastapi-0.104.1 greenlet-3.0.1 httptools-0.6.1 joblib-1.3.2 opensearch-py-2.0.1 passlib-1.7.4 pyasn1-0.5.1 python-dotenv-1.0.0 python-jose-3.3.0 python-multipart-0.0.6 rsa-4.9 scikit-learn-1.3.2 segment-analytics-python-2.2.0 smart-open-6.4.0 starlette-0.27.0 threadpoolctl-3.2.0 typing-extensions-4.8.0 urllib3-1.26.18 uvicorn-0.20.0 uvloop-0.19.0 watchfiles-0.21.0 websockets-12.0\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n" + ] + } + ], + "source": [ + "!pip install \"argilla[server]\"" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "51b3b93c", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import argilla as rg\n", + "from argilla._constants import DEFAULT_API_KEY" + ] + }, + { + "cell_type": "markdown", + "id": "c330bed5-38d1-45bf-b871-98e629ab3af8", + "metadata": {}, + "source": [ + "In order to run this notebook we will need some credentials to push and load datasets from `Argilla` and 🤗`hub`, let's set them in the following cell:" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "ca4fd7ae-7e31-405e-84c1-974828a903bd", + "metadata": { + "editable": true, + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "completed" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "parameters" + ] + }, + "outputs": [], + "source": [ + "# Argilla credentials\n", + "api_url = \"http://localhost:6900\" # \"https://.hf.space\"\n", + "api_key = DEFAULT_API_KEY # admin.apikey\n", + "hf_token = None # not used in this tutorial" + ] + }, + { + "cell_type": "markdown", + "id": "3464037d-e21e-4db4-bfb4-e300fe4c9e53", + "metadata": {}, + "source": [ + "Log to argilla:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "728e6af7", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/davidberenstein/Documents/programming/argilla/argilla/.venv/lib/python3.10/site-packages/argilla/client/client.py:124: UserWarning: Default user was detected and no workspace configuration was provided, so the default 'argilla' workspace will be used. If you want to setup another workspace, use the `rg.set_workspace` function or provide a different one on `rg.init`\n", + " warnings.warn(\n", + "/Users/davidberenstein/Documents/programming/argilla/argilla/.venv/lib/python3.10/site-packages/argilla/client/client.py:152: UserWarning: You're connecting to Argilla Server 1.19.0 using a different client version (1.14.0).\n", + "This may lead to potential compatibility issues during your experience.\n", + "To ensure a seamless and optimized connection, we highly recommend aligning your client version with the server version.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "rg.init(\n", + " api_url=api_url,\n", + " api_key=api_key\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "0279dbe1", + "metadata": {}, + "source": [ + "Alternatively, we can use the `CLI` to run the `argilla login` command and pass the `api_url` and `api_key` there." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "8d9f3570", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[31m╭─\u001b[0m\u001b[31m \u001b[0m\u001b[32mLogged in\u001b[0m\u001b[31m \u001b[0m\u001b[31m─────────────────────────────────────────────────────────────────\u001b[0m\u001b[31m─╮\u001b[0m\n", + "\u001b[31m│\u001b[0m Logged in successfully to 'http://localhost:6900' Argilla server! \u001b[31m│\u001b[0m\n", + "\u001b[31m╰──────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n" + ] + } + ], + "source": [ + "!python -m argilla login --api-url {api_url} --api-key {api_key}" + ] + }, + { + "cell_type": "markdown", + "id": "fa470f0b", + "metadata": {}, + "source": [ + "### Enable Telemetry" + ] + }, + { + "cell_type": "markdown", + "id": "95e3ca39", + "metadata": {}, + "source": [ + "We gain valuable insights from how you interact with our tutorials. To improve ourselves in offering you the most suitable content, using the following lines of code will help us understand that this tutorial is serving you effectively. Though this is entirely anonymous, you can choose to skip this step if you prefer. For more info, please check out the [Telemetry](../../reference/telemetry.md) page." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7671fbb5", + "metadata": {}, + "outputs": [], + "source": [ + "from argilla.utils.telemetry import tutorial_running\n", + "\n", + "tutorial_running()" + ] + }, + { + "cell_type": "markdown", + "id": "f50177aa-4c94-4b8f-8293-a3710e3cb0ca", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Configure `Users`" + ] + }, + { + "cell_type": "markdown", + "id": "529f4e77-6c05-4d59-b153-170acee36b97", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### Current active `User`\n", + "\n", + "For this tutorial, we'll start with exploring the currently active user we've configured during the initialization. We can do this using our `Python` client or `CLI`." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "fa3ffa5f", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "User(id=f3cc0d01-5129-41a6-a722-6c51b097abd0, username=argilla, role=owner, api_key=argilla.apikey, first_name=, last_name=None, inserted_at=2023-10-28 11:26:52.618074, updated_at=2023-10-28 11:26:52.618074)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "user = rg.User.me()\n", + "user" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "8b01b473-a815-427c-986e-6c2bedf1b5d7", + "metadata": { + "editable": true, + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "completed" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[31m╭─\u001b[0m\u001b[31m \u001b[0m\u001b[32mCurrent User\u001b[0m\u001b[31m \u001b[0m\u001b[31m──────────────────────────────────────────────────────────────\u001b[0m\u001b[31m─╮\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[1;33m • \u001b[0m\u001b[1mUsername\u001b[0m: argilla \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[1;33m • \u001b[0m\u001b[1mRole\u001b[0m: owner \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[1;33m • \u001b[0m\u001b[1mFirst name\u001b[0m: \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[1;33m • \u001b[0m\u001b[1mLast name\u001b[0m: None \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[1;33m • \u001b[0m\u001b[1mAPI Key\u001b[0m: argilla.apikey \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[1;33m • \u001b[0m\u001b[1mWorkspaces\u001b[0m: ['argilla', 'awesome-argilla-datasets'] \u001b[31m│\u001b[0m\n", + "\u001b[31m╰──────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n" + ] + } + ], + "source": [ + "!python -m argilla whoami" + ] + }, + { + "cell_type": "markdown", + "id": "5b4eb4ff-9530-4542-9975-25d0f4f725c1", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "completed" + }, + "tags": [] + }, + "source": [ + "As we can see, we are logged into the default `User` called `argilla`, which has the `owner` role. This `role` is allowed to create new `Users` and configure workspaces.\n", + "\n", + "### Create `User`\n", + "\n", + "Next, we will create two new users with the role of `admin` and `annotator` to configure our first small team. For convenience and reproducibility, we will call them `admin` and `annotator`, and we'll set their `password` to the default `argilla.apikey`. Their `api_key` will be randomly generated according to safety standards.\n", + "We will first create the `admin` user with our `Python` client." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "46387e9a", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "try:\n", + " user = rg.User.from_name(\"admin\")\n", + "except Exception:\n", + " user = rg.User.create(\n", + " username=\"admin\",\n", + " password=api_key,\n", + " role=\"admin\",\n", + " )\n", + "user" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "acdbee81", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "!python -m argilla users create --username owner --role owner --password {api_key}" + ] + }, + { + "cell_type": "markdown", + "id": "46e05aa8", + "metadata": {}, + "source": [ + "### Update `User`\n", + "\n", + "At the moment we can only update `User` role and this can only be done using the `CLI`." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "879b3c4d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "User with username 'owner' does not exists in database. Skipping...\n" + ] + } + ], + "source": [ + "!python -m argilla server database users update owner --role admin" + ] + }, + { + "cell_type": "markdown", + "id": "34348bdd", + "metadata": {}, + "source": [ + "Now, let's quickly change this back to avoid having wrong access in our organization." + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "2490bb88", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[User(id=f3cc0d01-5129-41a6-a722-6c51b097abd0, username=argilla, role=owner, api_key=argilla.apikey, first_name=, last_name=None, inserted_at=2023-10-28 11:26:52.618074, updated_at=2023-10-28 11:26:52.618074),\n", + " User(id=e26a191f-382d-4fe8-9cb8-3d40e0588c20, username=jose, role=annotator, api_key=s2npW--dIQs1cLpTg4Tqd_zkWdzxu3WiuPkIcAKBo2q4z3iWWM9xPt2KDjBBr4fL3Cu5EBiED7n5mMdE5K7i4kRj126QN7T_F7QYyYQNh8A, first_name=jose, last_name=None, inserted_at=2023-11-19 15:50:37.630232, updated_at=2023-11-19 15:50:37.630232),\n", + " User(id=bf9e8d61-f969-478f-8ea3-10f6f17119c6, username=dani, role=annotator, api_key=jMZWECjYgp4GHjcrpFOXSY1r6y4RSSRiXEGg7depEABCd18RanAmLdVk114MFuQxaHAi3d-TRgjr8OrH8pBYT54wqjCGUJD9Gn4UNa8PooE, first_name=dani, last_name=None, inserted_at=2023-11-19 15:52:56.040739, updated_at=2023-11-19 15:52:56.040739),\n", + " User(id=4d067c1a-fbb7-4300-bf46-bc0f5e7954a5, username=paco, role=annotator, api_key=8Crwd_JGlvbM5UD7Og-vgBy5EyBE5aHLVXxt9yd-zdMnKHtITG6PzKV0ldBd_U0obOEOoUn6J36AVX-54FGofotsfkf1kH_UtTpqLml6cb8, first_name=paco, last_name=None, inserted_at=2023-11-19 15:52:56.301647, updated_at=2023-11-19 15:52:56.301647),\n", + " User(id=0d69c392-09c4-4204-ba47-f3698b8e6369, username=damian, role=annotator, api_key=-muo7NpiWp0CnZrg6wFAHICqZHg8N5Itu4HKpTEwVTgZoOi8V1LJCr-qjVMOyDELd9rjKQWMMIvdkUlJZlxB7mNS3HNpusH4-Bra78yegK4, first_name=damian, last_name=None, inserted_at=2023-11-19 15:52:56.559801, updated_at=2023-11-19 15:52:56.559801),\n", + " User(id=693b2392-0606-4514-a416-f4231a1455d9, username=leyre, role=annotator, api_key=fkXIRgR3Pl5sjTG32uvUnaNq-0mwQYTPh9RXbeHWpmX1aD4fP83EgfTl9yN0_0rDUqkaDPMEVYVYSIgM0OazFaCudKCNDOgADzaDC4xneZc, first_name=leyre, last_name=None, inserted_at=2023-11-19 15:52:56.822445, updated_at=2023-11-19 15:52:56.822445),\n", + " User(id=13a3fb32-ac56-4d55-a4b2-685f4d768bc4, username=sara, role=annotator, api_key=UUC-m8QS0MAufPvqigUm6iZNnA7UFEr26AxuOlEUjqTntNBoX2tvQmf3NqDK6MtqkD1SmigKsBC2JZ1PqpEtqsLfuj94c3pjCHeNvoStkdc, first_name=sara, last_name=None, inserted_at=2023-11-19 15:52:57.082838, updated_at=2023-11-19 15:52:57.082838),\n", + " User(id=92a388b0-f0e4-425c-91e7-1f2da1f6e78a, username=alvaro, role=annotator, api_key=WPZXwfeIP1U4bGXkJfGsUi0DSXVvn0yuJG0i99XFW-glSmCmsI0FqnHu6GKO5XmVyUHIK7rBLeSScvo133e_2TljP2_mQkjiP2-jQZ7o8zY, first_name=alvaro, last_name=None, inserted_at=2023-11-19 