---
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** |