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:
- 306174f2c91c86834bc0319fef7a3f98e41a1c36ba4f39a0489c4eb2d2bd1e40
- Size of remote file:
- 15.9 MB
- SHA256:
- a0e8810117a094e916b2a5650af8950e5a66a73a0527f4c23357efe53116e352
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