15:52:57.345579, updated_at=2023-11-19 15:52:57.345579),\n", + " User(id=f2c54769-6b4c-44d6-8a87-ff7c04780a47, username=gabri, role=annotator, api_key=gncNEUBCw1X08hgkz2CS-I_7d46L7B0RMM9xA5_-iCirEQbpwtP_XyKhUS1ZrbaKXMveOlj5l44UX6i9auKDCF5Zx0MUyEVplvWgy9zJWho, first_name=gabri, last_name=None, inserted_at=2023-11-19 15:52:57.604679, updated_at=2023-11-19 15:52:57.604679),\n", + " User(id=0c00fd8f-7679-4a36-a11c-57e57358ee28, username=david, role=annotator, api_key=B7EbxxM-0V9YJGvDbBJMzSBksZEpyDJCccdgaigqvUUjF49PFhFn8rONwU3dS5iuvjjuQObVmCLGqii7X6XdJSlYtaKefldLP7y_cODjijk, first_name=david, last_name=None, inserted_at=2023-11-19 15:52:57.872378, updated_at=2023-11-19 15:52:57.872378),\n", + " User(id=a6d3c5f6-42fb-4c3f-9be9-dd789062404d, username=amelie, role=annotator, api_key=vuL2ab0PRbHaqFLyHJRw_jxNyJdhV_vL4P8kvFixUz1OzxsZU0u8NIuQ_Wrgr6eXw6_o1goeiLCFZtTZvMDj4SUc5wuOidU6pCPv1KyPKlE, first_name=amelie, last_name=None, inserted_at=2023-11-19 15:52:58.132252, updated_at=2023-11-19 15:52:58.132252),\n", + " User(id=d3d08f58-f6d8-4308-8027-c519e1358bb2, username=averill, role=annotator, api_key=aZwZDmZTliO2DNKoRiSl35q7wFAhI_2gx911qiWA3S7OVpkyrUca1BJ8FXt944NN5kxvvIXLvuA5pQAn5ALwrqsCBn_Mq0pJA_9bfGFe3xM, first_name=averill, last_name=None, inserted_at=2023-11-19 15:52:58.390813, updated_at=2023-11-19 15:52:58.390813),\n", + " User(id=37d41d50-039f-4da6-8d6a-ad41adc3110b, username=kursat, role=annotator, api_key=h2g6iPgzvF2fKJhbGAU7SOrryJpcfC0JvHinnBSM3Io8c4750_6khfBkqVi-iUj4kwLDNZiSBJ0rmzav-ANYc055kHCC4E3qMcrv7AizLl4, first_name=kursat, last_name=None, inserted_at=2023-11-19 15:52:58.651804, updated_at=2023-11-19 15:52:58.651804),\n", + " User(id=6d8cd4e1-3838-45db-91e0-010b341ec59b, username=agus, role=annotator, api_key=hKaGVFK-Nx8Fph4BfDhuoxeSXLjBHfGX6gsa4coZ2GP4NduVPRevELYiko0ZoJzBID7bf-skoDk66i4Xd7GfOcF03Ydq8xIBFeNbUu9Wdcs, first_name=agus, last_name=None, inserted_at=2023-11-19 15:52:58.912333, updated_at=2023-11-19 15:52:58.912333),\n", + " User(id=8bf5332f-783b-4bd5-9a15-c47602ad2958, username=ignacio, role=annotator, api_key=QQcTzdcdF0TIYmj_K6JY2pQj-SOZxF2h7tUHZSnOP-ijh9MrbiwNQ3Pu1JGLlwqCkmrovOk_-nsTc0OIse-mXoGpveR24lVR1eLFWOZ2Kqc, first_name=ignacio, last_name=None, inserted_at=2023-11-19 15:52:59.173538, updated_at=2023-11-19 15:52:59.173538),\n", + " User(id=2903f079-3c67-44ea-933a-7947af7cc562, username=leire, role=annotator, api_key=OC12O5VT6aNIfTlkzSjmC4-yD1WqP3HYtvlnj1G04ER4jNLpIIgZTvVEEmIUfKMA_nwWwa3vYDlU-SDHslDYCB7gY2_U-qC6jDOjDdRCfCE, first_name=leire, last_name=None, inserted_at=2023-11-20 14:00:40.465554, updated_at=2023-11-20 14:00:40.465554),\n", + " User(id=85a3ed76-f8fd-4117-8e3b-ff0005b62568, username=admin, role=admin, api_key=_69UuR06SaR8Ky0YnAVJEDkwJyJkOENz-p8Q-rGZuMZS_AuonNVu6lR44rsonIsJc1P-HfnCT_anA9cFN0-u_38aivHddu5UYK-4UWavqjM, first_name=admin, last_name=None, inserted_at=2023-11-23 08:12:29.504917, updated_at=2023-11-23 08:12:29.504917),\n", + " User(id=4914060f-e1c4-486a-bbaa-8e052bccbef4, username=owner, role=owner, api_key=Dbg477IxBWq7MdcxCQnrao-ZcMjIqOh50hjaJlIkfOZXC7mIkTFhwc3E17YPgX6E9wGZkX53LXANgaUiqqOoQo5WZ-a9I_HJgAR3NYgm9FI, first_name=owner, last_name=None, inserted_at=2023-11-23 08:13:58.344030, updated_at=2023-11-23 08:13:58.344030)]" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(rg.User.list())" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "c89f7d26", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "User with username 'owner' does not exists in database. Skipping...\n" + ] + } + ], + "source": [ + "!python -m argilla server database users update owner --role owner" + ] + }, + { + "cell_type": "markdown", + "id": "408cb3c9-c784-45c8-b206-058a23b801d8", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Conclusion" + ] + }, + { + "cell_type": "markdown", + "id": "6cdb1244-1589-4e85-be36-e0c959817c6d", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "completed" + }, + "tags": [] + }, + "source": [ + "In this tutorial, we created some Argilla `Users` and `Workspaces`, and created some annotation team configurations.\n", + "\n", + "If you need additional context, consult [user management](../../../../getting_started/installation/configurations/user_management.md) and [workspace management](../../../../getting_started/installation/configurations/workspace_management.md).\n", + "\n", + "Alternatively, you can go to the next step and [create a FeedbackDataset](/tutorials_and_integrations/tutorials/feedback/end2end_examples/create-dataset.html)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "papermill": { + "default_parameters": {}, + "duration": 5.95317, + "end_time": "2023-11-15T10:59:21.949504", + "environment_variables": {}, + "exception": null, + "input_path": "/home/agustin/github_repos/argilla-io/argilla/docs/_source/practical_guides/examples/text_classification/text-classification-create-dataset.ipynb", + "output_path": "/home/agustin/github_repos/argilla-io/argilla/docs/_source/practical_guides/examples/text_classification/text-classification-create-dataset.ipynb", + "parameters": {}, + "start_time": "2023-11-15T10:59:15.996334", + "version": "2.5.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/_source/tutorials_and_integrations/tutorials/feedback/end2end_examples/create-dataset-001.ipynb b/docs/_source/tutorials_and_integrations/tutorials/feedback/end2end_examples/create-dataset-001.ipynb new file mode 100644 index 0000000000..48298229bc --- /dev/null +++ b/docs/_source/tutorials_and_integrations/tutorials/feedback/end2end_examples/create-dataset-001.ipynb @@ -0,0 +1,1235 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "fc0c600b", + "metadata": { + "papermill": { + "duration": 0.046655, + "end_time": "2023-11-15T10:59:17.308341", + "exception": false, + "start_time": "2023-11-15T10:59:17.261686", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Creating a `FeedbackDataset`\n", + "\n", + "This tutorial is part of a series in which we will get to know the `FeedbackDataset`. Before starting this tutorial, you need to do the tutorial on [configuring users and workspaces](/tutorials_and_integrations/tutorials/feedback/end2end_examples/configure-users-and-workspaces.html). In this step, we will show how to configure a `FeedbackDataset` and add `FeedbackRecords` to it. If you need additional context, consult [our practical guide on creating a dataset](../../../../practical_guides/create_dataset.md).\n", + "\n", + "![workflow](../../../../_static/tutorials/end2end/base/workflow_create_dataset.svg)\n", + "\n", + "We will start by creating a basic dataset using the [ag_news](https://huggingface.co/datasets/ag_news) dataset as an example and push it to `Argilla` and the Hugging Face `hub`." + ] + }, + { + "cell_type": "markdown", + "id": "e557a44e-26fb-4b52-91ec-2b49d550a5cd", + "metadata": { + "editable": true, + "papermill": { + "duration": 0.011949, + "end_time": "2023-11-15T10:59:17.340523", + "exception": false, + "start_time": "2023-11-15T10:59:17.328574", + "status": "completed" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Table of Contents\n", + "\n", + "1. [Configure a FeedbackDataset](#create-feedbackdataset)\n", + "2. [Add FeedbackRecords](#add-records)\n", + " 1. [From a Hugging Face dataset](#create-feedbackdataset-datasets)\n", + " 2. [From a pandas.DataFrame](#create-feedbackdataset-pandas)\n", + "3. [Save and load a FeedbackDataset](#save-the-dataset)\n", + " 1. [Push our FeedbackDataset to Argilla](#push-to-argilla)\n", + " 2. [Push our FeedbackDataset to the Hugging Face hub](#push-to-hf-hub)\n", + "4. [Conclusions](#conclusions)" + ] + }, + { + "cell_type": "markdown", + "id": "566b7b82", + "metadata": { + "papermill": { + "duration": 0.007744, + "end_time": "2023-11-15T10:59:17.355040", + "exception": false, + "start_time": "2023-11-15T10:59:17.347296", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Running Argilla\n", + "\n", + "For this tutorial, you will need to have an Argilla server running. There are two main options for deploying and running Argilla:\n", + "\n", + "**Deploy Argilla on Hugging Face Spaces:** If you want to run tutorials with external notebooks (e.g., Google Colab) and you have an account on Hugging Face, you can deploy Argilla on Spaces with a few clicks:\n", + "\n", + "[![deploy on spaces](https://huggingface.co/datasets/huggingface/badges/raw/main/deploy-to-spaces-lg.svg)](https://huggingface.co/new-space?template=argilla/argilla-template-space)\n", + "\n", + "For details about configuring your deployment, check the [official Hugging Face Hub guide](https://huggingface.co/docs/hub/spaces-sdks-docker-argilla).\n", + "\n", + "**Launch Argilla using Argilla's quickstart Docker image**: This is the recommended option if you want [Argilla running on your local machine](../../../../getting_started/quickstart.md). Note that this option will only let you run the tutorial locally and not with an external notebook service.\n", + "\n", + "For more information on deployment options, please check the Deployment section of the documentation.\n", + "\n", + "
    \n", + "\n", + "Tip\n", + "\n", + "This tutorial is a Jupyter Notebook. There are two options to run it:\n", + "\n", + "- Use the Open in Colab button at the top of this page. This option allows you to run the notebook directly on Google Colab. Don't forget to change the runtime type to GPU for faster model training and inference.\n", + "- Download the .ipynb file by clicking on the View source link at the top of the page. This option allows you to download the notebook and run it on your local machine or on a Jupyter notebook tool of your choice.\n", + "
    " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "papermill": { + "duration": 0.004557, + "end_time": "2023-11-15T10:59:17.364909", + "exception": false, + "start_time": "2023-11-15T10:59:17.360352", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "First let's install our dependencies and import the necessary libraries:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "bbbd3c38-1e3c-4d7f-97e6-22dd3b668fc8", + "metadata": { + "editable": true, + "papermill": { + "duration": 3.857186, + "end_time": "2023-11-15T10:59:21.228277", + "exception": false, + "start_time": "2023-11-15T10:59:17.371091", + "status": "completed" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: argilla in /Users/agus/github_repos/argilla-io/argilla/.venv/lib/python3.10/site-packages (1.20.0.dev0)\n", + "Requirement already satisfied: 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(from pandas->datasets) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /Users/agus/github_repos/argilla-io/argilla/.venv/lib/python3.10/site-packages (from pandas->datasets) (2022.1)\n", + "Requirement already satisfied: six>=1.5 in /Users/agus/github_repos/argilla-io/argilla/.venv/lib/python3.10/site-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n" + ] + } + ], + "source": [ + "!pip install argilla\n", + "!pip install datasets" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "51b3b93c", + "metadata": { + "editable": true, + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "completed" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import argilla as rg\n", + "from argilla._constants import DEFAULT_API_KEY\n", + "from datasets import load_dataset" + ] + }, + { + "cell_type": "markdown", + "id": "c330bed5-38d1-45bf-b871-98e629ab3af8", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "In order to run this notebook we will need some credentials to push and load datasets from `Argilla` and 🤗`hub`, let's set them in the following cell:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ca4fd7ae-7e31-405e-84c1-974828a903bd", + "metadata": { + "editable": true, + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "completed" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "parameters" + ] + }, + "outputs": [], + "source": [ + "# Argilla credentials\n", + "api_url = \"http://localhost:6900\" # \"https://.hf.space\"\n", + "api_key = DEFAULT_API_KEY # admin.apikey\n", + "# Huggingface credentials\n", + "hf_token = \"hf_...\"" + ] + }, + { + "cell_type": "markdown", + "id": "3464037d-e21e-4db4-bfb4-e300fe4c9e53", + "metadata": {}, + "source": [ + "Log to argilla:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "728e6af7", + "metadata": { + "editable": true, + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "completed" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "#papermill_description=logging-to-argilla\n", + "rg.init(\n", + " api_url=api_url,\n", + " api_key=api_key\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "fa470f0b", + "metadata": {}, + "source": [ + "### Enable Telemetry" + ] + }, + { + "cell_type": "markdown", + "id": "95e3ca39", + "metadata": {}, + "source": [ + "We gain valuable insights from how you interact with our tutorials. To improve ourselves in offering you the most suitable content, using the following lines of code will help us understand that this tutorial is serving you effectively. Though this is entirely anonymous, you can choose to skip this step if you prefer. For more info, please check out the [Telemetry](../../reference/telemetry.md) page." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7671fbb5", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from argilla.utils.telemetry import tutorial_running\n", + "\n", + "tutorial_running()" + ] + }, + { + "cell_type": "markdown", + "id": "f50177aa-4c94-4b8f-8293-a3710e3cb0ca", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Configure a `FeedbackDataset`
    " + ] + }, + { + "cell_type": "markdown", + "id": "529f4e77-6c05-4d59-b153-170acee36b97", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "completed" + }, + "tags": [] + }, + "source": [ + "For this tutorial we will use the [ag_news](https://huggingface.co/datasets/ag_news) dataset which can be downloaded from the 🤗`hub`. We will load only the first 1000 items from the training sample." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "8b01b473-a815-427c-986e-6c2bedf1b5d7", + "metadata": { + "editable": true, + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "completed" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Dataset({\n", + " features: ['text', 'label'],\n", + " num_rows: 1000\n", + "})" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds = load_dataset(\"ag_news\", split=\"train[:1000]\")\n", + "ds" + ] + }, + { + "cell_type": "markdown", + "id": "5b4eb4ff-9530-4542-9975-25d0f4f725c1", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "completed" + }, + "tags": [] + }, + "source": [ + "We will just load the first 1000 records for this tutorial, but feel free to test the full dataset." + ] + }, + { + "cell_type": "markdown", + "id": "6d2e7997-62c6-49b3-9462-acd6143346a1", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "completed" + }, + "tags": [] + }, + "source": [ + "This dataset contains a collection of news articles (we can see the content in the `text` column), which have been asigned one of the following classification `labels`: *World (0), Sports (1), Business (2), Sci/Tech (3)*.\n", + "\n", + "Let's use the [task templates](https://docs.argilla.io/en/latest/practical_guides/create_dataset.html#task-templates) to create a feedback dataset ready for `text-classification`." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "7294b709", + "metadata": { + "editable": true, + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "completed" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "FeedbackDataset(\n", + " fields=[TextField(name='text', title='Text', required=True, type='text', use_markdown=False)]\n", + " questions=[LabelQuestion(name='label', title='Label', description='Classify the text by selecting the correct label from the given list of labels.', required=True, type='label_selection', labels=['World', 'Sports', 'Business', 'Sci/Tech'], visible_labels=None)]\n", + " guidelines=Classify the articles into one of the four categories.)\n", + " metadata_properties=[])\n", + ")" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "feedback_dataset = rg.FeedbackDataset.for_text_classification(\n", + " labels=[\"World\", \"Sports\", \"Business\", \"Sci/Tech\"],\n", + " guidelines=\"Classify the articles into one of the four categories.\",\n", + ")\n", + "feedback_dataset" + ] + }, + { + "cell_type": "markdown", + "id": "5a5238ea", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "completed" + }, + "tags": [] + }, + "source": [ + "We could compare this dataset with the custom configuration we would use previously (we can take a look at the [custom configuration](https://docs.argilla.io/en/latest/practical_guides/create_dataset.html#custom-configuration) for more information on the creation of a `FeedbackDataset` when we want a finer control):" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "f4823931-39a2-4942-ba83-894d62e0b7cc", + "metadata": { + "editable": true, + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "completed" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "FeedbackDataset(\n", + " fields=[TextField(name='text', title='Text from the article', required=True, type='text', use_markdown=False)]\n", + " questions=[LabelQuestion(name='label', title='In which category does this article fit?', description=None, required=True, type='label_selection', labels={'World': '0', 'Sports': '1', 'Business': '2', 'Sci/Tech': '3'}, visible_labels=None)]\n", + " guidelines=Classify the articles into one of the four categories.)\n", + " metadata_properties=[])\n", + ")" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "feedback_dataset_long = rg.FeedbackDataset(\n", + " guidelines=\"Classify the articles into one of the four categories.\",\n", + " fields=[\n", + " rg.TextField(name=\"text\", title=\"Text from the article\"),\n", + " ],\n", + " questions=[\n", + " rg.LabelQuestion(\n", + " name=\"label\",\n", + " title=\"In which category does this article fit?\",\n", + " labels={\"World\": \"0\", \"Sports\": \"1\", \"Business\": \"2\", \"Sci/Tech\": \"3\"},\n", + " required=True,\n", + " visible_labels=None\n", + " )\n", + " ]\n", + ")\n", + "feedback_dataset_long" + ] + }, + { + "cell_type": "markdown", + "id": "77ad10bc-d908-41ba-9a5c-c9cc9d56b67f", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Add `FeedbackRecords` \n", + "\n", + "### From a Hugging Face `dataset` " + ] + }, + { + "cell_type": "markdown", + "id": "9c194d68-6a87-4bc0-9d4e-f2afdb0561d9", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "completed" + }, + "tags": [] + }, + "source": [ + "The next step once we have our `FeedbackDataset` created is adding the [`FeedbackRecords`](https://docs.argilla.io/en/latest/getting_started/cheatsheet.html#create-records) to it." + ] + }, + { + "cell_type": "markdown", + "id": "5110f19b-8bf2-4d48-8a1f-ffd89cfa20f5", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "completed" + }, + "tags": [] + }, + "source": [ + "In order to create our records we can just loop over the items in the `datasets.Dataset`." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "825342c7-ef43-4747-8523-18f3e3fc6bd5", + "metadata": { + "editable": true, + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "completed" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "records = []\n", + "for i, item in enumerate(ds):\n", + " records.append(\n", + " rg.FeedbackRecord(\n", + " fields={\n", + " \"text\": item[\"text\"],\n", + " },\n", + " external_id=f\"record-{i}\"\n", + " )\n", + " )\n", + "\n", + "# We can add an external_id to each record to identify it later." + ] + }, + { + "cell_type": "markdown", + "id": "3eb1ae7a-01f9-439b-87ac-6d0064164c80", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### From a `pandas.DataFrame` " + ] + }, + { + "cell_type": "markdown", + "id": "dba19b65-60d3-4507-aca6-1b4b9bc77d08", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "completed" + }, + "tags": [] + }, + "source": [ + "If we had our data in a different format, let's say a `csv` file, maybe it's more direct to read the data using pandas for that.\n", + "\n", + "We will transform our dataset to pandas format for this example, and the remaining `FeedbackRecord` creation remains just the same:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "82beb35b-264f-491a-a6be-bd53f40fb509", + "metadata": { + "editable": true, + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "completed" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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    textlabel
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    \n", + "
