Instructions to use chitra/distilbert-negation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chitra/distilbert-negation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="chitra/distilbert-negation")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("chitra/distilbert-negation") model = AutoModelForSequenceClassification.from_pretrained("chitra/distilbert-negation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Upload tf_model.h5 with git-lfs
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Upload vocab.txt
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Upload tokenizer_config.json
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Upload pytorch_model.bin with git-lfs
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Upload config.json
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Upload added_tokens.json
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initial commit
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