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philschmid
/
MiniLM-L6-H384-uncased-sst2

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

Instructions to use philschmid/MiniLM-L6-H384-uncased-sst2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use philschmid/MiniLM-L6-H384-uncased-sst2 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="philschmid/MiniLM-L6-H384-uncased-sst2")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("philschmid/MiniLM-L6-H384-uncased-sst2")
    model = AutoModelForSequenceClassification.from_pretrained("philschmid/MiniLM-L6-H384-uncased-sst2")
  • Notebooks
  • Google Colab
  • Kaggle
MiniLM-L6-H384-uncased-sst2
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  • 1 contributor
History: 3 commits
philschmid's picture
philschmid
Delete .DS_Store
0c0ecdc almost 5 years ago
  • .gitattributes
    1.18 kB
    initial commit almost 5 years ago
  • all_results.json
    789 Bytes
    model version almost 5 years ago
  • config.json
    710 Bytes
    model version almost 5 years ago
  • eval_results.json
    346 Bytes
    model version almost 5 years ago
  • pytorch_model.bin
    90.9 MB
    xet
    model version almost 5 years ago
  • special_tokens_map.json
    112 Bytes
    model version almost 5 years ago
  • tokenizer.json
    466 kB
    model version almost 5 years ago
  • tokenizer_config.json
    512 Bytes
    model version almost 5 years ago
  • train_results.json
    463 Bytes
    model version almost 5 years ago
  • vocab.txt
    232 kB
    model version almost 5 years ago