Instructions to use Drigoro/distilbert_normal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Drigoro/distilbert_normal with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Drigoro/distilbert_normal")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Drigoro/distilbert_normal") model = AutoModelForSequenceClassification.from_pretrained("Drigoro/distilbert_normal", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Drigoro/distilbert_normal: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/Drigoro/distilbert_normal/resolve/main/training_args.bin
- Command line
-
hf download hf://Drigoro/distilbert_normal/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Drigoro/distilbert_normal/resolve/main/training_args.bin
5.11 kB
- Xet hash:
- 0a776156ce994ab280a5b2d066326e1a511cb4b2be8c440ec8523910f7c8c6c4
- Size of remote file:
- 5.11 kB
- SHA256:
- bf8ef393ebc02b60c668a44195d4b6742e3c1df5f3469dc575547ab48819c242
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