    " + ], + "text/plain": [ + " text label\n", + "0 Wall St. Bears Claw Back Into the Black (Reute... 2\n", + "1 Carlyle Looks Toward Commercial Aerospace (Reu... 2\n", + "2 Oil and Economy Cloud Stocks' Outlook (Reuters... 2\n", + "3 Iraq Halts Oil Exports from Main Southern Pipe... 2\n", + "4 Oil prices soar to all-time record, posing new... 2" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_dataset = ds.to_pandas()\n", + "df_dataset.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7c7b33af-297c-44ab-981c-b62cf1406843", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "records_pandas = []\n", + "for i, item in df_dataset.iterrows():\n", + " records_pandas.append(\n", + " rg.FeedbackRecord(\n", + " fields={\n", + " \"text\": item[\"text\"],\n", + " },\n", + " external_id=f\"record-{i}\"\n", + " )\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "dd3f1c38-3b56-46e8-8cac-7b3d21beb38d", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Let's add our records to the dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "9bd0ec1c-1fcb-4d4f-a0a2-4f581a670e8b", + "metadata": { + "editable": true, + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "completed" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "feedback_dataset.add_records(records)" + ] + }, + { + "cell_type": "markdown", + "id": "16fe9266-7a03-4be6-8789-b141854bcdda", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "completed" + }, + "tags": [] + }, + "source": [ + "By now we have our dataset with the texts ready to be labeled, let's push it to `Argilla`." + ] + }, + { + "cell_type": "markdown", + "id": "b867eb5b-0b5a-42b1-88f2-f5b67519243e", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Save and load a `FeedbackDataset` \n", + "\n", + "### From `Argilla` " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8f5581dc-8509-484e-ad4e-c73d15227cc9", + "metadata": { + "editable": true, + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "completed" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "#papermill_description=push-dataset-to-argilla\n", + "try:\n", + " # delete old dataset\n", + " remote_dataset = feedback_dataset.from_argilla(name=\"end2end_textclassification\", workspace=\"argilla\")\n", + " remote_dataset.delete()\n", + "except:\n", + " pass\n", + "remote_dataset = feedback_dataset.push_to_argilla(name=\"end2end_textclassification\", workspace=\"argilla\")" + ] + }, + { + "cell_type": "markdown", + "id": "aff0f7fd-722f-4f92-912d-029b134848f3", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "completed" + }, + "tags": [] + }, + "source": [ + "If we go to our `Argilla` instance we should see a similar screen like the following." + ] + }, + { + "cell_type": "markdown", + "id": "827fce93", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "completed" + }, + "tags": [] + }, + "source": [ + "![feedback-dataset](../../../../_static/tutorials/end2end/text-classification/feedback-dataset-text-classification-1.png)" + ] + }, + { + "cell_type": "markdown", + "id": "e5f09e2a-e192-4526-a686-b4d516ca5066", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Where we can see the *Text from the article* we wanted, and the different labels to choose from." + ] + }, + { + "cell_type": "markdown", + "id": "fd877169-bd9a-4596-981d-47d2ff4c7c24", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "completed" + }, + "tags": [] + }, + "source": [ + "We can now download the dataset from `Argilla` just to check it:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "fbae2025-c4fe-4e0c-b445-9ee308baa4a5", + "metadata": { + "editable": true, + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "completed" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "RemoteFeedbackDataset(\n", + " id=52b0dfc2-ed85-4805-923c-5d51b51ec4c9\n", + " name=end2end_textclassification\n", + " workspace=Workspace(id=ce760ed7-0fdf-4d79-b9b7-1c0e4ea896cd, name=argilla, inserted_at=2023-11-23 09:46:05.591993, updated_at=2023-11-23 09:46:05.591993)\n", + " url=http://localhost:6900/dataset/52b0dfc2-ed85-4805-923c-5d51b51ec4c9/annotation-mode\n", + " fields=[RemoteTextField(id=UUID('2835bf0e-1259-45b9-a97c-f9b671395563'), client=None, name='text', title='Text', required=True, type='text', use_markdown=False)]\n", + " questions=[RemoteLabelQuestion(id=UUID('bb6fc4f0-e4b7-480c-84a1-df717de4ac97'), client=None, name='label', title='Label', description=None, required=True, type='label_selection', labels=['World', 'Sports', 'Business', 'Sci/Tech'], visible_labels=None)]\n", + " guidelines=Classify the articles into one of the four categories.\n", + " metadata_properties=[]\n", + ")" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "remote_dataset = rg.FeedbackDataset.from_argilla(\"end2end_textclassification\", workspace=\"argilla\")\n", + "remote_dataset" + ] + }, + { + "cell_type": "markdown", + "id": "1f16d868-a9df-4f01-85ac-06bfcee19101", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### From Hugging Face hub \n", + "\n", + "If we wanted to share our dataset with the world, we could use the Huggingface hub for it." + ] + }, + { + "cell_type": "markdown", + "id": "24aae42a-6b0f-40eb-9d92-4aafcb735844", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "completed" + }, + "tags": [] + }, + "source": [ + "First we need to login to huggingface. The following cell will log us with our previous token.\n", + "\n", + "If we don't have one already, we can obtain it from [here](https://huggingface.co/docs/hub/security-tokens) (remember to set the *write* access)." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "1fabcc89-323f-4f4f-9ab9-4b778dd93474", + "metadata": { + "editable": true, + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "completed" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Token will not been saved to git credential helper. Pass `add_to_git_credential=True` if you want to set the git credential as well.\n", + "Token is valid (permission: write).\n", + "Your token has been saved to /Users/agus/.cache/huggingface/token\n", + "Login successful\n" + ] + } + ], + "source": [ + "from huggingface_hub import login\n", + "\n", + "login(token=hf_token)" + ] + }, + { + "cell_type": "markdown", + "id": "694c381d-7f7d-4b2d-938a-20df260ec957", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "completed" + }, + "tags": [] + }, + "source": [ + "And now we can just call the method on the `FeedbackDataset`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1c471559-916c-4bf8-b115-3d1ba0be621b", + "metadata": { + "editable": true, + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "completed" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "#papermill_description=push-dataset-to-huggingface\n", + "remote_dataset.push_to_huggingface(\"argilla/end2end_textclassification\")" + ] + }, + { + "cell_type": "markdown", + "id": "761250bc", + "metadata": {}, + "source": [ + "We can now download the dataset from Hugging Face just to check it:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c5405ba2", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "local_dataset = rg.FeedbackDataset.from_huggingface(\"argilla/end2end_textclassification\")" + ] + }, + { + "cell_type": "markdown", + "id": "408cb3c9-c784-45c8-b206-058a23b801d8", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Conclusion" + ] + }, + { + "cell_type": "markdown", + "id": "6cdb1244-1589-4e85-be36-e0c959817c6d", + "metadata": { + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "completed" + }, + "tags": [] + }, + "source": [ + "In this tutorial we created an `Argilla` `FeedbackDataset` for text classification, starting from [ag_news](https://huggingface.co/datasets/ag_news).\n", + "\n", + "We created a `FeedbackDataset` for text classification with a `LabelQuestion`, from data stored as a `datasets.Dataset` and a `pandas.DataFrame`.\n", + "This dataset was pushed both to `Argilla` where we can curate and label the records, and finally pushed it to the 🤗`hub`.\n", + "\n", + "To learn more about how to work with the `FeedbackDataset` check the [cheatsheet](https://docs.argilla.io/en/latest/getting_started/cheatsheet.html#cheatsheet)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + }, + "papermill": { + "default_parameters": {}, + "duration": 5.95317, + "end_time": "2023-11-15T10:59:21.949504", + "environment_variables": {}, + "exception": null, + "input_path": "/home/agustin/github_repos/argilla-io/argilla/docs/_source/practical_guides/examples/text_classification/text-classification-create-dataset.ipynb", + "output_path": "/home/agustin/github_repos/argilla-io/argilla/docs/_source/practical_guides/examples/text_classification/text-classification-create-dataset.ipynb", + "parameters": {}, + "start_time": "2023-11-15T10:59:15.996334", + "version": "2.5.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/_source/tutorials_and_integrations/tutorials/feedback/end2end_examples/filter-and-query-008.ipynb b/docs/_source/tutorials_and_integrations/tutorials/feedback/end2end_examples/filter-and-query-008.ipynb new file mode 100644 index 0000000000..e34622be9f --- /dev/null +++ b/docs/_source/tutorials_and_integrations/tutorials/feedback/end2end_examples/filter-and-query-008.ipynb @@ -0,0 +1,679 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Filter and Query Records\n", + "\n", + " This tutorial is part of a series in which we will get to know the `FeedbackDataset`. In this step, we will show how to filter and query our records, a process that can be really useful for efficiently analyzing data, extracting relevant information, managing data size, and enabling focused insights for informed decision-making. You can have a look at the previous tutorials to add [metadata](./add-metadata-003.ipynb), [vectors](./add-vectors-004.ipynb) and [suggestions and responses](./add-suggestions-and-responses-005.ipynb). Feel free to check out the [practical guides](../../../../practical_guides/practical_guides.md) page for more in-depth information." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![workflow](../../../../_static/tutorials/end2end/base/workflow_sort_and_filter.svg)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Table of contents\n", + "\n", + "1. [Pull the Dataset](#Pull-the-Dataset)\n", + " 1. [From Argilla](#From-Argilla)\n", + " 2. [From HuggingFace Hub](#From-HuggingFace-Hub)\n", + "2. [Filter](#Filter)\n", + " 1. [By Fields Content](#By-Fields-Content)\n", + " 2. [By Metadata Property](#By-Metadata-Property)\n", + " 3. [By Suggestions and Responses](#By-Suggestions-and-Responses)\n", + "3. [Sort](#Sort)\n", + "4. [Semantic Search](#Semantic-Search)\n", + "5. [Conclusion](#Conclusion)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Running Argilla\n", + "\n", + "For this tutorial, you will need to have an Argilla server running. There are two main options for deploying and running Argilla:\n", + "\n", + "**Deploy Argilla on Hugging Face Spaces:** If you want to run tutorials with external notebooks (e.g., Google Colab) and you have an account on Hugging Face, you can deploy Argilla on Spaces with a few clicks:\n", + "\n", + "[![deploy on spaces](https://huggingface.co/datasets/huggingface/badges/raw/main/deploy-to-spaces-lg.svg)](https://huggingface.co/new-space?template=argilla/argilla-template-space)\n", + "\n", + "For details about configuring your deployment, check the [official Hugging Face Hub guide](https://huggingface.co/docs/hub/spaces-sdks-docker-argilla).\n", + "\n", + "**Launch Argilla using Argilla's quickstart Docker image**: This is the recommended option if you want [Argilla running on your local machine](../../../../getting_started/quickstart.md). Note that this option will only let you run the tutorial locally and not with an external notebook service.\n", + "\n", + "For more information on deployment options, please check the Deployment section of the documentation.\n", + "\n", + "
    \n", + "\n", + "Tip\n", + "\n", + "This tutorial is a Jupyter Notebook. There are two options to run it:\n", + "\n", + "- Use the Open in Colab button at the top of this page. This option allows you to run the notebook directly on Google Colab. Don't forget to change the runtime type to GPU for faster model training and inference.\n", + "- Download the .ipynb file by clicking on the View source link at the top of the page. This option allows you to download the notebook and run it on your local machine or on a Jupyter notebook tool of your choice.\n", + "
    " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, let's install our dependencies and import the necessary libraries:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install argilla\n", + "!pip install datasets" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import argilla as rg\n", + "from argilla._constants import DEFAULT_API_KEY\n", + "from sentence_transformers import SentenceTransformer" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to run this notebook we will need some credentials to push and load datasets from `Argilla` and `🤗 Hub`, let's set them in the following cell:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "parameters" + ] + }, + "outputs": [], + "source": [ + "# Argilla credentials\n", + "api_url = \"http://localhost:6900\" # \"https://.hf.space\"\n", + "api_key = DEFAULT_API_KEY # admin.apikey\n", + "# Huggingface credentials\n", + "hf_token = \"hf_...\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Log in to argilla:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rg.init(api_url=api_url, api_key=api_key)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Enable Telemetry\n", + "We gain valuable insights from how you interact with our tutorials. To improve ourselves in offering you the most suitable content, using the following lines of code will help us understand that this tutorial is serving you effectively. Though this is entirely anonymous, you can choose to skip this step if you prefer. For more info, please check out the Telemetry page.\n", + "\n", + "```python\n", + "from argilla.utils.telemetry import tutorial_running\n", + "\n", + "tutorial_running()\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pull the Dataset\n", + "\n", + "As we uploaded the dataset we created in the [previous tutorial](#previous_tutorial) to both Argilla and HuggingFace Hub, we can pull the dataset from either of them, however, we only allow filtering and querying on `RemoteFeedbackDataset`, hence we will only pull the dataset from Argilla.\n", + "\n", + "### From Argilla\n", + "\n", + "We can pull the dataset from Argilla by using the `from_argilla` method. " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "dataset_remote_with_metadata = rg.FeedbackDataset.from_argilla(\"end2end_textclassification_with_metadata\")\n", + "dataset_remote_with_vectors = rg.FeedbackDataset.from_argilla(\"end2end_textclassification_with_vectors\")\n", + "dataset_remote_with_suggestions_and_responses = rg.FeedbackDataset.from_argilla(\"end2end_textclassification_with_suggestions_and_responses\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### From HuggingFace Hub\n", + "\n", + "Not all sorting and filtering is supported with a local `FeedbackDataset`, pulled from HuggingFace Hub. Hence, we will only pull the dataset from Argilla." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
    \n", + "\n", + "Note \n", + "\n", + "The dataset pulled from HuggingFace Hub is an instance of `FeedbackDataset` whereas the dataset pulled from Argilla is an instance of `RemoteFeedbackDataset`. The difference between the two is that the former is a local one and the changes made on it stay locally. On the other hand, the latter is a remote one and the changes made on it are directly reflected on the dataset on the Argilla server, which can make your process faster.\n", + "\n", + "
    " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "RemoteFeedbackRecord(id=UUID('873a60bd-e371-434d-9d05-8c4cc97ff6cf'), client=, fields={'text': \"Wall St. Bears Claw Back Into the Black (Reuters) Reuters - Short-sellers, Wall Street's dwindling\\\\band of ultra-cynics, are seeing green again.\"}, metadata={'group': 'group-2', 'length': 144, 'length_std': 144.0}, vectors={}, responses=[], suggestions=(), external_id='record-0')" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Filter\n", + "\n", + "Filtering allow us to select a subset of the records in our dataset based on a condition. We can filter our dataset by using the `filter_by` method that return a FeedbackDataset with a subset of the records. We can filter by field by metadata property and by status.\n", + "\n", + "> Note: The records won’t be modified unless updates or deletions are specifically applied at record-level." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### By Fields Content\n", + "\n", + "We can filter our dataset by the content of the fields. We will only need to write in the search bar in the top left corner on top of the record card the content you want to filter by. In the image, you can see that we searched for the records with the 'blue' word (which appears highlighted), and two with this requirement were found." + ] + }, + { + "attachments": { + "filter_by_fields.PNG": { + "image/png": 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zTH4wP5Ij9+q5X5GDr4dG9oTNpRoMnj++8rC89uD/JRc/P2u3iPf/1x68Vj549RG7JZ80DHFzJzB4wuZO3OAJzB2dMeU0eK75hwm5fNneJYcCg+f999+3W9J18OBBb7HleuUbPP8oo3+2G0J69Z6r5ar/9IjvMNZs8MzKI//pKvmbuIGkOrdH1ptr/uNOx80RQgihGvXMM894po6+QNZq7qi0Dde23NXGNxr90pL30p6BPo/r3CIEBo8bf1qOd2wdBs+xXWog3CcvfVjZ5nNCXvqROf57/uiQyJSjM7FjrcHjmUTnY/ualCUxeJQzJ2RqdKv8IDB6PO6QHz+5X46XR4kck+e+Z7bf9owciZs/H74kP9Zzfj7pp1uQtr+cco8KWgSDRzvZukaRGjpBx1v/r9t0n+ucpeSTt1+U7b+arLIGj2+sPPta2GwxnJyWnXEz54tzcvzgdtk+edwe+6nMvWL+Phj8bTl5RMZ+OynHvft+Ku++vDV5zCd/kLGtz8r0yWBbejie3ToWC4d/zYjB4xrRU/pQ3v1T/YYywFKjL/Z5pevteGZEBnpMIQVmzJZd8pe/nPb5YFrefuF7stcb8fIVmXzjnHfo8afUjLleDr/n/RnSnBy+2xwbTOUKrmmNoYoOyqQXzmBE0LRM6j1u/568e97bUNalGWsm5TB49jwVm14V3D8499Sz/t8bH5Xg1EB/eXmtd41aR/BMb/lbz+S5dOGch/5ft9Uzgids7sQNnrTpWQ0zePRGavBMTSXXtYlLp2eNjIzYv2qXZ8b8h/vFdccTQzrCxi7AXLPB86psNOdd/X/8jfzNf4rzFfmS2ffVIdbiQQgh1Fjdc889nrmj/9aqlWzwPPM7fyhylNDCvTUbPCWZ/LmGV9d7uc+f0hOi5zbdFxhJgcETMpbKnJD9Pw++MHaH/GDzY/LMy5Ny7E/N+0n2JTN4QpwtHZcjoa9o3fbQS/6oqSC9nAtg23QL4r2agbMIBo+i/V7N9z/+8Y899P+6zXXsUqJTmHZu3SlT85XRRW58Y2Xyg+j2T35v0iNuyhjOvX9Atv5qWj7Sv88dlwMRk8aBd8x2E474PjWHtsr2qY/s3+5wfHT4Wdn6ylxsOpcNR9jQ+dMR73n3v/2RfHoueixAq6Ev9nm1qAaPi9uvl1dfDkbWfCS//5Fu/5Ls+RedchXFW9cmMG6CayZMk5jBExgvIROnrGD9nxwGz8svfGQ3WMUNnmCkj8vEee9RfypYDQaP6vKlCzK99f+VqZ/9P3LopzfI4cEbvW1FpeHLa/C4zJ2PP/64MVO0VPpJTf0Uut4wTfrVDTWC3n77bbuldnlmTDBKJybf4Pnnhhg8f/UP/yz3//B+J48cYAQPQgihxunpp5/2zB19YdR/9e9atJKnaNW7Bo+P2+DxTZu75eGnn0t8ht1nyk7dyjJ4lDNy4ncvyRNb7guNVrlN7vvNInwKvAEsh8FTQaev+SbPE4fPtoTBo2hnW0ftNMvIncDc2T+bZ3qSb6xMxQwYb+0es92NNVZO6yicMfnD6ei5ETKOia4PlBEO1xpCjhE7n8wekOef9sP41E79TPxxOfN56ByAFkFf7PNqUado9Q/Iu1MvlXnvvY/ks4hPUZnm9PKW4HPqcXb5I2TyGjxxIyasYP2fJjd4VJcufu4ZPEot5o5Kw+cyeMLmTk0GT9FFllV6UTV49GsgepO4dH0enZo1NDQkl4sMD0qRZ8ZctV72+CPFIpp64K/kqqs3+qN70gyeu6oZPFNy/39ImaKFEEIINVjBGjw7d+70/q5nDR4WWa5titbRHWoePCT7T1S2ualm8EQ5+6cpeeb7evzd8tys+5jlxGXwNJQzR+UlNchejq1XZAnS1k+zo/KMpk15OlyIM1PymO57yK6RtIwGjxJ0uF37lhS73s2Lb+dde8ZtrHw0tV22vpa1OLPhzLvy4tbnYl/uipFxzIevVTd4vHA4RhJ98cFkwuAJOPfpJ/LRsSMyvnOrbHeM/gFodvTFPq+WfJHlmGYH9R4dcqTaZJa8Bk9g4ujUrvgnrwLjpREGT8ZIob/s+5Z3jXoMHtWlLz7zjJ5apWGoNnonzeBRc6dUKrkNnlo+k6766KOP5IknnpDHHntM9uzZI6+//rocOHBAfvvb33ojd5566invX11ouV75Bs/VyXVwFl6Vf/4PV8nVd+4Rz/tJGDzm3LvMufHpXfa88CLLUz/8K7nqPzqmgZ16VR7ZOixvscgyQgihBigwc3QNnrBqNXmW4jPpLubm5rxRO4qO2nUdUyuLYvD8yE4Lspx98xlv/RxdODky0mbhqIw9+UzIoEg3eE787jl55pdbZSxm5Pjm0W2y0/vSVnOx6AbP2SPyjE5xu+3hpHl2/oS85MWliRsvbc/Kkad1RM9t/oie0LEn9vl54MfB4st5DZ4nj7TM18sK88m7Mv70dnnxaJGFhd3Gyqez+ytTsUKcWzgTmgL1ibz1W8dCxgsfylvTb8mHn4aOSXyq/SOZ/tVW2T8bjOjLCMf2STkem3b10fSzUYPnszNy6pPY6MAMEwigmdEX+yJa0s+kx/T569/z7rPHHBsZEvLZQfndz74nh0YP+mvu5DV45LQcfVD//pK8OhEevXFOZgf9xZcbYvDoGkEb9dyvyKsHKossX/qLCU8diyw3UhqGvNOzlPjonVSDZ+vwcfnpdm9ppML6/PPP5fDhw2UzRw0fXXdHp2XpyJ3XXnutISaPP0Xr7+Tv/vpv5J93vCUnzIP9eW6PbPwvV3tfvhr+oz3QYfCce/Gf5Wqz7Ut/e7/3efU9Ox6Sr//1V+WG66MGj5walX+8+iq5+r9slNGZE17knZgZ9e8R/hQ7QgghVKN0xI6aOM8++6zdEpVuD4/sySNtw7Utd7Xxi4Wuw6cv32Gmp6edx9ZCYw2eE/LSJg3jbfLjoefkuVeDz5cH6+foJ87HZFK/0KVTrexnzx8+aK+ZNYLHfonrljsfkucm/a98Tb6w1d/2fceolGVG02jjxo1enH3jG9/wphx98MEHzmPr4cSrD/ufLL+tRx5+0k552/GY/NhOYbstWDhZObFfHvbWPPqBbH1h0sThlOx/9iH/0/bhz9VXM3gCY0mvs+M5GXszSL8VgvcVqa0yfGBOPrG/6lbIWofHbax4Boy53s79lU+jnzs9J5O67XCwbo5vwGzf/rxM/fGMP8rm01Py1t7tsnXPH8ojtIJjpuetAXPujMxN7pStO6fko7JxkxKOcx95z7Vz/7vyiXfsOTnz3qSMmTowbN58emxctuqi0B/bZ9V7HHxWtu56K/HpeIBmR1/si0pH6ehULF1vR9H/Fx65E6iAwVP5utWXZG//gBzV6VwHHpVX/8Xf9vJua7TkNnjMJYOROrd9WV7+xZPy9tSz8rv+62XsR50xk6Yeg8fcJ/yZdLuG0J7b6/tMeiNVxOBxTc86deqU2+A59OYnckvPEXubxktH9qjJ8+qryWlSeRVMp5qdG5X1f3216CfTlav/er1smwk5fw6DR93At7Z+Xf73q/1zrvr3X5X79/05NkXL6tSrcv/ffql8fUWNoVcZvYMQQqgB0pdrnd6cJTV5vv/979u/qkvbcG3LXW38YtFaBo/hxJQ1bsy+8HSg8ydk8skf+2ZCwJ0/lidePR4aBZI9RevMzHPy8HfttT1uk56B5+Ro5KtAzcMjjzxSDuuLL77oPKYRlN6Mx4tBDZ/RI1KKf3HsxKQ8semO0LEah2Ny7EzomGoGj+Hs7Jg8FKyD9Msp5zGtypm39bPh/vozCUJmS5I0g8fw6Udy5MVh2V6+1lMyNv1hYsrTJ8cOyHPbK/d7as8R+cgbvZN1zLR8WP5ampIRjoXjMrXrqej134uvwfOpfDg9Jk/ZY9z3AGgNajF4GqoiBo/qwpz8/mftXrjLfLtdDr5wtDKqp4DBo/r0zQF5+U77WXTl3gE5rp8w9/7fGINHdeHUS3L4Z2vtwtBr5dDrH8mFos+/SNLnSDN4AgM/zeDR0TupBo/yX9f9rvA6PEWkHUE1ef70pz/ZLfXpnI7g+bNjQZ4qOpf3nHN6fUPxWyCEEEJLJm27tQ13te3gYOGMnHV9zvz8Wa/zFPlCV0HOeucb4p/7bjK0M3nvvfd6ayW69jccE+e54zY4ts441LRYsdO0FoNz/ovEucwFi8/5LxyZn2TPc0w657wXmipfBfu8vnsANAP6Yt+SunBO/M+qn7Nf2apfn39qrmfKfXw5nkXVH+zn2JvQ4AmP3lGy1t/JNHj++7b3ap6mlVcaAIQQQgg1Ttp2axvuatsB0tDOok6zd+0DAIDFpWUNnpbSaflwd5+8fNfa2ALR52T2F3/tpcEr+5Ifi1pKVTN41NyJGzzh9XcyDZ73PliQ//z11wp/Lh0hhBBCyyNts7Xt1jbc1bYDAABA84HBszT67I0+f6TO7X8lezZ9Rw4Nfkv2BtPCfvConFjSYUNJaTjC5o7L4Elbf0cNHp0dlWrwKPdvObboo3gQQggh1Bhpm61tt6tNBwAAgOYEg2fpFF2Dx3Dvt+TQCwfl40bNMatD9Rg8OnqnqsFzsvSZXP+N1+XNd/hcFEIIIdTM0rZa22xtu11tOgAAADQnGDxItegGj/LrPSe8r3FcvmzvihBCCKGmkrbR2lZrm+1qywEAAKB5weBBqloNnmD9nVwGj6LDve/Z/K69LUIIIYSaSdpGMzULAACgNcHgQapaDJ7wAsu5DR7lzgf+ID8anLO3RgghhFAzSNtmbaNdbTcAAAA0Py/1/K/y+QJfmL6Spemv+aAeg+fkyZP5DR5FO5D6KyHTtRBCCKHllbbF2iZj7gAAALQ2hwb+bzl5+Fe2hUdXojT9Dw38F6fBo+aOy+CJfyK9sMGj6BBwnefPwssIIYTQ8kjbYG2LmZYFAADQ+hx/dav87ufttpVHV6I0/d/fv2XpDR5FF3HUL3Xo51gXzl60QUIIIYTQYkrbXG17tQ1mQWUAAICVw8Sm1fLBq4/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+ } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![filter_by_fields.PNG](attachment:filter_by_fields.PNG)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### By Metadata Property" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The UI allow us to filter using the metadata properties and combine the needed filter. Below, you can see an example, where we filtered by our metadata `Annotation Group`, `Length of the text` and `Standard Deviation` properties, so that from the 1000 records, we only got 242. Note that if they were set to `visible_for_annotators=False`, it would only appear for users with the `admin` or `owner` role.