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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0e4c067a742adabaf9e49d96d48771374056ed727ea5ff0fd3f4a6f3d4b82f8f
- Size of remote file:
- 558 kB
- SHA256:
- 6c5f8d4e1b8930f694690bf1c0b676c87f40cc44d147dd4134d73b143bcbb828
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