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Model: opensearch-project/opensearch-neural-sparse-encoding-v1 Source: Original Platform
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1_SpladePooling/config.json
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{
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"pooling_strategy": "max",
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"activation_function": "relu",
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"word_embedding_dimension": null
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}
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README.md
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README.md
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---
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language: en
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license: apache-2.0
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tags:
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- learned sparse
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- opensearch
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- transformers
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- retrieval
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- passage-retrieval
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- query-expansion
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- document-expansion
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- bag-of-words
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- sentence-transformers
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- sparse-encoder
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- sparse
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- splade
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pipeline_tag: feature-extraction
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library_name: sentence-transformers
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---
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# opensearch-neural-sparse-encoding-v1
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## Select the model
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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.
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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.
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| Model | Inference-free for Retrieval | Model Parameters | AVG NDCG@10 | AVG FLOPS |
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|-------|------------------------------|------------------|-------------|-----------|
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| [opensearch-neural-sparse-encoding-v1](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-v1) | | 133M | 0.524 | 11.4 |
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| [opensearch-neural-sparse-encoding-v2-distill](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-v2-distill) | | 67M | 0.528 | 8.3 |
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| [opensearch-neural-sparse-encoding-doc-v1](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-doc-v1) | ✔️ | 133M | 0.490 | 2.3 |
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| [opensearch-neural-sparse-encoding-doc-v2-distill](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-doc-v2-distill) | ✔️ | 67M | 0.504 | 1.8 |
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| [opensearch-neural-sparse-encoding-doc-v2-mini](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-doc-v2-mini) | ✔️ | 23M | 0.497 | 1.7 |
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| [opensearch-neural-sparse-encoding-doc-v3-distill](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-doc-v3-distill) | ✔️ | 67M | 0.517 | 1.8 |
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| [opensearch-neural-sparse-encoding-doc-v3-gte](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-doc-v3-gte) | ✔️ | 133M | 0.546 | 1.7 |
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## Overview
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- **Paper**: [Towards Competitive Search Relevance For Inference-Free Learned Sparse Retrievers](https://arxiv.org/abs/2411.04403)
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- **Fine-tuning sample**: [opensearch-sparse-model-tuning-sample](https://github.com/zhichao-aws/opensearch-sparse-model-tuning-sample)
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This is a learned sparse retrieval model. It encodes the queries and documents to 30522 dimensional **sparse vectors**. The non-zero dimension index means the corresponding token in the vocabulary, and the weight means the importance of the token.
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This model is trained on MS MARCO dataset.
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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.
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## Usage (Sentence Transformers)
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First install the Sentence Transformers library:
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```bash
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pip install -U sentence-transformers
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```
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Then you can load this model and run inference.
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```python
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from sentence_transformers.sparse_encoder import SparseEncoder
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# Download from the 🤗 Hub
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model = SparseEncoder("opensearch-project/opensearch-neural-sparse-encoding-v1")
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query = "What's the weather in ny now?"
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document = "Currently New York is rainy."
