Instructions to use hf-tiny-model-private/tiny-random-LukeForTokenClassification 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-LukeForTokenClassification 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-LukeForTokenClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-LukeForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-tiny-model-private/tiny-random-LukeForTokenClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0902e2c837e88778a94335441066638bae0e3387721971a68362d9d664c12337
- Size of remote file:
- 6.78 MB
- SHA256:
- bbaad52c489886c1908a6b48b6f6bbff6caec62f21ad1cc64b213fea088ebea7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.