Instructions to use Cournane/roberta-base-labels-Hat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cournane/roberta-base-labels-Hat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Cournane/roberta-base-labels-Hat")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Cournane/roberta-base-labels-Hat") model = AutoModelForSequenceClassification.from_pretrained("Cournane/roberta-base-labels-Hat") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- be48e8a2be398deb65c901e808901db338d3a6d6e358471782e9592ee42d9bda
- Size of remote file:
- 499 MB
- SHA256:
- 433fe6bb9fbe567dc7301587e719bca2d549a15715fa2f8d8dfe5c1a39f1cf2c
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