Instructions to use VARabi/Sentence-ALDi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VARabi/Sentence-ALDi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="VARabi/Sentence-ALDi")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("VARabi/Sentence-ALDi") model = AutoModelForSequenceClassification.from_pretrained("VARabi/Sentence-ALDi", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from VARabi/Sentence-ALDi: direct link, hf CLI and curl.
- Browser
- Download file 3.52 kB
-
https://huggingface.co/VARabi/Sentence-ALDi/resolve/main/training_args.bin
- Command line
-
hf download hf://VARabi/Sentence-ALDi/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/VARabi/Sentence-ALDi/resolve/main/training_args.bin
3.52 kB
- Xet hash:
- e44d2600da585b510f5295fe134b2d5556955d03863f616897d84628e19c7388
- Size of remote file:
- 3.52 kB
- SHA256:
- bbc8625719231c0cb60062ac046e1d8ffa4236ab87e5315c7b722394df689979
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