\n", + "\n", + "> This can also be very useful to assign records to your team members in a common workspace. Please refer to the [metadata tutorial](./add-metadata-003.ipynb) and how to [assign records](./assign-records-002.ipynb) for more information." + ] + }, + { + "attachments": { + "filter-by-metadata.PNG": { + "image/png": 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+ } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![filter-by-metadata.PNG](attachment:filter-by-metadata.PNG)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we will make the same filtering but using the `filter_by` method provided in the Python SDK. Thus, we will will need to combine the three filters. In addition, each metadata is a different type, so we will need to use `TermsMetadataFilter` for the annotation group, `IntegerMetadataFilter` for the length of the text, and `FloatMetadataFilter` for the standard deviation. We will be using the following parameters:\n", + "\n", + "| | Description | TermsMetadataFilter | IntegerMetadataFilter | FloatMetadataFilter |\n", + "|-------|-------|-------|-------|-------|\n", + "| *name* | name of the metadata property | group | length | length_std |\n", + "| *ge* | values greater than or equal | no-required | 0 | 204 |\n", + "| *le* | values lower than or equal | no-required | 282 | 290 |\n", + "| *values* | values searched | group-1 and group-2 | no-required | no-required |" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> In the case of Integer and Float filters at least one of `ge` or `le` should be provided." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "242\n" + ] + } + ], + "source": [ + "filtered_records = dataset_remote_with_metadata.filter_by(\n", + " metadata_filters=[\n", + " rg.TermsMetadataFilter(\n", + " name=\"group\",\n", + " values=[\"group-1\", \"group-2\"]\n", + " ),\n", + " rg.IntegerMetadataFilter(\n", + " name=\"length\",\n", + " le=282\n", + " ),\n", + " rg.FloatMetadataFilter(\n", + " name=\"length_std\",\n", + " ge=204, \n", + " le=290\n", + " ), \n", + " ]\n", + ")\n", + "\n", + "print(len(filtered_records))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### By Status" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also filter by the status. The `response_status` can be of the following types:\n", + "\n", + "- `missing`, if the records have no responses, or `draft`, if they have them but they are not submitted yet. In both cases, in the UI will appear as `Pending`.\n", + "- `discarded`, if the responses were discarded.\n", + "- `submitted`, if the responses are submitted." + ] + }, + { + "attachments": { + "filter-by-status.PNG": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![filter-by-status.PNG](attachment:filter-by-status.PNG)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So, now we will use the UI to annotate and discard some samples to be used as examples. And we will start to filter." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* First, we want to check the submitted records so we can compute the time it is taking us to complete the annotation process. And we can see that we still have many work to do." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Submitted records: 10\n" + ] + } + ], + "source": [ + "filtered_dataset = dataset_remote_with_metadata.filter_by(response_status=[\"submitted\"])\n", + "print('Submitted records:', len(filtered_dataset))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Now, we want to check those that are pending, so we will filter by `missing` and `draft` status. And we can see that we have 973 records pending to be annotated." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pending records: 973\n" + ] + } + ], + "source": [ + "filtered_dataset = dataset_remote_with_metadata.filter_by(response_status=[\"missing\", \"draft\"])\n", + "print('Pending records:', len(filtered_dataset))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Finally, we will check how many records were discarded from the filtered records using metadata." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Discarded records: 7\n" + ] + } + ], + "source": [ + "filtered_dataset = dataset_remote_with_metadata.filter_by(\n", + " metadata_filters=[\n", + " rg.TermsMetadataFilter(\n", + " name=\"group\",\n", + " values=[\"group-1\", \"group-2\"]\n", + " ),\n", + " rg.IntegerMetadataFilter(\n", + " name=\"length\",\n", + " le=282\n", + " ),\n", + " rg.FloatMetadataFilter(\n", + " name=\"length_std\",\n", + " ge=204, \n", + " le=290\n", + " ),\n", + " ],\n", + " response_status=[\"discarded\"]\n", + ")\n", + "\n", + "print('Discarded records:', len(filtered_dataset))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### By Suggestions and Responses" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Within the UI filters, you can filter records according to the value of responses given by **the current user**. The type of the questions should be `LabelQuestion`, `MultiLabelQuestion` or `RatingQuestion`. If you prefer to filter records based on suggestions it is possible to gilter by suggestion score, value and agent. The option in Python SDK will be available soon." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Sort" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also order the records according to one or several attributes. In the UI, this can be done easily using the `Sort` menu. So, we will focus on how to do it in the Python SDK using `sort_by`. This methos will allow us to sort by the last updated (updated_at) or any metadata properties (metadata.my-metadata-name) in ascending or descending order." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* First, we will sort the records to know the record that was the lasted updated and by groups." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "from argilla import SortBy\n", + "\n", + "sorted_records = dataset_remote_with_suggestions_and_responses.sort_by(\n", + " [\n", + " SortBy(field=\"updated_at\", order=\"desc\"),\n", + " SortBy(field=\"metadata.group\", order=\"asc\")\n", + " ]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Then, we want to order the filtered records in the same way. So, we can combine `filter_by` and `sort_by`." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "filtered_dataset = dataset_remote_with_suggestions_and_responses.filter_by(\n", + " response_status=[\"discarded\"]\n", + ").sort_by(\n", + " [\n", + " SortBy(field=\"updated_at\", order=\"desc\"),\n", + " SortBy(field=\"metadata.group\", order=\"asc\")\n", + " ]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Semantic Search" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this section, we will see how to use the semantic search feature in Python SDK. To find similar records, we will use the `find_similar_records` method. We need to feed this method with the vector name we want to use for the search. For the particular vector, we have two options: we can either use a record by the `record` parameter or we can define a new vector by the `vector` parameter. You can limit the number of records you want to get by the `max_results` parameter. \n", + "\n", + "> It is also possible to use the semantic search in the UI. You will just need to click the `Find Similar` button on the right top of the record to see similar records. You have the opportunity to limit the number of results as well as define your research as the most or least similar records. You can also see the similarity score of each record to the record you are searching for." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "similar_records = dataset_remote_with_vectors.find_similar_records(\n", + " vector_name=\"sentence_embedding\",\n", + " record=dataset_remote_with_vectors[0],\n", + " max_results=5\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `similar_records` object we created contains the 5 top records similar to the first record in the dataset. Let us see what these records are." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[\"Wall St. Bears Claw Back Into the Black NEW YORK (Reuters) - Short-sellers, Wall Street's dwindling band of ultra-cynics, are seeing green again.\",\n", + " \"Strategies for a Sideways Market (Reuters) Reuters - The bulls and the bears are in this\\\\together, scratching their heads and wondering what's going to\\\\happen next.\",\n", + " \"Strategies for a Sideways Market WASHINGTON (Reuters) - The bulls and the bears are in this together, scratching their heads and wondering what's going to happen next.\",\n", + " 'Wall Street to Open Little Changed NEW YORK (Reuters) - Wall Street is seen opening little changed on Monday as crude prices remain high, but insurers may dip on worries about their potential liabilities after a hurricane struck Florida on Friday.',\n", + " \"Chrysler's Bling King After a tough year, Detroit's troubled carmaker is back -- thanks to a maverick designer and a car that is dazzling the hip-hop crowd\"]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "[record[0].fields[\"text\"] for record in similar_records]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On the other hand, we can feed the `find_similar_records` method with a vector we define. Let us first create an embedding with the sentence transformers model and then find the records similar to this vector." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "text = \"Baseball: Smoltz sizzles as Braves force deciding game HOUSTON, United States : JD Drew singled to drive in a run in the top of the ninth inning and lift the Atlanta Braves to a 6-5 win over Houston and force the Astros into a fifth and deciding game.\"\n", + "\n", + "model = SentenceTransformer(\"TaylorAI/bge-micro\")\n", + "\n", + "text_vector = model.encode(text).tolist()\n", + "\n", + "similar_records = dataset_remote_with_vectors.find_similar_records(\n", + " vector_name=\"sentence_embedding\",\n", + " value=text_vector,\n", + " max_results=5\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see the ids of the records that are similar to the vector we defined." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['record-746', 'record-459', 'record-642', 'record-716', 'record-643']" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "[record[0].external_id for record in similar_records]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this tutorial, we learned how to filter and query our records. We delved into the specifics of filtering based on field content, metadata attributes, various statuses and suggestions and responses. Additionally, we explored methods to order our records, whether by the most recent updates or by any metadata characteristic. Finally, we saw how to use the semantic search feature." