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| { | ||
| "word_embedding_dimension": 768, | ||
| "pooling_mode_cls_token": false, | ||
| "pooling_mode_mean_tokens": true, | ||
| "pooling_mode_max_tokens": false, | ||
| "pooling_mode_mean_sqrt_len_tokens": false | ||
| } |
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| --- | ||
| language: en | ||
| license: apache-2.0 | ||
| library_name: sentence-transformers | ||
| tags: | ||
| - sentence-transformers | ||
| - feature-extraction | ||
| - sentence-similarity | ||
| - transformers | ||
| datasets: | ||
| - s2orc | ||
| - flax-sentence-embeddings/stackexchange_xml | ||
| - ms_marco | ||
| - gooaq | ||
| - yahoo_answers_topics | ||
| - code_search_net | ||
| - search_qa | ||
| - eli5 | ||
| - snli | ||
| - multi_nli | ||
| - wikihow | ||
| - natural_questions | ||
| - trivia_qa | ||
| - embedding-data/sentence-compression | ||
| - embedding-data/flickr30k-captions | ||
| - embedding-data/altlex | ||
| - embedding-data/simple-wiki | ||
| - embedding-data/QQP | ||
| - embedding-data/SPECTER | ||
| - embedding-data/PAQ_pairs | ||
| - embedding-data/WikiAnswers | ||
| pipeline_tag: sentence-similarity | ||
| --- | ||
|
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||
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| # all-mpnet-base-v2 | ||
| This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. | ||
|
|
||
| ## Usage (Sentence-Transformers) | ||
| Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: | ||
|
|
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| ``` | ||
| pip install -U sentence-transformers | ||
| ``` | ||
|
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| Then you can use the model like this: | ||
| ```python | ||
| from sentence_transformers import SentenceTransformer | ||
| sentences = ["This is an example sentence", "Each sentence is converted"] | ||
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| model = SentenceTransformer('sentence-transformers/all-mpnet-base-v2') | ||
| embeddings = model.encode(sentences) | ||
| print(embeddings) | ||
| ``` | ||
|
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||
| ## Usage (HuggingFace Transformers) | ||
| Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. | ||
|
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| ```python | ||
| from transformers import AutoTokenizer, AutoModel | ||
| import torch | ||
| import torch.nn.functional as F | ||
|
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| #Mean Pooling - Take attention mask into account for correct averaging | ||
| def mean_pooling(model_output, attention_mask): | ||
| token_embeddings = model_output[0] #First element of model_output contains all token embeddings | ||
| input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() | ||
| return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) | ||
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|
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| # Sentences we want sentence embeddings for | ||
| sentences = ['This is an example sentence', 'Each sentence is converted'] | ||
|
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| # Load model from HuggingFace Hub | ||
| tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-mpnet-base-v2') | ||
| model = AutoModel.from_pretrained('sentence-transformers/all-mpnet-base-v2') | ||
|
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| # Tokenize sentences | ||
| encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') | ||
|
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| # Compute token embeddings | ||
| with torch.no_grad(): | ||
| model_output = model(**encoded_input) | ||
|
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| # Perform pooling | ||
| sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) | ||
|
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| # Normalize embeddings | ||
| sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1) | ||
|
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| print("Sentence embeddings:") | ||
| print(sentence_embeddings) | ||
| ``` | ||
|
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| ## Evaluation Results | ||
|
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| For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/all-mpnet-base-v2) | ||
|
|
||
| ------ | ||
|
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| ## Background | ||
|
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| The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised | ||
| contrastive learning objective. We used the pretrained [`microsoft/mpnet-base`](https://huggingface.co/microsoft/mpnet-base) model and fine-tuned in on a | ||
| 1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset. | ||
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| We developped this model during the | ||
| [Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), | ||
| organized by Hugging Face. We developped this model as part of the project: | ||
| [Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks. | ||
|
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| ## Intended uses | ||
|
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| Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures | ||
| the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks. | ||
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| By default, input text longer than 384 word pieces is truncated. | ||
|
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|
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| ## Training procedure | ||
|
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| ### Pre-training | ||
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| We use the pretrained [`microsoft/mpnet-base`](https://huggingface.co/microsoft/mpnet-base) model. Please refer to the model card for more detailed information about the pre-training procedure. | ||
