Instructions to use BWayne/LILT_mr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BWayne/LILT_mr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="BWayne/LILT_mr")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("BWayne/LILT_mr") model = AutoModelForTokenClassification.from_pretrained("BWayne/LILT_mr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from BWayne/LILT_mr: direct link, hf CLI and curl.
- Browser
- Download file 521 MB
-
https://huggingface.co/BWayne/LILT_mr/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://BWayne/LILT_mr/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/BWayne/LILT_mr/resolve/main/pytorch_model.bin
521 MB
- Xet hash:
- c95785bc8249b69d8e6cd12837973a17fcaecd66f6feabf8464e692a9bc23302
- Size of remote file:
- 521 MB
- SHA256:
- c98aa9264f0113515d64cd4fb044afcec6ba83c2e7e717a00099156791f0ee8f
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