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 pytorch_model.bin from VARabi/ALDi-Token-DI-50: direct link, hf CLI and curl.
- Browser
- Download file 649 MB
-
https://huggingface.co/VARabi/ALDi-Token-DI-50/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://VARabi/ALDi-Token-DI-50/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/VARabi/ALDi-Token-DI-50/resolve/main/pytorch_model.bin
649 MB
- Xet hash:
- 6eeaf7c1572b2a4610edc20328cab68c8a4a85f47649a084434cf20328cc564c
- Size of remote file:
- 649 MB
- SHA256:
- 1ca22d8bccf5ff16b91400d5d45124d6a0192993dfa619d5a2e4da8ab464352f
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