Instructions to use ydshieh/tiny-random-LiltForTokenClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ydshieh/tiny-random-LiltForTokenClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ydshieh/tiny-random-LiltForTokenClassification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ydshieh/tiny-random-LiltForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("ydshieh/tiny-random-LiltForTokenClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ydshieh/tiny-random-LiltForTokenClassification: direct link, hf CLI and curl.
- Browser
- Download file 293 kB
-
https://huggingface.co/ydshieh/tiny-random-LiltForTokenClassification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ydshieh/tiny-random-LiltForTokenClassification/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/ydshieh/tiny-random-LiltForTokenClassification/resolve/main/pytorch_model.bin
293 kB
- Xet hash:
- e2f8aed576989f6fe87273ddce17e76a5745b88cf86538955513d58b026c439e
- Size of remote file:
- 293 kB
- SHA256:
- 11b3ba2e4491cb2f4230e92a5695211280f335c54de56d93cdb92a362a57d7b8
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