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 pytorch_model.bin from VARabi/ALDi-Token-DI: direct link, hf CLI and curl.
- Browser
- Download file 649 MB
-
https://huggingface.co/VARabi/ALDi-Token-DI/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://VARabi/ALDi-Token-DI/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/VARabi/ALDi-Token-DI/resolve/main/pytorch_model.bin
649 MB
- Xet hash:
- d0cf77e1bde58ea865f77ff86eefdccb7ac9512e23ba33dd58263792c5a8108e
- Size of remote file:
- 649 MB
- SHA256:
- 7ff85dba579bca3aa2ae6faff5086f9422af9dfca51f08c249c6ddf177bc7771
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.