readme: ---
  pipeline_tag: sentence-similarity
  tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  language: en
  license: apache-2.0
  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
  
  ---
  
  
  # 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 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.
  
  ## Intended uses
  
  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.
  
  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