readme: ---
  pipeline_tag: sentence-similarity
  license: apache-2.0
  tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  - transformers
  ---
  
  # msmarco-distilbert-dot-v5
  This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for **semantic search**. It has been trained on 500K (query, answer) pairs from the [MS MARCO dataset](https://github.com/microsoft/MSMARCO-Passage-Ranking/). For an introduction to semantic search, have a look at: [SBERT.net - Semantic Search](https://www.sbert.net/examples/applications/semantic-search/README.html)
  
  
  ## 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, util
  
  query = "How many people live in London?"
  docs = ["Around 9 Million people live in London", "London is known for its financial district"]
  
  #Load the model
  model = SentenceTransformer('sentence-transformers/msmarco-distilbert-dot-v5')
  
  #Encode query and documents
  query_emb = model.encode(query)
  doc_emb = model.encode(docs)
  
  #Compute dot score between query and all document embeddings
  scores = util.dot_score(query_emb, doc_emb)[0].cpu().tolist()
  
  #Combine docs & scores
  doc_score_pairs = list(zip(docs, scores))
  
  #Sort by decreasing score
  doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
  
  #Output passages & scores
  print("Query:", query)
  for doc, score in doc_score_pairs:
      print(score, doc)
  ```
  
  
  ## 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 correct pooling-operation on-top of the contextualized word embeddings.
  
  ```python
  from transformers import AutoTokenizer, AutoModel
  import torch
  
  #Mean Pooling - Take attention mask into account for correct averaging
  def mean_pooling(model_output, attention_mask):
      token_embeddings = model_output.last_hidden_state
      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)
  
  
  #Encode text
  def encode(texts):
      # Tokenize sentences
      encoded_input = tokenizer(texts, padding=True, truncation=True, return_tensors='pt')
  
      # Compute token embeddings
      with torch.no_grad():
          model_output = model(**encoded_input, return_dict=True)
  
      # Perform pooling
      embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
  
      return embeddings
  
  
  # Sentences we want sentence embeddings for
  query = "How many people live in London?"
  docs = ["Around 9 Million people live in London", "London is known for its financial district"]
  
  # Load model from HuggingFace Hub
  tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/msmarco-distilbert-dot-v5")
  model = AutoModel.from_pretrained("sentence-transformers/msmarco-distilbert-dot-v5")
  
  #Encode query and docs
  query_emb = encode(query)
  doc_emb = encode(docs)
  
  #Compute dot score between query and all document embeddings
  scores = torch.mm(query_emb, doc_emb.transpose(0, 1))[0].cpu().tolist()
  
  #Combine docs & scores
  doc_score_pairs = list(zip(docs, scores))
  
  #Sort by decreasing score
  doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
  
  #Output passages & scores
  print("Query:", query)
  for doc, score in doc_score_pairs:
      print(score, doc)
  ```
  
  ## Technical Details
  
  In the following some technical details how this model must be used:
  
  | Setting | Value |
  | --- | :---: |
  | Dimensions | 768 |
  | Max Sequence Length | 512 |
  | Produces normalized embeddings | No |
  | Pooling-Method | Mean pooling |
  | Suitable score functions | dot-product (e.g. `util.dot_score`) |
  
  
  ## Evaluation Results
  
  <!--- Describe how your model was evaluated -->
  
  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=msmarco-distilbert-base-dot-v5)
  
  
  ## Training
  
  See `train_script.py` in this repository for the used training script.
  
  
  
  The model was trained with the parameters:
  
  **DataLoader**:
  
  `torch.utils.data.dataloader.DataLoader` of length 7858 with parameters:
  ```
  {'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
  ```
  
  **Loss**:
  
  `sentence_transformers.losses.MarginMSELoss.MarginMSELoss` 
  
  Parameters of the fit()-Method:
  ```
  {
      "callback": null,
      "epochs": 30,
      "evaluation_steps": 0,
      "evaluator": "NoneType",
      "max_grad_norm": 1,
      "optimizer_class": "<class 'transformers.optimization.AdamW'>",
      "optimizer_params": {
          "lr": 1e-05
      },
      "scheduler": "WarmupLinea