Instructions to use VARabi/Sentence-ALDi-50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VARabi/Sentence-ALDi-50 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="VARabi/Sentence-ALDi-50")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("VARabi/Sentence-ALDi-50") model = AutoModelForSequenceClassification.from_pretrained("VARabi/Sentence-ALDi-50", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from VARabi/Sentence-ALDi-50: direct link, hf CLI and curl.
- Browser
- Download file 3.52 kB
-
https://huggingface.co/VARabi/Sentence-ALDi-50/resolve/main/training_args.bin
- Command line
-
hf download hf://VARabi/Sentence-ALDi-50/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/VARabi/Sentence-ALDi-50/resolve/main/training_args.bin
3.52 kB
- Xet hash:
- 653a4dac61faaa25a7e0a9dc9fcbebfa1630c05b67d0a580285f5c967d747b35
- Size of remote file:
- 3.52 kB
- SHA256:
- d40cfd843c416ec9c00e520d86ae666cba0a4f454166084558a0fdafafc46857
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