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tyatabe
/
nli_sv

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

Instructions to use tyatabe/nli_sv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use tyatabe/nli_sv with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="tyatabe/nli_sv")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("tyatabe/nli_sv")
    model = AutoModelForSequenceClassification.from_pretrained("tyatabe/nli_sv")
  • Notebooks
  • Google Colab
  • Kaggle
nli_sv
2.26 GB
Ctrl+K
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  • 1 contributor
History: 3 commits
tyatabe's picture
tyatabe
Upload tokenizer
07ae1f0 over 3 years ago
  • .gitattributes
    1.44 kB
    Upload tokenizer over 3 years ago
  • README.md
    21 Bytes
    initial commit over 3 years ago
  • config.json
    994 Bytes
    Upload XLMRobertaForSequenceClassification over 3 years ago
  • pytorch_model.bin

    Detected Pickle imports (4)

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

    What is a pickle import?

    2.24 GB
    xet
    Upload XLMRobertaForSequenceClassification over 3 years ago
  • special_tokens_map.json
    280 Bytes
    Upload tokenizer over 3 years ago
  • tokenizer.json
    17.1 MB
    xet
    Upload tokenizer over 3 years ago
  • tokenizer_config.json
    452 Bytes
    Upload tokenizer over 3 years ago