Update README.md

README.md

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----
-license: apache-2.0
----
+---
+language: en
+license: apache-2.0
+tags:
+- learned sparse
+- opensearch
+- transformers
+- retrieval
+- passage-retrieval
+- document-expansion
+- bag-of-words
+---
+
+# opensearch-neural-sparse-encoding-doc-v2-distill
+This is a learned sparse retrieval model. It encodes the documents to 30522 dimensional **sparse vectors**. For queries, it just use a tokenizer and a weight look-up table to generate sparse vectors. The non-zero dimension index means the corresponding token in the vocabulary, and the weight means the importance of the token. And the similarity score is the inner product of query/document sparse vectors. In the real-world use case, the search performance of opensearch-neural-sparse-encoding-v1 is comparable to BM25.
+
+This model is trained on MS MARCO dataset.
+
+OpenSearch neural sparse feature supports learned sparse retrieval with lucene inverted index. Link: https://opensearch.org/docs/latest/query-dsl/specialized/neural-sparse/. The indexing and search can be performed with OpenSearch high-level API.
+
+## Select the model
+The model should be selected considering search relevance, model inference and retrieval efficiency(FLOPS). We benchmark models' **zero-shot performance** on a subset of BEIR benchmark: TrecCovid,NFCorpus,NQ,HotpotQA,FiQA,ArguAna,Touche,DBPedia,SCIDOCS,FEVER,Climate FEVER,SciFact,Quora.
+
+Overall, the v2 series of models have better search relevance, efficiency and inference speed than the v1 series. The specific advantages and disadvantages may vary across different datasets.
+
+| Model | Inference-free for Retrieval | Model Parameters | AVG NDCG@10 | AVG FLOPS |
+|-------|------------------------------|------------------|-------------|-----------|
+| [opensearch-neural-sparse-encoding-v1](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-v1) |  | 133M | 0.524 | 11.4 |
+| [opensearch-neural-sparse-encoding-v2-distill](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-v2-distill) |  | 67M | 0.528 | 8.3 |
+| [opensearch-neural-sparse-encoding-doc-v1](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-doc-v1) | ✔️ | 133M | 0.490 | 2.3 |
+| [opensearch-neural-sparse-encoding-doc-v2-distill](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-doc-v2-distill) | ✔️ | 67M | 0.504 | 1.8 |
+
+
+## Usage (HuggingFace)
+This model is supposed to run inside OpenSearch cluster. But you can also use it outside the cluster, with HuggingFace models API. 
+
+```python
+import json
+import itertools
+import torch
+
+from transformers import AutoModelForMaskedLM, AutoTokenizer
+
+
+# get sparse vector from dense vectors with shape batch_size * seq_len * vocab_size
+def get_sparse_vector(feature, output):
+    values, _ = torch.max(output*feature["attention_mask"].unsqueeze(-1), dim=1)
+    values = torch.log(1 + torch.relu(values))
+    values[:,special_token_ids] = 0
+    return values
+    
+# transform the sparse vector to a dict of (token, weight)
+def transform_sparse_vector_to_dict(sparse_vector):
+    sample_indices,token_indices=torch.nonzero(sparse_vector,as_tuple=True)
+    non_zero_values = sparse_vector[(sample_indices,token_indices)].tolist()
+    number_of_tokens_for_each_sample = torch.bincount(sample_indices).cpu().tolist()
+    tokens = [transform_sparse_vector_to_dict.id_to_token[_id] for _id in token_indices.tolist()]
+
+    output = []
+    end_idxs = list(itertools.accumulate([0]+number_of_tokens_for_each_sample))
+    for i in range(len(end_idxs)-1):
+        token_strings = tokens[end_idxs[i]:end_idxs[i+1]]
+        weights = non_zero_values[end_idxs[i]:end_idxs[i+1]]
+        output.append(dict(zip(token_strings, weights)))
+    return output
+    
+# download the idf file from model hub. idf is used to give weights for query tokens
+def get_tokenizer_idf(tokenizer):
+    from huggingface_hub import hf_hub_download
+    local_cached_path = hf_hub_download(repo_id="opensearch-project/opensearch-neural-sparse-encoding-doc-v2-distill", filename="idf.json")
+    with open(local_cached_path) as f:
+        idf = json.load(f)
+    idf_vector = [0]*tokenizer.vocab_size
+    for token,weight in idf.items():
+        _id = tokenizer._convert_token_to_id_with_added_voc(token)
+        idf_vector[_id]=weight
+    return torch.tensor(idf_vector)
+
+# load the model
+model = AutoModelForMaskedLM.from_pretrained("opensearch-project/opensearch-neural-sparse-encoding-doc-v2-distill")
+tokenizer = AutoTokenizer.from_pretrained("opensearch-project/opensearch-neural-sparse-encoding-doc-v2-distill")
+idf = get_tokenizer_idf(tokenizer)
+
+# set the special tokens and id_to_token transform for post-process
+special_token_ids = [tokenizer.vocab[token] for token in tokenizer.special_tokens_map.values()]
+get_sparse_vector.special_token_ids = special_token_ids
+id_to_token = ["" for i in range(tokenizer.vocab_size)]
+for token, _id in tokenizer.vocab.items():
+    id_to_token[_id] = token
+transform_sparse_vector_to_dict.id_to_token = id_to_token
+
+
+
+query = "What's the weather in ny now?"
