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Intel
/
bert-mini-sst2-distilled-sparse-90-1X4-block

Text Classification
Transformers
PyTorch
ONNX
bert
text-embeddings-inference
Model card Files Files and versions
xet
Community
1

Instructions to use Intel/bert-mini-sst2-distilled-sparse-90-1X4-block with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Intel/bert-mini-sst2-distilled-sparse-90-1X4-block with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="Intel/bert-mini-sst2-distilled-sparse-90-1X4-block")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("Intel/bert-mini-sst2-distilled-sparse-90-1X4-block")
    model = AutoModelForSequenceClassification.from_pretrained("Intel/bert-mini-sst2-distilled-sparse-90-1X4-block")
  • Notebooks
  • Google Colab
  • Kaggle
bert-mini-sst2-distilled-sparse-90-1X4-block
116 MB
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  • 1 contributor
History: 8 commits
BoDong's picture
BoDong
add the int8 onnx model and int8 Neural Engine IR
9f940d7 over 3 years ago
  • sparse_int8_ir
    add the int8 onnx model and int8 Neural Engine IR over 3 years ago
  • .gitattributes
    1.34 kB
    initial commit almost 4 years ago
  • README.md
    621 Bytes
    add change almost 4 years ago
  • config.json
    844 Bytes
    First model version almost 4 years ago
  • int8-model.onnx
    35.3 MB
    xet
    add the int8 onnx model and int8 Neural Engine IR over 3 years ago
  • pytorch_model.bin
    44.7 MB
    xet
    First model version almost 4 years ago
  • special_tokens_map.json
    112 Bytes
    First model version almost 4 years ago
  • tokenizer.json
    711 kB
    First model version almost 4 years ago
  • tokenizer_config.json
    360 Bytes
    First model version almost 4 years ago
  • vocab.txt
    232 kB
    First model version almost 4 years ago