Upload README.md

README.md

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+---
+tags:
+- mteb
+model-index:
+- name: sentence-transformers/all-MiniLM-L6-v2
+  results:
+  - task:
+      type: text-retrieval
+      name: Retrieval
+    dataset:
+      name: BSARDRetrieval (default)
+      type: mteb/BSARDRetrieval
+      config: default
+      split: test
+      revision: 8c492add6a14ac188f2debdaf6cbdfb406fd6be3
+    metrics:
+    - type: recall_at_100
+      value: 0.0
+      name: recall_at_100
+    source:
+      url: https://github.com/embeddings-benchmark/mteb/
+      name: MTEB
+  - task:
+      type: translation
+      name: BitextMining
+    dataset:
+      name: BornholmBitextMining (default)
+      type: mteb/BornholmBitextMining
+      config: default
+      split: test
+      revision: 5b02048bd75e79275aa91a1fce6cdfd3f4a391cb
+    metrics:
+    - type: f1
+      value: 0.2968132161955691
+      name: f1
+    source:
+      url: https://github.com/embeddings-benchmark/mteb/
+      name: MTEB
+  - task:
+      type: sentence-similarity
+      name: STS
+    dataset:
+      name: STS22 (ar)
+      type: mteb/sts22-crosslingual-sts
+      config: ar
+      split: test
+      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
+    metrics:
+    - type: cosine_spearman
+      value: 0.2263866797712348
+      name: cosine_spearman
+    source:
+      url: https://github.com/embeddings-benchmark/mteb/
+      name: MTEB
+  - task:
+      type: sentence-similarity
+      name: STS
+    dataset:
+      name: STS22 (de)
+      type: mteb/sts22-crosslingual-sts
+      config: de
+      split: test
+      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
+    metrics:
+    - type: cosine_spearman
+      value: 0.31044353994772356
+      name: cosine_spearman
+    source:
+      url: https://github.com/embeddings-benchmark/mteb/
+      name: MTEB
+  - task:
+      type: sentence-similarity
+      name: STS
+    dataset:
+      name: STS22 (de-en)
+      type: mteb/sts22-crosslingual-sts
+      config: de-en
+      split: test
+      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
+    metrics:
+    - type: cosine_spearman
+      value: 0.44038685024247604
+      name: cosine_spearman
+    source:
+      url: https://github.com/embeddings-benchmark/mteb/
+      name: MTEB
+  - task:
+      type: sentence-similarity
+      name: STS
+    dataset:
+      name: STS22 (de-fr)
+      type: mteb/sts22-crosslingual-sts
+      config: de-fr
+      split: test
+      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
+    metrics:
+    - type: cosine_spearman
+      value: 0.3006758748207823
+      name: cosine_spearman
+    source:
+      url: https://github.com/embeddings-benchmark/mteb/
+      name: MTEB
+  - task:
+      type: sentence-similarity
+      name: STS
+    dataset:
+      name: STS22 (de-pl)
+      type: mteb/sts22-crosslingual-sts
+      config: de-pl
+      split: test
+      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
+    metrics:
+    - type: cosine_spearman
+      value: 0.04927056559940413
+      name: cosine_spearman
+    source:
+      url: https://github.com/embeddings-benchmark/mteb/
+      name: MTEB
+  - task:
+      type: sentence-similarity
+      name: STS
+    dataset:
+      name: STS22 (en)
+      type: mteb/sts22-crosslingual-sts
+      config: en
+      split: test
+      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
+    metrics:
+    - type: cosine_spearman
+      value: 0.6721465212910986
+      name: cosine_spearman
+    source:
+      url: https://github.com/embeddings-benchmark/mteb/
+      name: MTEB
+  - task:
+      type: sentence-similarity
+      name: STS
+    dataset:
+      name: STS22 (es)
+      type: mteb/sts22-crosslingual-sts
+      config: es
+      split: test
+      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
+    metrics:
+    - type: cosine_spearman
+      value: 0.5477772552456677
+      name: cosine_spearman
+    source:
+      url: https://github.com/embeddings-benchmark/mteb/
+      name: MTEB
+  - task:
+      type: sentence-similarity
+      name: STS
+    dataset:
+      name: STS22 (es-en)
+      type: mteb/sts22-crosslingual-sts
+      config: es-en
+      split: test
+      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
+    metrics:
+    - type: cosine_spearman
+      value: 0.5341895837272506
+      name: cosine_spearman
+    source:
+      url: https://github.com/embeddings-benchmark/mteb/
+      name: MTEB
+  - task:
+      type: sentence-similarity
+      name: STS
+    dataset:
+      name: STS22 (es-it)
+      type: mteb/sts22-crosslingual-sts
+      config: es-it
+      split: test
+      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
+    metrics:
+    - type: cosine_spearman
+      value: 0.44269936659450304
+      name: cosine_spearman
+    source:
+      url: https://github.com/embeddings-benchmark/mteb/
+      name: MTEB
+  - task:
+      type: sentence-similarity
+      name: STS
+    dataset:
+      name: STS22 (fr)
+      type: mteb/sts22-crosslingual-sts
+      config: fr
+      split: test
+      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
+    metrics:
+    - type: cosine_spearman
+      value: 0.7700398643056744
+      name: cosine_spearman
+    source:
+      url: https://github.com/embeddings-benchmark/mteb/
+      name: MTEB
+  - task:
+      type: sentence-similarity
+      name: STS
+    dataset:
+      name: STS22 (fr-pl)
+      type: mteb/sts22-crosslingual-sts
+      config: fr-pl
+      split: test
+      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
+    metrics:
+    - type: cosine_spearman
+      value: 0.50709255283711
+      name: cosine_spearman
