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dpv
/
roberta-minipile-tokenizer

Feature Extraction
Transformers
PyTorch
roberta
text-embeddings-inference
Model card Files Files and versions
xet
Community
1

Instructions to use dpv/roberta-minipile-tokenizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use dpv/roberta-minipile-tokenizer with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="dpv/roberta-minipile-tokenizer")
    # Load model directly
    from transformers import AutoTokenizer, AutoModel
    
    tokenizer = AutoTokenizer.from_pretrained("dpv/roberta-minipile-tokenizer")
    model = AutoModel.from_pretrained("dpv/roberta-minipile-tokenizer")
  • Notebooks
  • Google Colab
  • Kaggle
roberta-minipile-tokenizer
502 MB
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  • 1 contributor
History: 3 commits
dpv's picture
dpv
Upload tokenizer
7aadeff almost 3 years ago
  • .gitattributes
    1.52 kB
    initial commit almost 3 years ago
  • config.json
    631 Bytes
    Upload model almost 3 years ago
  • merges.txt
    463 kB
    Upload tokenizer almost 3 years ago
  • pytorch_model.bin

    Detected Pickle imports (4)

    • "torch._utils._rebuild_tensor_v2",
    • "torch.FloatStorage",
    • "collections.OrderedDict",
    • "torch.LongStorage"

    What is a pickle import?

    499 MB
    xet
    Upload model almost 3 years ago
  • special_tokens_map.json
    957 Bytes
    Upload tokenizer almost 3 years ago
  • tokenizer.json
    2.12 MB
    Upload tokenizer almost 3 years ago
  • tokenizer_config.json
    1.39 kB
    Upload tokenizer almost 3 years ago
  • vocab.json
    806 kB
    Upload tokenizer almost 3 years ago