Instructions to use hf-tiny-model-private/tiny-random-EsmForTokenClassification 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-EsmForTokenClassification 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-EsmForTokenClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-EsmForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-tiny-model-private/tiny-random-EsmForTokenClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 07ac06ea40049e7f7c0d8aca4aebc4fd2408f788c675778af827c84c1e867e22
- Size of remote file:
- 220 kB
- SHA256:
- 8be69f138c50470b5139382eea3ca75828b4c27f51e637504a9b41962b48fb2a
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