Instructions to use hf-tiny-model-private/tiny-random-LiltForTokenClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-LiltForTokenClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hf-tiny-model-private/tiny-random-LiltForTokenClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-LiltForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-tiny-model-private/tiny-random-LiltForTokenClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c836bd2ecae34111c7a628cb33230cc3fe7ac23a8049703104d77905c570b49a
- Size of remote file:
- 294 kB
- SHA256:
- c5ca346dc09e6c7a4ad6a7cf2af22444e842c58021245df9956d6c6e2a6b23ce
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