Instructions to use hf-tiny-model-private/tiny-random-LongformerForQuestionAnswering 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-LongformerForQuestionAnswering with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="hf-tiny-model-private/tiny-random-LongformerForQuestionAnswering")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-LongformerForQuestionAnswering") model = AutoModelForQuestionAnswering.from_pretrained("hf-tiny-model-private/tiny-random-LongformerForQuestionAnswering", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7454f3b673850f70fb73df574ee82fa74e02dea6d6e50bb00e597533268a9851
- Size of remote file:
- 437 kB
- SHA256:
- 50780174c6081e72af51f057fb36356b2b5871ba07093cc263c16d2bb8a90a54
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