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opensearch-project/
opensearch-neural-sparse-encoding-multilingual-v1

Name: opensearch-neural-sparse-encoding-multilingual-v1
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opensearch-neural-sparse-encoding-multilingual-v1

Select the model

The model should be selected considering search relevance, model inference and retrieval efficiency(FLOPS). We benchmark models' performance on MIRACL benchmark (we exclude th since the uncased backbone can not encode it). We recommend to use it with max_ratio pruning.

Model	Inference-free for Retrieval	Model Parameters	AVG NDCG@10	AVG FLOPS	AVG EMB SIZE
opensearch-neural-sparse-encoding-multilingual-v1	✔️	160M	0.629	1.3	138
opensearch-neural-sparse-encoding-multilingual-v1; prune_ratio 0.1	✔️	160M	0.626	0.8	75

Overview

Paper: Towards Competitive Search Relevance For Inference-Free Learned Sparse Retrievers
Fine-tuning sample: opensearch-sparse-model-tuning-sample

This is a learned sparse retrieval model. It encodes the documents to 105879 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.

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 (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

python

from sentence_transformers.sparse_encoder import SparseEncoder

# Download from the 🤗 Hub
model = SparseEncoder("opensearch-project/opensearch-neural-sparse-encoding-multilingual-v1")

query = "What's the weather in ny now?"
document = "Currently New York is rainy."

query_embed = model.encode_query(query)
document_embed = model.encode_document(document)

sim = model.similarity(query_embed, document_embed)
print(f"Similarity: {sim}")
# Similarity: tensor([[7.7400]])

decoded_query = model.decode(query_embed)
decoded_document = model.decode(document_embed)

for i in range(len(decoded_query)):
    query_token, query_score = decoded_query[i]
    doc_score = next((score for token, score in decoded_document if token == query_token), 0)
    if doc_score != 0:
        print(f"Token: {query_token}, Query score: {query_score:.4f}, Document score: {doc_score:.4f}")

# Token: weather, Query score: 3.0699, Document score: 1.2821
# Token: now, Query score: 1.6406, Document score: 0.9018
# Token: ?, Query score: 1.6108, Document score: 0.3141
# Token: ny, Query score: 1.2721, Document score: 1.3446
# Token: in, Query score: 0.6005, Document score: 0.1804

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, prune_ratio=0.1):
    values, _ = torch.max(output*feature["attention_mask"].unsqueeze(-1), dim=1)
    values = torch.log(1 + torch.relu(values))
    values[:,special_token_ids] = 0
    max_values = values.max(dim=-1)[0].unsqueeze(1) * prune_ratio
    return values * (values > max_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-multilingual-v1", 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-multilingual-v1")
tokenizer = AutoTokenizer.from_pretrained("opensearch-project/opensearch-neural-sparse-encoding-multilingual-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(7.6317, 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: 3.0699, score in document: 1.2821, token: weather
# score in query: 1.6406, score in document: 0.9018, token: now
# score in query: 1.6108, score in document: 0.3141, token: ?
# score in query: 1.2721, score in document: 1.3446, token: ny

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

Model	Average	bn	te	es	fr	id	hi	ru	ar	zh	fa	ja	fi	sw	ko	en
BM25	0.305	0.482	0.383	0.077	0.115	0.297	0.350	0.256	0.395	0.175	0.287	0.312	0.458	0.351	0.371	0.267
opensearch-neural-sparse-encoding-multilingual-v1	0.629	0.670	0.740	0.542	0.558	0.582	0.486	0.658	0.740	0.562	0.514	0.669	0.767	0.768	0.607	0.575
opensearch-neural-sparse-encoding-multilingual-v1; prune_ratio 0.1	0.626	0.667	0.740	0.537	0.555	0.576	0.481	0.655	0.737	0.558	0.511	0.664	0.761	0.766	0.604	0.572

License

This project is licensed under the Apache v2.0 License.

opensearch-project/opensearch-neural-sparse-encoding-multilingual-v1