Instructions to use VARabi/ALDi-Token-DI-50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VARabi/ALDi-Token-DI-50 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="VARabi/ALDi-Token-DI-50")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("VARabi/ALDi-Token-DI-50") model = AutoModelForTokenClassification.from_pretrained("VARabi/ALDi-Token-DI-50", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from VARabi/ALDi-Token-DI-50: direct link, hf CLI and curl.
- Browser
- Download file 3.52 kB
-
https://huggingface.co/VARabi/ALDi-Token-DI-50/resolve/main/training_args.bin
- Command line
-
hf download hf://VARabi/ALDi-Token-DI-50/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/VARabi/ALDi-Token-DI-50/resolve/main/training_args.bin
3.52 kB
- Xet hash:
- b72ac989dabeb1b1e07b69f9feb4cd56f804574944996c8a7f57c66a95846fc0
- Size of remote file:
- 3.52 kB
- SHA256:
- d3892a71c4fa561c79fbb3c15946bbd13e2b4a48d56eaecc50358c9ba85f0ba9
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