Instructions to use VARabi/Sentence-ALDi-30 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VARabi/Sentence-ALDi-30 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="VARabi/Sentence-ALDi-30")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("VARabi/Sentence-ALDi-30") model = AutoModelForSequenceClassification.from_pretrained("VARabi/Sentence-ALDi-30", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from VARabi/Sentence-ALDi-30: direct link, hf CLI and curl.
- Browser
- Download file 3.52 kB
-
https://huggingface.co/VARabi/Sentence-ALDi-30/resolve/main/training_args.bin
- Command line
-
hf download hf://VARabi/Sentence-ALDi-30/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/VARabi/Sentence-ALDi-30/resolve/main/training_args.bin
3.52 kB
- Xet hash:
- 768565f0ceba579ed664bbd66188a321fbdb54e6251ecd90916954bd64df972e
- Size of remote file:
- 3.52 kB
- SHA256:
- d04fb60ffb9d02de3ae35f0fec2ad0543a5704872ba9d432f2c2bf599bd00c6c
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