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Akshat131
/
splade-multi-static-pruned-v2

Feature Extraction
Transformers
Safetensors
English
bert
fill-mask
splade
sparse-retrieval
information-retrieval
beir
code-search
static-query
distilled
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use Akshat131/splade-multi-static-pruned-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Akshat131/splade-multi-static-pruned-v2 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="Akshat131/splade-multi-static-pruned-v2")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForMaskedLM
    
    tokenizer = AutoTokenizer.from_pretrained("Akshat131/splade-multi-static-pruned-v2")
    model = AutoModelForMaskedLM.from_pretrained("Akshat131/splade-multi-static-pruned-v2", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
splade-multi-static-pruned-v2
184 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 7 commits
Akshat131's picture
Akshat131
Update README.md with evaluation benchmark results
b05da47 verified 9 days ago
  • .gitattributes
    1.52 kB
    initial commit 9 days ago
  • README.md
    5.13 kB
    Update README.md with evaluation benchmark results 9 days ago
  • config.json
    1.12 kB
    Upload trained 3-layer static SPLADE model with evaluation benchmark stats 9 days ago
  • model.safetensors
    183 MB
    xet
    Upload trained 3-layer static SPLADE model with evaluation benchmark stats 9 days ago
  • static_query_weights.pt
    369 kB
    xet
    Upload trained 3-layer static SPLADE model with evaluation benchmark stats 9 days ago
  • tokenizer.json
    711 kB
    Upload trained 3-layer static SPLADE model with evaluation benchmark stats 9 days ago
  • tokenizer_config.json
    633 Bytes
    Upload trained 3-layer static SPLADE model with evaluation benchmark stats 9 days ago