Instructions to use hf-tiny-model-private/tiny-random-AlbertModel 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-AlbertModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-tiny-model-private/tiny-random-AlbertModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-AlbertModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-AlbertModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4ae36d25a4a1f424396d1ede0d4c9e84e7d07ea26077e53fde0502a602bb68cb
- Size of remote file:
- 15.9 MB
- SHA256:
- 187b3a0468bc0a370bb576d48fe1bd1f7f879f2fd41aae739f052ddd4e77c845
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