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query_embed = model.encode_query(query)
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document_embed = model.encode_document(document)
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sim = model.similarity(query_embed, document_embed)
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print(f"Similarity: {sim}")
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# Similarity: tensor([[22.3299]])
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decoded_query = model.decode(query_embed)
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decoded_document = model.decode(document_embed)
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for i in range(len(decoded_query)):
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query_token, query_score = decoded_query[i]
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doc_score = next((score for token, score in decoded_document if token == query_token), 0)
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if doc_score != 0:
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print(f"Token: {query_token}, Query score: {query_score:.4f}, Document score: {doc_score:.4f}")
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# Token: ny, Query score: 2.9262, Document score: 2.1335
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# Token: weather, Query score: 2.5206, Document score: 1.5277
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# Token: york, Query score: 2.0373, Document score: 2.3489
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# Token: cool, Query score: 1.5786, Document score: 0.8752
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# Token: current, Query score: 1.4636, Document score: 1.5132
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# Token: season, Query score: 0.7761, Document score: 0.8860
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# Token: 2020, Query score: 0.7560, Document score: 0.6726
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# Token: summer, Query score: 0.7222, Document score: 0.6292
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# Token: nina, Query score: 0.6888, Document score: 0.6419
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# Token: storm, Query score: 0.6451, Document score: 0.8200
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# Token: brooklyn, Query score: 0.4698, Document score: 0.7635
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# Token: julian, Query score: 0.4562, Document score: 0.1208
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# Token: wow, Query score: 0.3484, Document score: 0.3903
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# Token: usa, Query score: 0.3439, Document score: 0.4160
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# Token: manhattan, Query score: 0.2751, Document score: 0.8260
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# Token: fog, Query score: 0.2013, Document score: 0.7735
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# Token: mood, Query score: 0.1989, Document score: 0.2961
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# Token: climate, Query score: 0.1653, Document score: 0.3437
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# Token: nature, Query score: 0.1191, Document score: 0.1533
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# Token: temperature, Query score: 0.0665, Document score: 0.0599
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# Token: windy, Query score: 0.0552, Document score: 0.3396
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```
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## Usage (HuggingFace)
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This model is supposed to run inside OpenSearch cluster. But you can also use it outside the cluster, with HuggingFace models API.
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```python
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import itertools
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import torch
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from transformers import AutoModelForMaskedLM, AutoTokenizer
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# get sparse vector from dense vectors with shape batch_size * seq_len * vocab_size
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def get_sparse_vector(feature, output):
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values, _ = torch.max(output*feature["attention_mask"].unsqueeze(-1), dim=1)
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values = torch.log(1 + torch.relu(values))
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values[:,special_token_ids] = 0
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return values
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# transform the sparse vector to a dict of (token, weight)
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def transform_sparse_vector_to_dict(sparse_vector):
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sample_indices,token_indices=torch.nonzero(sparse_vector,as_tuple=True)
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non_zero_values = sparse_vector[(sample_indices,token_indices)].tolist()
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number_of_tokens_for_each_sample = torch.bincount(sample_indices).cpu().tolist()
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tokens = [transform_sparse_vector_to_dict.id_to_token[_id] for _id in token_indices.tolist()]
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output = []
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end_idxs = list(itertools.accumulate([0]+number_of_tokens_for_each_sample))
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for i in range(len(end_idxs)-1):
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token_strings = tokens[end_idxs[i]:end_idxs[i+1]]
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weights = non_zero_values[end_idxs[i]:end_idxs[i+1]]
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output.append(dict(zip(token_strings, weights)))
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return output
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# load the model
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model = AutoModelForMaskedLM.from_pretrained("opensearch-project/opensearch-neural-sparse-encoding-v1")
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tokenizer = AutoTokenizer.from_pretrained("opensearch-project/opensearch-neural-sparse-encoding-v1")
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# set the special tokens and id_to_token transform for post-process
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special_token_ids = [tokenizer.vocab[token] for token in tokenizer.special_tokens_map.values()]
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get_sparse_vector.special_token_ids = special_token_ids
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id_to_token = ["" for i in range(tokenizer.vocab_size)]
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for token, _id in tokenizer.vocab.items():
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id_to_token[_id] = token
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transform_sparse_vector_to_dict.id_to_token = id_to_token
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query = "What's the weather in ny now?"
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document = "Currently New York is rainy."
