docs: improve embedding page (#762)

* docs: improve embedding page

* chore: add changelog

* docs: update wording

Co-authored-by: George Mastrapas <[email protected]>

* test: replace ecommerce model from hf

---------

Co-authored-by: George Mastrapas <[email protected]>

CHANGELOG.md

@@ -20,6 +20,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 - Fix huggingface link of jina embeddings. ([#761](https://github.com/jina-ai/finetuner/pull/761))
 
+- Remove redundant text in jina embedding page. ([#762](https://github.com/jina-ai/finetuner/pull/762))
+
 
 ## [0.8.0] - 2023-07-13
 

docs/get-started/pretrained.md

@@ -3,12 +3,12 @@
 
 Starting with Finetuner 0.8.0,
 we have introduced a suite of pre-trained text embedding models licensed under Apache 2.0.
-The model have a range of useThese models have a variety of use cases, including information retrieval, semantic textual similarity, text reranking, and more.
+These models have a variety of use cases, including information retrieval, semantic textual similarity, text reranking, and more.
 The suite consists of the following models:
 
-- `jina-embedding-s-en-v1` **[Huggingface](https://huggingface.co/jinaai/jina-embedding-s-en-v1)**: This is a compact model with just 35 million parameters, that performs lightning-fast inference while delivering impressive performance.
-- `jina-embedding-b-en-v1` **[Huggingface](https://huggingface.co/jinaai/jina-embedding-b-en-v1)**: This model has a size of 110 million parameters, performs fast inference and delivers better performance than our smaller model.
-- `jina-embedding-l-en-v1` **[Huggingface](https://huggingface.co/jinaai/jina-embedding-l-en-v1)**: This is a relatively large model with a size of 330 million parameters, that performs single-gpu inference and delivers better performance than our other model.
+- `jina-embedding-s-en-v1` [**[Huggingface](https://huggingface.co/jinaai/jina-embedding-s-en-v1)**]: This is a compact model with just 35 million parameters, that performs lightning-fast inference while delivering impressive performance.
+- `jina-embedding-b-en-v1` [**[Huggingface](https://huggingface.co/jinaai/jina-embedding-b-en-v1)**]: This model has a size of 110 million parameters, performs fast inference and delivers better performance than our smaller model.
+- `jina-embedding-l-en-v1` [**[Huggingface](https://huggingface.co/jinaai/jina-embedding-l-en-v1)**]: This is a relatively large model with a size of 330 million parameters, that performs single-gpu inference and delivers better performance than the other models.
 
 ## Usage
 

tests/integration/test_hf_models.py

@@ -1,15 +1,14 @@
 import pytest
 
-from finetuner import get_model
+from finetuner import build_model
 
 
 @pytest.mark.parametrize(
     'model',
     [
-        'https://huggingface.co/jinaai/ecommerce-sbert-model',
-        'jinaai/ecommerce-sbert-model',
+        'jinaai/jina-embedding-s-en-v1',
     ],
 )
-def test_get_model(model):
-    model = get_model(model)
+def test_build_model(model):
+    model = build_model(name=model)
     assert model