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Model: sentence-transformers/msmarco-distilbert-cos-v5
Source: Original Platform

README.md

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+---
+language:
+- en
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers
+pipeline_tag: sentence-similarity
+---
+
+# msmarco-distilbert-cos-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 Passages 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-cos-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
+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
+import torch.nn.functional as F
+
+#Mean Pooling - Take average of all tokens
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output.last_hidden_state #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)
+
+
+#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'])
+
+    # Normalize embeddings
+    embeddings = F.normalize(embeddings, p=2, dim=1)
+	
+    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-cos-v5")
+model = AutoModel.from_pretrained("sentence-transformers/msmarco-distilbert-cos-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
+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 |
+| Produces normalized embeddings | Yes |
+| Pooling-Method | Mean pooling |
+| Suitable score functions | dot-product (`util.dot_score`), cosine-similarity (`util.cos_sim`), or euclidean distance |
+
+Note: When loaded with `sentence-transformers`, this model produces normalized embeddings with length 1. In that case, dot-product and cosine-similarity are equivalent. dot-product is preferred as it is faster. Euclidean distance is proportional to dot-product and can also be used.
+
+## Citing & Authors
+
+This model was trained by [sentence-transformers](https://www.sbert.net/). 
+        
+If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084):
+```bibtex 
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "http://arxiv.org/abs/1908.10084",
+}
+```