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "argilla-markdown", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/_source/tutorials_and_integrations/tutorials/feedback/end2end_examples/train-model-006.ipynb b/docs/_source/tutorials_and_integrations/tutorials/feedback/end2end_examples/train-model-006.ipynb new file mode 100644 index 0000000000..3283b557ae --- /dev/null +++ b/docs/_source/tutorials_and_integrations/tutorials/feedback/end2end_examples/train-model-006.ipynb @@ -0,0 +1,462 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Train a Model with `ArgillaTrainer`\n", + "\n", + "In this part of our end-to-end series, we will be training our model with ArgillaTrainer. You can refer to previous tutorials such as [creating the dataset](./create-dataset-001.ipynb) or [adding responses and suggestions](./add-suggestions-and-responses-005.ipynb). Feel free to check out the [practical guides](../../../../practical_guides/practical_guides.md) page for more in-depth information.\n", + "\n", + "Fine-tuning is a process of training a model with a dataset that is similar to the one it was trained on. This way, the model can learn to perform better on a specific task. In our case, we will be fine-tuning a model with the dataset we created in the previous tutorial. Note that Argilla trainer is a wrapper around many different frameworks and models. Please have a look at the [ArgillaTrainer](../../../../practical_guides/fine_tune.md#the-argillatrainer) page for more information on the NLP tasks and models that are supported. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![workflow](../../../../_static/tutorials/end2end/base/workflow_train_models.svg)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Table of Contents\n", + "\n", + "1. [Pull the Dataset](#Pull-the-Dataset)\n", + " 1. [From Argilla](#From-Argilla)\n", + " 2. [From HuggingFace Hub](#From-HuggingFace-Hub)\n", + "2. [Train with Default ArgillaTrainer](#Train-with-Default-ArgillaTrainer-Settings)\n", + "3. [Train with Custom Formatting Function](#Train-with-Custom-Formatting-Function)\n", + "4. [Push the Model to Huggingface Hub](#Push-the-Model-to-HuggingFace-Hub)\n", + "5. [Conclusion](#Conclusion)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Running Argilla\n", + "\n", + "For this tutorial, you will need to have an Argilla server running. There are two main options for deploying and running Argilla:\n", + "\n", + "**Deploy Argilla on Hugging Face Spaces:** If you want to run tutorials with external notebooks (e.g., Google Colab) and you have an account on Hugging Face, you can deploy Argilla on Spaces with a few clicks:\n", + "\n", + "[![deploy on spaces](https://huggingface.co/datasets/huggingface/badges/raw/main/deploy-to-spaces-lg.svg)](https://huggingface.co/new-space?template=argilla/argilla-template-space)\n", + "\n", + "For details about configuring your deployment, check the [official Hugging Face Hub guide](https://huggingface.co/docs/hub/spaces-sdks-docker-argilla).\n", + "\n", + "**Launch Argilla using Argilla's quickstart Docker image**: This is the recommended option if you want [Argilla running on your local machine](../../../../getting_started/quickstart.md). Note that this option will only let you run the tutorial locally and not with an external notebook service.\n", + "\n", + "For more information on deployment options, please check the Deployment section of the documentation.\n", + "\n", + "
    \n", + "\n", + "Tip\n", + "\n", + "This tutorial is a Jupyter Notebook. There are two options to run it:\n", + "\n", + "- Use the Open in Colab button at the top of this page. This option allows you to run the notebook directly on Google Colab. Don't forget to change the runtime type to GPU for faster model training and inference.\n", + "- Download the .ipynb file by clicking on the View source link at the top of the page. This option allows you to download the notebook and run it on your local machine or on a Jupyter notebook tool of your choice.\n", + "
    " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, let's install our dependencies and import the necessary libraries:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install argilla\n", + "!pip install datasets transformers" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import argilla as rg\n", + "from argilla.feedback import FeedbackDataset, TrainingTask, ArgillaTrainer\n", + "from datasets import load_dataset\n", + "from argilla._constants import DEFAULT_API_KEY" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to run this notebook we will need some credentials to push and load datasets from `Argilla` and `🤗 Hub`, let's set them in the following cell:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "parameters" + ] + }, + "outputs": [], + "source": [ + "# Argilla credentials\n", + "api_url = \"http://localhost:6900\" # \"https://.hf.space\"\n", + "api_key = DEFAULT_API_KEY # admin.apikey\n", + "# Huggingface credentials\n", + "hf_token = \"hf_...\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Log in to Argilla:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rg.init(api_url=api_url, api_key=api_key)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Enable Telemetry\n", + "We gain valuable insights from how you interact with our tutorials. To improve ourselves in offering you the most suitable content, using the following lines of code will help us understand that this tutorial is serving you effectively. Though this is entirely anonymous, you can choose to skip this step if you prefer. For more info, please check out the Telemetry page.\n", + "\n", + "```python\n", + "from argilla.utils.telemetry import tutorial_running\n", + "\n", + "tutorial_running()\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pull the Dataset\n", + "\n", + "As we uploaded the dataset that we created in the [previous tutorial](./create-dataset-001.ipynb) to both Argilla and HuggingFace Hub, we can pull the dataset from either of them. Let us see how we can pull the dataset from both.\n", + "\n", + "### From Argilla\n", + "\n", + "We can pull the dataset from Argilla by using the `from_argilla` method. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = rg.FeedbackDataset.from_argilla(\"end2end_textclassification_with_suggestions_and_responses\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### From HuggingFace Hub\n", + "\n", + "We can also pull the dataset from HuggingFace Hub. Similarly, we can use the `from_huggingface` method to pull the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = rg.FeedbackDataset.from_huggingface(\"argilla/end2end_textclassification_with_suggestions_and_responses\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
    \n", + "\n", + "Note \n", + "\n", + "The dataset pulled from HuggingFace Hub is an instance of `FeedbackDataset` whereas the dataset pulled from Argilla is an instance of `RemoteFeedbackDataset`. The difference between the two is that the former is a local one and the changes made on it stay locally. On the other hand, the latter is a remote one and the changes made on it are directly reflected on the dataset on the Argilla server, which can make your process faster.\n", + "\n", + "
    " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us briefly examine what our dataset looks like. It is a dataset that consists of data items with the field `text` that is yet to be annotated." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'text': \"Wall St. Bears Claw Back Into the Black (Reuters) Reuters - Short-sellers, Wall Street's dwindling\\\\band of ultra-cynics, are seeing green again.\"}" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dataset[0].fields" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Train with Default ArgillaTrainer Settings\n", + "\n", + "As we previously mentioned, you can employ `ArgillaTrainer` for most of your favorite NLP tasks and libraries. What it does is simplify the training process by providing a unified interface for training and fine-tuning models. Let us first see how we can train our model with the default settings.\n", + "\n", + "`TrainingTask` is the class that is responsible for how to process the data for training. To make things even easier, it offers task-specific methods that you can use for your particular task at hand. For this tutorial, we will be using the `for_text_classification` method, which will prepare a default data processing function for us. You can see a list of all the available task-specific methods in the [documentation](../../../../practical_guides/fine_tune.md#the-trainingtask)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "task = TrainingTask.for_text_classification(\n", + " text=dataset.field_by_name(\"text\"),\n", + " label=dataset.question_by_name(\"label\"),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And, we need to define an `ArgillaTrainer` for our task that will be used for training. You can define the framework you want to work with in the `framework` parameter. For this tutorial, we will be using the `en_core_web_sm` model from the `spacy` framework. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "trainer = ArgillaTrainer(\n", + " dataset=dataset,\n", + " task=task,\n", + " framework=\"spacy\",\n", + " train_size=0.8,\n", + " model=\"en_core_web_sm\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us customize the training process a bit more with the `update_config` method. We specify a model we want to use from HuggingFace. We also specify the number of epochs and the learning rate. You can have a look at the comprehensive list of [training configs](../../../../practical_guides/fine_tune.md#training-configs) for more customization options." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "trainer.update_config(\n", + " max_steps=1\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are already good to go with the default settings. We only need to call the `train` method to train our model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "trainer.train(output_dir=\"textcat_model_spacy\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Train with Custom Formatting Function\n", + "\n", + "The default settings might not be the best for your tasks sometimes. In those cases, you might need to customize the data processing function. You can do that by passing a custom formatting function to the `for_text_classification` method. Let us first define a custom formatting function and then train our model with it.\n", + "\n", + "It is important to note that the custom formatting function should return an object that will be accepted by the model. In our case, we will be returning a tuple of texts and labels." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def formatting_func(sample):\n", + " text = sample[\"text\"]\n", + " label = sample[\"label\"][0][\"value\"]\n", + " return(text, label)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we can feed the `TrainingTasks` with our custom formatting function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "task = TrainingTask.for_text_classification(formatting_func=formatting_func)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this example, we can use the `spacy` framework." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "trainer = ArgillaTrainer(\n", + " dataset=dataset,\n", + " task=task,\n", + " framework=\"spacy\",\n", + " train_size=0.8,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us customize the training process a bit more with the `update_config` method. We specify a model we want to use from HuggingFace. We also specify the number of epochs and the learning rate. You can have a look at the comprehensive list of [training configs](../../../../practical_guides/fine_tune.md#training-configs) for more customization options." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "trainer.update_config(\n", + " max_steps=1\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Again, we just need to call the `train` method to train our model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "trainer.train(output_dir=\"textcat_model_spacy\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Push the Model to HuggingFace Hub\n", + "\n", + "Now that we completed the training and we have a model that can be used for inference, we can push it to HuggingFace Hub. Argilla again offers quite a simple way to accomplish this with just a single line of code.\n", + "\n", + "First, to be able to upload your model to the Hub, you must be logged in to the Hub. The following cell will log us with our previous token.\n", + "\n", + "If we don't have one already, we can obtain it from [here](https://huggingface.co/docs/hub/security-tokens) (remember to set the write access)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from huggingface_hub import login\n", + "\n", + "login(token=hf_token)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We only need to call the `push_to_huggingface` method to push the model to HuggingFace Hub. Do not forget to create a model card as well, which will make the model more understandable for the users." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "trainer.push_to_huggingface(\"textcat_model_spacy\", generate_card=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "In this tutorial, we have seen how to train a model with ArgillaTrainer. We have also customized the training process with custom formatting functions and training configs. After training, we have pushed our model to HuggingFace Hub with the `push_to_huggingface` method. You can refer to the [metrics](./use-metrics-007.ipynb) tutorial to see how you can evaluate your model." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/_source/tutorials_and_integrations/tutorials/feedback/end2end_examples/use-metrics-007.ipynb b/docs/_source/tutorials_and_integrations/tutorials/feedback/end2end_examples/use-metrics-007.ipynb new file mode 100644 index 0000000000..05550ca33d --- /dev/null +++ b/docs/_source/tutorials_and_integrations/tutorials/feedback/end2end_examples/use-metrics-007.ipynb @@ -0,0 +1,57 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Use Metrics" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This tutorial is still WIP." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import argilla as rg\n", + "from argilla._constants import DEFAULT_API_KEY" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "parameters" + ] + }, + "outputs": [], + "source": [ + "# Argilla credentials\n", + "api_url = \"http://localhost:6900\" # \"https://.hf.space\"\n", + "api_key = DEFAULT_API_KEY # admin.apikey\n", + "# Huggingface credentials\n", + "hf_token = \"hf_...\"" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/_source/tutorials_and_integrations/tutorials/tutorials.md b/docs/_source/tutorials_and_integrations/tutorials/tutorials.md index 60d21414aa..5e49b24a51 100644 --- a/docs/_source/tutorials_and_integrations/tutorials/tutorials.md +++ b/docs/_source/tutorials_and_integrations/tutorials/tutorials.md @@ -1,11 +1,66 @@ # Tutorials +Whether you’re a beginner or an experienced user, these tutorials will walk you through our features and functionalities making it easy for you to understand and implement them. + **Feedback Dataset** ```{include} /_common/feedback_dataset.md ``` -Here you can find end-to-end examples to help you get started with curanting datasets and collecting feedback to fine-tune LLMs and other language models. +*Beginner* + +Are you new to Argilla and do you want to walk through low-key notebooks to reproduce specific features, these tutorials are perfect for you. They can be followed step by step but you can also work through individual examples with an Argilla-compatible dataset we have already prepared for you and which can be downloaded from the Hugging Face hub. + +![workflow](/_static/tutorials/end2end/base/workflow.svg) + +````{grid} 1 1 3 3 +:class-container: tuto-section-2 +```{grid-item-card} Configuring Users and Workspaces +:link: feedback/end2end_examples/configure-users-and-workspaces-000.html + +Learn how to configure `Users` and `Workspaces`. +``` +```{grid-item-card} Creating a FeedbackDataset +:link: feedback/end2end_examples/create-dataset-001.html + +Learn how to configure a `FeedbackDataset` and add `FeedbackRecords` to it. +``` +```{grid-item-card} Assign records to your team +:link: feedback/end2end_examples/assign-records-002.html + +Learn how to easily assign records to your team. +``` +```{grid-item-card} Adding Metadata to a FeedbackDataset +:link: feedback/end2end_examples/add-metadata-003.html + +Learn how to add metadata properties to a `FeedbackDataset`. +``` +```{grid-item-card} Adding Vectors to a FeedbackDataset +:link: feedback/end2end_examples/add-vectors-004.html + +Learn how to add vectors and vector settings to a `FeedbackDataset`. +``` +```{grid-item-card} Adding Responses and Suggestions to a FeedbackDataset +:link: feedback/end2end_examples/add-suggestions-and-responses-005.html + +Learn how to add suggestions and responses to a `FeedbackDataset`. +``` +```{grid-item-card} Filter and Query your FeedbackDataset +:link: feedback/end2end_examples/filter-and-query-008.html + +Learn how to filter and query your `FeedbackDataset`. +``` +```{grid-item-card} Train Your Model with ArgillaTrainer +:link: feedback/end2end_examples/train-model-006.html + +Learn how to train your model with `ArgillaTrainer`. +``` +```` + + +*Advanced* + +Here you can find more advanced applied examples to help you get started with curating datasets and collecting feedback to fine-tune LLMs and other language models. ````{grid} 1 1 3 3 :class-container: tuto-section-2 @@ -133,4 +188,4 @@ other_datasets/label_errors_cleanlab other_datasets/weak_supervision_ner other_datasets/weak_supervision_text_classification_semantic_search other_datasets/labelling-tokenclassification-using-spacy-llm -``` --> \ No newline at end of file +``` --> diff --git a/scripts/end2end_examples.py b/scripts/end2end_examples.py new file mode 100644 index 0000000000..7a8e63997f --- /dev/null +++ b/scripts/end2end_examples.py @@ -0,0 +1,133 @@ +# Copyright 2021-present, the Recognai S.L. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +To run this script you need to install papermill first: + +$ pip install papermill + +Then run the script from the argilla repo root: + +$ cd argilla-repo +$ python scripts/end2end_examples.py --help +""" + +import os +import tempfile +from dataclasses import dataclass, field +from pathlib import Path +from typing import Dict, Optional + +import papermill +import typer +from argilla._constants import DEFAULT_API_KEY + + +@dataclass +class ExampleNotebook: + sort_index: int = field(init=False) + src_filename: Path + dst_filename: Path + parameters: Dict = field(default_factory=dict) + + def __post_init__(self): + self.src_filename = Path(self.src_filename) + assert self.src_filename.exists(), f"File {self.src_filename} does not exist" + self.sort_index = int(self.src_filename.stem.split("-")[-1]) + self.dst_filename = Path(self.dst_filename) + dst_folder = self.dst_filename.parent + if not dst_folder.exists(): + dst_folder.mkdir(exist_ok=True) + + def run(self): + try: + nb = papermill.execute_notebook( + str(self.src_filename), + str(self.dst_filename), + parameters=self.parameters, + kernel_name="python3", + ) + for cell in nb["cells"]: + if cell["cell_type"] == "code": + if not cell["metadata"]["papermill"]["status"] == "completed": + raise Exception( + f"Notebook {self.src_filename} failed:\n" + "Code cell failed to execute:\n" + f"{cell['source']}\n" + "Output:\n" + f"{cell['outputs']}" + ) + print(f"✅ {self.src_filename}") + except Exception as e: + print(f"❌ {self.src_filename}") + raise e from None + + def clean(self): + if self.dst_filename.exists(): + self.dst_filename.unlink() + print(f"Removed output notebook: {self.dst_filename.stem}") + + +def get_huggingface_token() -> Optional[str]: + if token := os.environ.get("HF_HUB_ACCESS_TOKEN"): + return token + + from huggingface_hub import HfFolder + + if token := HfFolder.get_token(): + return token + + raise ValueError("No token found. Please set HF_HUB_ACCESS_TOKEN environment variable.") + + +def main( + api_url: Optional[str] = "http://localhost:6900", + api_key: Optional[str] = DEFAULT_API_KEY, + hf_token: Optional[str] = None, + examples_folder: Optional[Path] = "docs/_source/tutorials_and_integrations/tutorials/feedback/end2end_examples", +) -> None: + """ + Run the end2end example notebooks. If no arguments are passed, it + will try to get the api_key and the hf_token from the environment variables. + """ + if not hf_token: + hf_token = get_huggingface_token() + + notebook_parameters = { + "hf_token": hf_token, + "api_url": api_url, + "api_key": api_key, + } + + # Name of the output notebook that will be removed after running the examples + output_notebook = "output_notebook.ipynb" + + with tempfile.TemporaryDirectory() as tmpdir: + examples = [] + for filename in examples_folder.glob("*.ipynb"): + examples.append( + ExampleNotebook( + src_filename=filename, + dst_filename=Path(tmpdir) / output_notebook, + parameters=notebook_parameters, + ) + ) + examples = sorted(examples, key=lambda x: x.sort_index) + + for example in examples: + example.run() + + +if __name__ == "__main__": + typer.run(main) diff --git a/src/argilla/client/feedback/utils/assignment.py b/src/argilla/client/feedback/utils/assignment.py index c8ae428c49..40ed72ba51 100644 --- a/src/argilla/client/feedback/utils/assignment.py +++ b/src/argilla/client/feedback/utils/assignment.py @@ -89,7 +89,7 @@ def assign_records_to_groups( if len(records) < len(groups.keys()): warnings.warn( - f"The number of groups is higher than the number of records. Some users will not be assigned any records.", + "The number of groups is higher than the number of records. Some users will not be assigned any records.", UserWarning, ) @@ -147,7 +147,7 @@ def assign_records_to_individuals( if len(records) < len(users): warnings.warn( - f"The number of users is higher than the number of records. Some users will not be assigned any records.", + "The number of users is higher than the number of records. Some users will not be assigned any records.", UserWarning, )