+    source:
+      url: https://github.com/embeddings-benchmark/mteb/
+      name: MTEB
+  - task:
+      type: sentence-similarity
+      name: STS
+    dataset:
+      name: STS22 (it)
+      type: mteb/sts22-crosslingual-sts
+      config: it
+      split: test
+      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
+    metrics:
+    - type: cosine_spearman
+      value: 0.6039610834515271
+      name: cosine_spearman
+    source:
+      url: https://github.com/embeddings-benchmark/mteb/
+      name: MTEB
+  - task:
+      type: sentence-similarity
+      name: STS
+    dataset:
+      name: STS22 (pl)
+      type: mteb/sts22-crosslingual-sts
+      config: pl
+      split: test
+      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
+    metrics:
+    - type: cosine_spearman
+      value: 0.26768906191975933
+      name: cosine_spearman
+    source:
+      url: https://github.com/embeddings-benchmark/mteb/
+      name: MTEB
+  - task:
+      type: sentence-similarity
+      name: STS
+    dataset:
+      name: STS22 (pl-en)
+      type: mteb/sts22-crosslingual-sts
+      config: pl-en
+      split: test
+      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
+    metrics:
+    - type: cosine_spearman
+      value: 0.32797912957778136
+      name: cosine_spearman
+    source:
+      url: https://github.com/embeddings-benchmark/mteb/
+      name: MTEB
+  - task:
+      type: sentence-similarity
+      name: STS
+    dataset:
+      name: STS22 (ru)
+      type: mteb/sts22-crosslingual-sts
+      config: ru
+      split: test
+      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
+    metrics:
+    - type: cosine_spearman
+      value: 0.14721380413194854
+      name: cosine_spearman
+    source:
+      url: https://github.com/embeddings-benchmark/mteb/
+      name: MTEB
+  - task:
+      type: sentence-similarity
+      name: STS
+    dataset:
+      name: STS22 (tr)
+      type: mteb/sts22-crosslingual-sts
+      config: tr
+      split: test
+      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
+    metrics:
+    - type: cosine_spearman
+      value: 0.3369451080773859
+      name: cosine_spearman
+    source:
+      url: https://github.com/embeddings-benchmark/mteb/
+      name: MTEB
+  - task:
+      type: sentence-similarity
+      name: STS
+    dataset:
+      name: STS22 (zh)
+      type: mteb/sts22-crosslingual-sts
+      config: zh
+      split: test
+      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
+    metrics:
+    - type: cosine_spearman
+      value: 0.4492964024177277
+      name: cosine_spearman
+    source:
+      url: https://github.com/embeddings-benchmark/mteb/
+      name: MTEB
+  - task:
+      type: sentence-similarity
+      name: STS
+    dataset:
+      name: STS22 (zh-en)
+      type: mteb/sts22-crosslingual-sts
+      config: zh-en
+      split: test
+      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
+    metrics:
+    - type: cosine_spearman
+      value: 0.41643997417444484
+      name: cosine_spearman
+    source:
+      url: https://github.com/embeddings-benchmark/mteb/
+      name: MTEB
+---
+# all-MiniLM-L6-v2
+This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 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:
+
+```
+pip install -U sentence-transformers
+```
+
+Then you can use the model like this:
+```python
+from sentence_transformers import SentenceTransformer
+sentences = ["This is an example sentence", "Each sentence is converted"]
+
+model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+## 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.
+
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+import torch.nn.functional as F
+
+#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)
+
+
+# Sentences we want sentence embeddings for
+sentences = ['This is an example sentence', 'Each sentence is converted']
+
+# Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
+model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
+
+# Tokenize sentences
+encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+
+# Compute token embeddings
+with torch.no_grad():
+    model_output = model(**encoded_input)
+
+# Perform pooling
+sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+
+# Normalize embeddings
+sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
+
+print("Sentence embeddings:")
+print(sentence_embeddings)
+```
+
+## Evaluation Results
+
+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-MiniLM-L6-v2)
+
+------
+
+## Background
+
+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 [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) 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.
+
+We developed 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 developed 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.
+
+## Intended uses
+
+Our model is intended to be used as a sentence and short paragraph encoder. Given an input text, it outputs a vector which captures
+the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks.
+
+By default, input text longer than 256 word pieces is truncated.
+
+
+## Training procedure
+
+### Pre-training
+
+We use the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model. Please refer to the model card for more detailed information about the pre-training procedure.
+
+### Fine-tuning
+
+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.
+
+#### Hyper parameters
+
+We 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).
+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`.
+
+#### Training data
+
+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.
+
+
+| 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** |