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HomeLLMssplade cocondenser ensembledistil

splade cocondenser ensembledistil

by naver

Open source · 395k downloads · 61 likes

2.2
(61 reviews)EmbeddingAPI & Local
About

This model, named SPLADE CoCondenser EnsembleDistil, is a sparse encoder designed for semantic search and information retrieval. It converts sentences or paragraphs into sparse vectors of 30,522 dimensions, enabling efficient similarity-based search. Its architecture is based on the CoCondenser model, optimized to capture fine-grained semantic relationships in texts. It excels particularly in information retrieval tasks where the precision and relevance of results are crucial. What sets it apart is its ability to combine the power of dense representations with the efficiency of sparse approaches, offering a balance between performance and interpretability.

Documentation

SPLADE CoCondenser EnsembleDistil

SPLADE model for passage retrieval. For additional details, please visit:

  • paper: https://arxiv.org/abs/2205.04733
  • code: https://github.com/naver/splade
MRR@10 (MS MARCO dev)R@1000 (MS MARCO dev)
splade-cocondenser-ensembledistil38.398.3

Model Details

This is a SPLADE Sparse Encoder model. It maps sentences & paragraphs to a 30522-dimensional sparse vector space and can be used for semantic search and sparse retrieval.

Model Description

  • Model Type: SPLADE Sparse Encoder
  • Base model: Luyu/co-condenser-marco
  • Maximum Sequence Length: 512 tokens (256 for evaluation reproduction)
  • Output Dimensionality: 30522 dimensions
  • Similarity Function: Dot Product

Full Model Architecture

CSS
SparseEncoder(
  (0): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM 
  (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

Bash
pip install -U sentence-transformers

Then you can load this model and run inference.

Python
from sentence_transformers import SparseEncoder

# Download from the 🤗 Hub
model = SparseEncoder("naver/splade-cocondenser-ensembledistil")
# Run inference
queries = ["what causes aging fast"]
documents = [
    "UV-A light, specifically, is what mainly causes tanning, skin aging, and cataracts, UV-B causes sunburn, skin aging and skin cancer, and UV-C is the strongest, and therefore most effective at killing microorganisms. Again â\x80\x93 single words and multiple bullets.",
    "Answers from Ronald Petersen, M.D. Yes, Alzheimer's disease usually worsens slowly. But its speed of progression varies, depending on a person's genetic makeup, environmental factors, age at diagnosis and other medical conditions. Still, anyone diagnosed with Alzheimer's whose symptoms seem to be progressing quickly â\x80\x94 or who experiences a sudden decline â\x80\x94 should see his or her doctor.",
    "Bell's palsy and Extreme tiredness and Extreme fatigue (2 causes) Bell's palsy and Extreme tiredness and Hepatitis (2 causes) Bell's palsy and Extreme tiredness and Liver pain (2 causes) Bell's palsy and Extreme tiredness and Lymph node swelling in children (2 causes)",
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 30522] [3, 30522]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[ 9.9933, 10.8691,  3.4265]])

Citation

If you use our checkpoint, please cite our work:

INI
@misc{https://doi.org/10.48550/arxiv.2205.04733,
  doi = {10.48550/ARXIV.2205.04733},
  url = {https://arxiv.org/abs/2205.04733},
  author = {Formal, Thibault and Lassance, Carlos and Piwowarski, Benjamin and Clinchant, Stéphane},
  keywords = {Information Retrieval (cs.IR), Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective},
  publisher = {arXiv},
  year = {2022},
  copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
}
Capabilities & Tags
sentence-transformerspytorchbertspladequery-expansiondocument-expansionbag-of-wordspassage-retrievalknowledge-distillationsparse-encoder
Links & Resources
Specifications
CategoryEmbedding
AccessAPI & Local
LicenseOpen Source
PricingOpen Source
Rating
2.2

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