Instructions to use hf-tiny-model-private/tiny-random-LongformerModel 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-LongformerModel 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-LongformerModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-LongformerModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-LongformerModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 60438a8a04a17a350c24f9aff913320acc1129eec731f58202d8d100e51ec339
- Size of remote file:
- 415 kB
- SHA256:
- f825bf1c614fc1342972ce5a9cbdb16f3d9a0358cd09d26ee15bbe0d3eed5325
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