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# encode the query & document
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feature = tokenizer([query, document], padding=True, truncation=True, return_tensors='pt', return_token_type_ids=False)
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output = model(**feature)[0]
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sparse_vector = get_sparse_vector(feature, output)
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# get similarity score
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sim_score = torch.matmul(sparse_vector[0],sparse_vector[1])
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print(sim_score) # tensor(22.3299, grad_fn=<DotBackward0>)
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query_token_weight, document_query_token_weight = transform_sparse_vector_to_dict(sparse_vector)
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for token in sorted(query_token_weight, key=lambda x:query_token_weight[x], reverse=True):
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if token in document_query_token_weight:
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print("score in query: %.4f, score in document: %.4f, token: %s"%(query_token_weight[token],document_query_token_weight[token],token))
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# result:
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# score in query: 2.9262, score in document: 2.1335, token: ny
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# score in query: 2.5206, score in document: 1.5277, token: weather
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# score in query: 2.0373, score in document: 2.3489, token: york
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# score in query: 1.5786, score in document: 0.8752, token: cool
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# score in query: 1.4636, score in document: 1.5132, token: current
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# score in query: 0.7761, score in document: 0.8860, token: season
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# score in query: 0.7560, score in document: 0.6726, token: 2020
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# score in query: 0.7222, score in document: 0.6292, token: summer
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# score in query: 0.6888, score in document: 0.6419, token: nina
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# score in query: 0.6451, score in document: 0.8200, token: storm
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# score in query: 0.4698, score in document: 0.7635, token: brooklyn
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# score in query: 0.4562, score in document: 0.1208, token: julian
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# score in query: 0.3484, score in document: 0.3903, token: wow
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# score in query: 0.3439, score in document: 0.4160, token: usa
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# score in query: 0.2751, score in document: 0.8260, token: manhattan
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# score in query: 0.2013, score in document: 0.7735, token: fog
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# score in query: 0.1989, score in document: 0.2961, token: mood
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# score in query: 0.1653, score in document: 0.3437, token: climate
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# score in query: 0.1191, score in document: 0.1533, token: nature
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# score in query: 0.0665, score in document: 0.0600, token: temperature
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# score in query: 0.0552, score in document: 0.3396, token: windy
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```
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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.
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## Detailed Search Relevance
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<div style="overflow-x: auto;">
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| Model | Average | Trec Covid | NFCorpus | NQ | HotpotQA | FiQA | ArguAna | Touche | DBPedia | SCIDOCS | FEVER | Climate FEVER | SciFact | Quora |
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|-------|---------|------------|----------|----|----------|------|---------|--------|---------|---------|-------|---------------|---------|-------|
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| [opensearch-neural-sparse-encoding-v1](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-v1) | 0.524 | 0.771 | 0.360 | 0.553 | 0.697 | 0.376 | 0.508 | 0.278 | 0.447 | 0.164 | 0.821 | 0.263 | 0.723 | 0.856 |
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| [opensearch-neural-sparse-encoding-v2-distill](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-v2-distill) | 0.528 | 0.775 | 0.347 | 0.561 | 0.685 | 0.374 | 0.551 | 0.278 | 0.435 | 0.173 | 0.849 | 0.249 | 0.722 | 0.863 |
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| [opensearch-neural-sparse-encoding-doc-v1](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-doc-v1) | 0.490 | 0.707 | 0.352 | 0.521 | 0.677 | 0.344 | 0.461 | 0.294 | 0.412 | 0.154 | 0.743 | 0.202 | 0.716 | 0.788 |
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| [opensearch-neural-sparse-encoding-doc-v2-distill](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-doc-v2-distill) | 0.504 | 0.690 | 0.343 | 0.528 | 0.675 | 0.357 | 0.496 | 0.287 | 0.418 | 0.166 | 0.818 | 0.224 | 0.715 | 0.841 |
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| [opensearch-neural-sparse-encoding-doc-v2-mini](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-doc-v2-mini) | 0.497 | 0.709 | 0.336 | 0.510 | 0.666 | 0.338 | 0.480 | 0.285 | 0.407 | 0.164 | 0.812 | 0.216 | 0.699 | 0.837 |
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| [opensearch-neural-sparse-encoding-doc-v3-distill](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-doc-v3-distill) | 0.517 | 0.724 | 0.345 | 0.544 | 0.694 | 0.356 | 0.520 | 0.294 | 0.424 | 0.163 | 0.845 | 0.239 | 0.708 | 0.863 |
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| [opensearch-neural-sparse-encoding-doc-v3-gte](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-doc-v3-gte) | 0.546 | 0.734 | 0.360 | 0.582 | 0.716 | 0.407 | 0.520 | 0.389 | 0.455 | 0.167 | 0.860 | 0.312 | 0.725 | 0.873 |
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</div>
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## License
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This project is licensed under the [Apache v2.0 License](https://github.com/opensearch-project/neural-search/blob/main/LICENSE).
