Update README.md

README.md

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 ---
+language: en
 license: apache-2.0
+tags:
+- learned sparse
+- opensearch
+- transformers
+- retrieval
 ---
+
+# opensearch-neural-sparse-encoding-v1
+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.
+
+
+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.
+
+## Usage (HuggingFace)
+This model is supposed to run inside OpenSearch cluster. But you can also use it outside the cluster, with HuggingFace models API. 
+
+```
+import json
+import itertools
+import torch
+
+from transformers import AutoModelForMaskedLM, AutoTokenizer
+from transformers.utils import cached_path,hf_bucket_url
+
+
+# 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):
+    url = hf_bucket_url("opensearch-project/opensearch-neural-sparse-encoding-doc-v1","idf.json")
+    local_cached_path = cached_path(url)
+    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-v1")
+tokenizer = AutoTokenizer.from_pretrained("opensearch-project/opensearch-neural-sparse-encoding-doc-v1")
+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: 1.0552, token: ny
+# score in query: 4.5684, score in document: 1.1697, token: weather
+# score in query: 3.5895, score in document: 0.3932, 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. 
+
+## Performance
+This model is trained on MS MARCO dataset. The search relevance score of it can be found here (Neural sparse search bi-encoder) https://opensearch.org/blog/improving-document-retrieval-with-sparse-semantic-encoders/.