Instructions to use clairedhx/NLP_projet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use clairedhx/NLP_projet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="clairedhx/NLP_projet")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("clairedhx/NLP_projet") model = AutoModelForTokenClassification.from_pretrained("clairedhx/NLP_projet", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3ea6d002c4d778525d74faf8c790ad767e99cb9582314167c10982ceee5e4650
- Size of remote file:
- 4.92 kB
- SHA256:
- 67c7a1a37aed2af73ecc5ec04405e3d84c7e738fbc6745f598b4fe4d140bf23d
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