Instructions to use hf-tiny-model-private/tiny-random-BartForQuestionAnswering 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-BartForQuestionAnswering 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-BartForQuestionAnswering")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-BartForQuestionAnswering") model = AutoModelForQuestionAnswering.from_pretrained("hf-tiny-model-private/tiny-random-BartForQuestionAnswering", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 00350b4710f257a230628d96266a1debaceaa92ab76f8c7a5fcb26d947f78514
- Size of remote file:
- 119 kB
- SHA256:
- 61614fec07593ef7ad5e98c9e2f237e7a6316856933ddf35a7d200dcfd1e132e
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