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## Copyright
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Copyright OpenSearch Contributors. See [NOTICE](https://github.com/opensearch-project/neural-search/blob/main/NOTICE) for details.
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config.json
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config.json
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{
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"_name_or_path": "opensearch-project/opensearch-neural-sparse-encoding-v1",
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"architectures": [
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"BertForMaskedLM"
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"torchscript": true,
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"transformers_version": "4.18.0",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 30522
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}
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config_sentence_transformers.json
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config_sentence_transformers.json
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{
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"model_type": "SparseEncoder",
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"__version__": {
|
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"sentence_transformers": "5.0.0",
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"transformers": "4.50.3",
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"pytorch": "2.6.0+cu124"
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},
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"prompts": {
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"query": "",
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"document": ""
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},
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"default_prompt_name": null,
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"similarity_fn_name": "dot"
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}
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model.safetensors
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:9642a9d3ea8b8a31212847d960f8baa01e87a72e18dcb95705aa84335d4e8851
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size 531848824
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modules.json
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modules.json
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[
|
||||
{
|
||||
"idx": 0,
|
||||
"name": "0",
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"path": "",
|
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"type": "sentence_transformers.sparse_encoder.models.MLMTransformer"
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},
|
||||
{
|
||||
"idx": 1,
|
||||
"name": "1",
|
||||
"path": "1_SpladePooling",
|
||||
"type": "sentence_transformers.sparse_encoder.models.SpladePooling"
|
||||
}
|
||||
]
|
||||
3
pytorch_model.bin
Normal file
3
pytorch_model.bin
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:a359b09196aa613e875df91fe3d6a3b94e10e0d21e840b392ff3027e36ac97af
|
||||
size 531889705
|
||||
4
sentence_bert_config.json
Normal file
4
sentence_bert_config.json
Normal file
@@ -0,0 +1,4 @@
|
||||
{
|
||||
"max_seq_length": 512,
|
||||
"do_lower_case": false
|
||||
}
|
||||
7
special_tokens_map.json
Normal file
7
special_tokens_map.json
Normal file
@@ -0,0 +1,7 @@
|
||||
{
|
||||
"cls_token": "[CLS]",
|
||||
"mask_token": "[MASK]",
|
||||
"pad_token": "[PAD]",
|
||||
"sep_token": "[SEP]",
|
||||
"unk_token": "[UNK]"
|
||||
}
|
||||
30672
tokenizer.json
Normal file
30672
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
55
tokenizer_config.json
Normal file
55
tokenizer_config.json
Normal file
@@ -0,0 +1,55 @@
|
||||
{
|
||||
"added_tokens_decoder": {
|
||||
"0": {
|
||||
"content": "[PAD]",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100": {
|
||||
"content": "[UNK]",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"101": {
|
||||
"content": "[CLS]",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"102": {
|
||||
"content": "[SEP]",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"103": {
|
||||
"content": "[MASK]",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"clean_up_tokenization_spaces": true,
|
||||
"cls_token": "[CLS]",
|
||||
"do_lower_case": true,
|
||||
"mask_token": "[MASK]",
|
||||
"model_max_length": 512,
|
||||
"pad_token": "[PAD]",
|
||||
"sep_token": "[SEP]",
|
||||
"strip_accents": null,
|
||||
"tokenize_chinese_chars": true,
|
||||
"tokenizer_class": "BertTokenizer",
|
||||
"unk_token": "[UNK]"
|
||||
}
|
||||
Reference in New Issue
Block a user