Instructions to use VARabi/ALDi-Token-DI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VARabi/ALDi-Token-DI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="VARabi/ALDi-Token-DI")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("VARabi/ALDi-Token-DI") model = AutoModelForTokenClassification.from_pretrained("VARabi/ALDi-Token-DI", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from VARabi/ALDi-Token-DI: direct link, hf CLI and curl.
- Browser
- Download file 3.52 kB
-
https://huggingface.co/VARabi/ALDi-Token-DI/resolve/main/training_args.bin
- Command line
-
hf download hf://VARabi/ALDi-Token-DI/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/VARabi/ALDi-Token-DI/resolve/main/training_args.bin
3.52 kB
- Xet hash:
- b13497919bbda36e37b173ee66b58bc7400edcd39bf387214acdd65faa49facd
- Size of remote file:
- 3.52 kB
- SHA256:
- 4bb3243e713170949466998e8a474a592be4c2bf750567ed9f5e8fd61388dc92
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