|
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| ### Fine-tuning | ||
|
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| We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch. | ||
| We then apply the cross entropy loss by comparing with true pairs. | ||
|
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| #### Hyper parameters | ||
|
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| We trained ou model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core). | ||
| We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with | ||
| a 2e-5 learning rate. The full training script is accessible in this current repository: `train_script.py`. | ||
|
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| #### Training data | ||
|
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| We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences. | ||
| We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file. | ||
|
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|
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||
| | Dataset | Paper | Number of training tuples | | ||
| |--------------------------------------------------------|:----------------------------------------:|:--------------------------:| | ||
| | [Reddit comments (2015-2018)](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) | [paper](https://arxiv.org/abs/1904.06472) | 726,484,430 | | ||
| | [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Abstracts) | [paper](https://aclanthology.org/2020.acl-main.447/) | 116,288,806 | | ||
| | [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs | [paper](https://doi.org/10.1145/2623330.2623677) | 77,427,422 | | ||
| | [PAQ](https://github.com/facebookresearch/PAQ) (Question, Answer) pairs | [paper](https://arxiv.org/abs/2102.07033) | 64,371,441 | | ||
| | [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Titles) | [paper](https://aclanthology.org/2020.acl-main.447/) | 52,603,982 | | ||
| | [S2ORC](https://github.com/allenai/s2orc) (Title, Abstract) | [paper](https://aclanthology.org/2020.acl-main.447/) | 41,769,185 | | ||
| | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs | - | 25,316,456 | | ||
| | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title+Body, Answer) pairs | - | 21,396,559 | | ||
| | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs | - | 21,396,559 | | ||
| | [MS MARCO](https://microsoft.github.io/msmarco/) triplets | [paper](https://doi.org/10.1145/3404835.3462804) | 9,144,553 | | ||
| | [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) | [paper](https://arxiv.org/pdf/2104.08727.pdf) | 3,012,496 | | ||
| | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 1,198,260 | | ||
| | [Code Search](https://huggingface.co/datasets/code_search_net) | - | 1,151,414 | | ||
| | [COCO](https://cocodataset.org/#home) Image captions | [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) | 828,395| | ||
| | [SPECTER](https://github.com/allenai/specter) citation triplets | [paper](https://doi.org/10.18653/v1/2020.acl-main.207) | 684,100 | | ||
| | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 681,164 | | ||
| | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 659,896 | | ||
| | [SearchQA](https://huggingface.co/datasets/search_qa) | [paper](https://arxiv.org/abs/1704.05179) | 582,261 | | ||
| | [Eli5](https://huggingface.co/datasets/eli5) | [paper](https://doi.org/10.18653/v1/p19-1346) | 325,475 | | ||
| | [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) | 317,695 | | ||
| | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles) | | 304,525 | | ||
| | AllNLI ([SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) | [paper SNLI](https://doi.org/10.18653/v1/d15-1075), [paper MultiNLI](https://doi.org/10.18653/v1/n18-1101) | 277,230 | | ||
| | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (bodies) | | 250,519 | | ||
| | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles+bodies) | | 250,460 | | ||
| | [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) | [paper](https://www.aclweb.org/anthology/D13-1155/) | 180,000 | | ||
| | [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) | [paper](https://arxiv.org/abs/1810.09305) | 128,542 | | ||
| | [Altlex](https://github.com/chridey/altlex/) | [paper](https://aclanthology.org/P16-1135.pdf) | 112,696 | | ||
| | [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | - | 103,663 | | ||
| | [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) | [paper](https://www.aclweb.org/anthology/P11-2117/) | 102,225 | | ||
| | [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) | 100,231 | | ||
| | [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | [paper](https://aclanthology.org/P18-2124.pdf) | 87,599 | | ||
| | [TriviaQA](https://huggingface.co/datasets/trivia_qa) | - | 73,346 | | ||
| | **Total** | | **1,170,060,424** | |
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| { | ||
| "_name_or_path": "microsoft/mpnet-base", | ||
| "architectures": [ | ||
| "MPNetForMaskedLM" | ||
| ], | ||
| "attention_probs_dropout_prob": 0.1, | ||
| "bos_token_id": 0, | ||
| "eos_token_id": 2, | ||
| "hidden_act": "gelu", | ||
| "hidden_dropout_prob": 0.1, | ||
| "hidden_size": 768, | ||
| "initializer_range": 0.02, | ||
| "intermediate_size": 3072, | ||
| "layer_norm_eps": 1e-05, | ||
| "max_position_embeddings": 514, | ||
| "model_type": "mpnet", | ||
| "num_attention_heads": 12, | ||
| "num_hidden_layers": 12, | ||
| "pad_token_id": 1, | ||
| "relative_attention_num_buckets": 32, | ||
| "transformers_version": "4.8.2", | ||
| "vocab_size": 30527 | ||
| } |
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| { | ||
| "__version__": { | ||
| "sentence_transformers": "2.0.0", | ||
| "transformers": "4.6.1", | ||
| "pytorch": "1.8.1" | ||
| } | ||
| } |
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