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dcferreira
/
detoxify-optimized

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

Instructions to use dcferreira/detoxify-optimized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use dcferreira/detoxify-optimized with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="dcferreira/detoxify-optimized")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("dcferreira/detoxify-optimized")
    model = AutoModelForSequenceClassification.from_pretrained("dcferreira/detoxify-optimized")
  • Notebooks
  • Google Colab
  • Kaggle
detoxify-optimized
Ctrl+K
Ctrl+K
  • 2 contributors
History: 6 commits
Daniel Ferreira
use top_k instead of return_all_scores
327bec4 almost 3 years ago
  • .gitattributes
    1.48 kB
    initial commit almost 3 years ago
  • .gitignore
    18 Bytes
    first commit almost 3 years ago
  • README.md
    1.99 kB
    use top_k instead of return_all_scores almost 3 years ago
  • config.json
    1.49 kB
    add config.json almost 3 years ago
  • evaluate.py
    8.31 kB
    first commit almost 3 years ago
  • model_optimized.onnx
    567 MB
    xet
    add model weights almost 3 years ago
  • requirements.txt
    127 Bytes
    first commit almost 3 years ago
  • sentencepiece.bpe.model
    5.07 MB
    xet
    add model weights almost 3 years ago
  • special_tokens_map.json
    279 Bytes
    add model weights almost 3 years ago
  • tokenizer.json
    17.1 MB
    add model weights almost 3 years ago
  • tokenizer_config.json
    443 Bytes
    add model weights almost 3 years ago