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Kenpache
/
flame2

Text Classification
Transformers
Safetensors
PyTorch
xlm-roberta
finance
sentiment-analysis
financial-sentiment-analysis
multilingual
financial-nlp
perspective-aware
stock-market
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use Kenpache/flame2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Kenpache/flame2 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="Kenpache/flame2")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("Kenpache/flame2")
    model = AutoModelForSequenceClassification.from_pretrained("Kenpache/flame2")
  • Notebooks
  • Google Colab
  • Kaggle
flame2
2.26 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 8 commits
Kenpache's picture
Kenpache
Upload README.md with huggingface_hub
d078b7c verified about 2 months ago
  • .gitattributes
    1.57 kB
    Upload tokenizer.json with huggingface_hub about 2 months ago
  • README.md
    11.4 kB
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  • config.json
    970 Bytes
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  • model.safetensors
    2.24 GB
    xet
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  • tokenizer.json
    16.8 MB
    xet
    Upload tokenizer.json with huggingface_hub about 2 months ago
  • tokenizer_config.json
    314 Bytes
    Upload tokenizer_config.json with huggingface_hub about 2 months ago