Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use HAOUHAT/legal-doctrine-Coding-Challenge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HAOUHAT/legal-doctrine-Coding-Challenge with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HAOUHAT/legal-doctrine-Coding-Challenge")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HAOUHAT/legal-doctrine-Coding-Challenge") model = AutoModelForSequenceClassification.from_pretrained("HAOUHAT/legal-doctrine-Coding-Challenge") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8f24f4d4bf9082fd67688879fb9dcf93c8dff7da3d21b1a8d0536de4a20d409d
- Size of remote file:
- 268 MB
- SHA256:
- 954c8e415010e13600397e134fbcc531b222721765f7d4e513357157c8e27cf7
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