+document = "Currently New York is rainy."
+
+# encode the query
+feature_query = tokenizer([query], padding=True, truncation=True, return_tensors='pt', return_token_type_ids=False)
+input_ids = feature_query["input_ids"]
+batch_size = input_ids.shape[0]
+query_vector = torch.zeros(batch_size, tokenizer.vocab_size)
+query_vector[torch.arange(batch_size).unsqueeze(-1), input_ids] = 1
+query_sparse_vector = query_vector*idf
+
+# encode the document
+feature_document = tokenizer([document], padding=True, truncation=True, return_tensors='pt', return_token_type_ids=False)
+output = model(**feature_document)[0]
+document_sparse_vector = get_sparse_vector(feature_document, output)
+
+
+# get similarity score
+sim_score = torch.matmul(query_sparse_vector[0],document_sparse_vector[0])
+print(sim_score)   # tensor(12.8465, grad_fn=<DotBackward0>)
+
+
+query_token_weight = transform_sparse_vector_to_dict(query_sparse_vector)[0]
+document_query_token_weight = transform_sparse_vector_to_dict(document_sparse_vector)[0]
+for token in sorted(query_token_weight, key=lambda x:query_token_weight[x], reverse=True):
+    if token in document_query_token_weight:
+        print("score in query: %.4f, score in document: %.4f, token: %s"%(query_token_weight[token],document_query_token_weight[token],token))
+        
+
+        
+# result:
+# score in query: 5.7729, score in document: 1.4109, token: ny
+# score in query: 4.5684, score in document: 1.4673, token: weather
+# score in query: 3.5895, score in document: 0.7473, token: now
+```
+
+The above code sample shows an example of neural sparse search. Although there is no overlap token in original query and document, but this model performs a good match. 
+
+## Detailed Search Relevance
+
+| Dataset | [opensearch-neural-sparse-encoding-v1](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-v1) | [opensearch-neural-sparse-encoding-v2-distill](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-v2-distill) | [opensearch-neural-sparse-encoding-doc-v1](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-doc-v1) | [opensearch-neural-sparse-encoding-doc-v2-distill](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-doc-v2-distill) |
+|---------|-------------------------------------------------------------------------|-------------------------------------------------------------------------------------|------------------------------------------------------------------------------|------------------------------------------------------------------------------------------|
+| Trec Covid | 0.771 | 0.775 | 0.707 | 0.690 |
+| NFCorpus | 0.360 | 0.347 | 0.352 | 0.343 |
+| NQ | 0.553 | 0.561 | 0.521 | 0.528 |
+| HotpotQA | 0.697 | 0.685 | 0.677 | 0.675 |
+| FiQA | 0.376 | 0.374 | 0.344 | 0.357 |
+| ArguAna | 0.508 | 0.551 | 0.461 | 0.496 |
+| Touche | 0.278 | 0.278 | 0.294 | 0.287 |
+| DBPedia | 0.447 | 0.435 | 0.412 | 0.418 |
+| SCIDOCS | 0.164 | 0.173 | 0.154 | 0.166 |
+| FEVER | 0.821 | 0.849 | 0.743 | 0.818 |
+| Climate FEVER | 0.263 | 0.249 | 0.202 | 0.224 |
+| SciFact | 0.723 | 0.722 | 0.716 | 0.715 |
+| Quora | 0.856 | 0.863 | 0.788 | 0.841 |
+| **Average** | **0.524** | **0.528** | **0.490** | **0.504** |