Instructions to use hf-tiny-model-private/tiny-random-AlbertForTokenClassification 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-AlbertForTokenClassification 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-AlbertForTokenClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-AlbertForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-tiny-model-private/tiny-random-AlbertForTokenClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 60e494836d5d26598e6e72c2eb328d712ed5b7234643bb0d5a2bdef73e37a111
- Size of remote file:
- 15.9 MB
- SHA256:
- 352c458dc04d0d4e1e76007e0f1d790c409e6b00c061d96a245ff4f1b81144a2
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