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clapAI
/
roberta-large-multilingual-sentiment

Text Classification
Transformers
TensorBoard
Safetensors
xlm-roberta
sentiment
multilingual
modernbert
sentiment-analysis
product-reviews
place-reviews
text-embeddings-inference
Model card Files Files and versions
xet
Metrics Training metrics Community

Instructions to use clapAI/roberta-large-multilingual-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use clapAI/roberta-large-multilingual-sentiment with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="clapAI/roberta-large-multilingual-sentiment")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("clapAI/roberta-large-multilingual-sentiment")
    model = AutoModelForSequenceClassification.from_pretrained("clapAI/roberta-large-multilingual-sentiment")
  • Notebooks
  • Google Colab
  • Kaggle
roberta-large-multilingual-sentiment
1.14 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 9 commits
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hungnm
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  • runs
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  • .gitattributes
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  • README.md
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  • all_results.json
    677 Bytes
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  • config.json
    946 Bytes
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  • eval_results.json
    273 Bytes
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  • model.safetensors
    1.12 GB
    xet
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  • special_tokens_map.json
    280 Bytes
    Training in progress, epoch 1 over 1 year ago
  • test_results.json
    257 Bytes
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  • tokenizer.json
    17.1 MB
    xet
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  • tokenizer_config.json
    1.27 kB
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  • train_results.json
    169 Bytes
    Model save over 1 year ago
  • trainer_state.json
    537 kB
    Model save over 1 year ago
  • training_args.bin
    6.9 kB
    xet
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