Instructions to use hf-tiny-model-private/tiny-random-LongformerForTokenClassification 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-LongformerForTokenClassification 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-LongformerForTokenClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-LongformerForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-tiny-model-private/tiny-random-LongformerForTokenClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8aae41f06db2d2fc4b8000df3d89db3d376d4c8bad84efb4d8f3d6073238b5b5
- Size of remote file:
- 412 kB
- SHA256:
- 0e4b196893fff24d5eb303502dd602e14134a6a9630c4b56b2a44b8271afe3eb
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