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JLake310
/
roberta-large-topic-classification

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

Instructions to use JLake310/roberta-large-topic-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use JLake310/roberta-large-topic-classification with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="JLake310/roberta-large-topic-classification")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("JLake310/roberta-large-topic-classification")
    model = AutoModelForSequenceClassification.from_pretrained("JLake310/roberta-large-topic-classification")
  • Notebooks
  • Google Colab
  • Kaggle
roberta-large-topic-classification
2.69 GB
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  • 1 contributor
History: 3 commits
SFconvertbot's picture
SFconvertbot
Adding `safetensors` variant of this model
c3f564f almost 3 years ago
  • .gitattributes
    1.48 kB
    initial commit almost 3 years ago
  • config.json
    1.12 kB
    model init almost 3 years ago
  • model.safetensors
    1.35 GB
    xet
    Adding `safetensors` variant of this model almost 3 years ago
  • pytorch_model.bin
    1.35 GB
    xet
    model init almost 3 years ago
  • special_tokens_map.json
    156 Bytes
    model init almost 3 years ago
  • tokenizer.json
    495 kB
    model init almost 3 years ago
  • tokenizer_config.json
    582 Bytes
    model init almost 3 years ago
  • vocab.txt
    248 kB
    model init almost 3 years ago