Instructions to use Patcas/distilbert-3ssclass-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Patcas/distilbert-3ssclass-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Patcas/distilbert-3ssclass-classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Patcas/distilbert-3ssclass-classification") model = AutoModelForSequenceClassification.from_pretrained("Patcas/distilbert-3ssclass-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Patcas/distilbert-3ssclass-classification: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/Patcas/distilbert-3ssclass-classification/resolve/main/training_args.bin
- Command line
-
hf download hf://Patcas/distilbert-3ssclass-classification/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Patcas/distilbert-3ssclass-classification/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- 8e845192c3e157175d721d4c5fc97150efbd4c1aeb20d85bbd152691b4d7b742
- Size of remote file:
- 5.3 kB
- SHA256:
- 9769f73c594d2f639d95340fd69dca57fb61f5ae3d998a0cb3f993bc625495de
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