Instructions to use Patcas/distilbert-3class-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Patcas/distilbert-3class-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Patcas/distilbert-3class-classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Patcas/distilbert-3class-classification") model = AutoModelForSequenceClassification.from_pretrained("Patcas/distilbert-3class-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Patcas/distilbert-3class-classification: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/Patcas/distilbert-3class-classification/resolve/main/training_args.bin
- Command line
-
hf download hf://Patcas/distilbert-3class-classification/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Patcas/distilbert-3class-classification/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- ee92de98da4958f8e65a7fa48f378fd14a487db8d12923e4c88d81993e5b2cdd
- Size of remote file:
- 5.3 kB
- SHA256:
- 06fd29c4f5f02944a8bd7440e9a326c543cfeed38a121ccfc476e2bc3ee0fb9c
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