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:
- aaac6229719c7ea83ba255f813581b8e79f73a11dea980eb0882ed4c7ef8170d
- Size of remote file:
- 557 kB
- SHA256:
- 60858805a59bb05224382faeb9a3f1e4843b6563c2ece5b81acd642b081de80e
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