sentence-transformers/all-mpnet-base-v2

1---2language: en3license: apache-2.04library_name: sentence-transformers5tags:6- sentence-transformers7- feature-extraction8- sentence-similarity9- transformers10- text-embeddings-inference11datasets:12- s2orc13- flax-sentence-embeddings/stackexchange_xml14- ms_marco15- gooaq16- yahoo_answers_topics17- code_search_net18- search_qa19- eli520- snli21- multi_nli22- wikihow23- natural_questions24- trivia_qa25- embedding-data/sentence-compression26- embedding-data/flickr30k-captions27- embedding-data/altlex28- embedding-data/simple-wiki29- embedding-data/QQP30- embedding-data/SPECTER31- embedding-data/PAQ_pairs32- embedding-data/WikiAnswers33pipeline_tag: sentence-similarity34---35 36 37# all-mpnet-base-v238This 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.39 40## Usage (Sentence-Transformers)41Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:42 43```44pip install -U sentence-transformers45```46 47Then you can use the model like this:48```python49from sentence_transformers import SentenceTransformer50sentences = ["This is an example sentence", "Each sentence is converted"]51 52model = SentenceTransformer('sentence-transformers/all-mpnet-base-v2')53embeddings = model.encode(sentences)54print(embeddings)55```56 57## Usage (HuggingFace Transformers)58Without [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.59 60```python61from transformers import AutoTokenizer, AutoModel62import torch63import torch.nn.functional as F64 65#Mean Pooling - Take attention mask into account for correct averaging66def mean_pooling(model_output, attention_mask):67    token_embeddings = model_output[0] #First element of model_output contains all token embeddings68    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()69    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)70 71 72# Sentences we want sentence embeddings for73sentences = ['This is an example sentence', 'Each sentence is converted']74 75# Load model from HuggingFace Hub76tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-mpnet-base-v2')77model = AutoModel.from_pretrained('sentence-transformers/all-mpnet-base-v2')78 79# Tokenize sentences80encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')81 82# Compute token embeddings83with torch.no_grad():84    model_output = model(**encoded_input)85 86# Perform pooling87sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])88 89# Normalize embeddings90sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)91 92print("Sentence embeddings:")93print(sentence_embeddings)94```95 96## Usage (Text Embeddings Inference (TEI))97 98[Text Embeddings Inference (TEI)](https://github.com/huggingface/text-embeddings-inference) is a blazing fast inference solution for text embedding models.99 100- CPU:101```bash102docker run -p 8080:80 -v hf_cache:/data --pull always ghcr.io/huggingface/text-embeddings-inference:cpu-latest --model-id sentence-transformers/all-mpnet-base-v2 --pooling mean --dtype float16103```104 105- NVIDIA GPU:106```bash107docker run --gpus all -p 8080:80 -v hf_cache:/data --pull always ghcr.io/huggingface/text-embeddings-inference:cuda-latest --model-id sentence-transformers/all-mpnet-base-v2 --pooling mean --dtype float16108```109 110Send a request to `/v1/embeddings` to generate embeddings via the [OpenAI Embeddings API](https://platform.openai.com/docs/api-reference/embeddings/create):111```bash112curl http://localhost:8080/v1/embeddings \113  -H 'Content-Type: application/json' \114  -d '{115    "model": "sentence-transformers/all-mpnet-base-v2",116    "input": ["This is an example sentence", "Each sentence is converted"]117  }'118```119 120Or check the [Text Embeddings Inference API specification](https://huggingface.github.io/text-embeddings-inference/) instead.121 122------123 124## Background125 126The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised 127contrastive learning objective. We used the pretrained [`microsoft/mpnet-base`](https://huggingface.co/microsoft/mpnet-base) model and fine-tuned in on a 1281B 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.129 130We developed this model during the 131[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), 132organized by Hugging Face. We developed this model as part of the project:133[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.134 135## Intended uses136 137Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it outputs a vector which captures 138the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks.139 140By default, input text longer than 384 word pieces is truncated.141 142 143## Training procedure144 145### Pre-training 146 147We 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.148 149### Fine-tuning 150 151We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch.152We then apply the cross entropy loss by comparing with true pairs.153 154#### Hyper parameters155 156We trained our model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core).157We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with158a 2e-5 learning rate. The full training script is accessible in this current repository: `train_script.py`.159 160#### Training data161 162We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences.163We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file.164 165 166| Dataset                                                  | Paper                                    | Number of training tuples  |167|--------------------------------------------------------|:----------------------------------------:|:--------------------------:|168| [Reddit comments (2015-2018)](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) | [paper](https://arxiv.org/abs/1904.06472) | 726,484,430 |169| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Abstracts) | [paper](https://aclanthology.org/2020.acl-main.447/) | 116,288,806 |170| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs | [paper](https://doi.org/10.1145/2623330.2623677) | 77,427,422 |171| [PAQ](https://github.com/facebookresearch/PAQ) (Question, Answer) pairs | [paper](https://arxiv.org/abs/2102.07033) | 64,371,441 |172| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Titles) | [paper](https://aclanthology.org/2020.acl-main.447/) | 52,603,982 |173| [S2ORC](https://github.com/allenai/s2orc) (Title, Abstract) | [paper](https://aclanthology.org/2020.acl-main.447/) | 41,769,185 |174| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs  | - | 25,316,456 |175| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title+Body, Answer) pairs  | - | 21,396,559 |176| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs  | - | 21,396,559 |177| [MS MARCO](https://microsoft.github.io/msmarco/) triplets | [paper](https://doi.org/10.1145/3404835.3462804) | 9,144,553 |178| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) | [paper](https://arxiv.org/pdf/2104.08727.pdf) | 3,012,496 |179| [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 |180| [Code Search](https://huggingface.co/datasets/code_search_net) | - | 1,151,414 |181| [COCO](https://cocodataset.org/#home) Image captions | [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) | 828,395|182| [SPECTER](https://github.com/allenai/specter) citation triplets | [paper](https://doi.org/10.18653/v1/2020.acl-main.207) | 684,100 |183| [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 |184| [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 |185| [SearchQA](https://huggingface.co/datasets/search_qa) | [paper](https://arxiv.org/abs/1704.05179) | 582,261 |186| [Eli5](https://huggingface.co/datasets/eli5) | [paper](https://doi.org/10.18653/v1/p19-1346) | 325,475 |187| [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) | 317,695 |188| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles) | | 304,525 |189| 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 | 190| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (bodies) | | 250,519 |191| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles+bodies) | | 250,460 |192| [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) | [paper](https://www.aclweb.org/anthology/D13-1155/) | 180,000 |193| [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) | [paper](https://arxiv.org/abs/1810.09305) | 128,542 |194| [Altlex](https://github.com/chridey/altlex/) | [paper](https://aclanthology.org/P16-1135.pdf) | 112,696 |195| [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | - | 103,663 |196| [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) | [paper](https://www.aclweb.org/anthology/P11-2117/) | 102,225 |197| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) | 100,231 |198| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | [paper](https://aclanthology.org/P18-2124.pdf) | 87,599 |199| [TriviaQA](https://huggingface.co/datasets/trivia_qa) | - | 73,346 |200| **Total** | | **1,170,060,424** |