Instructions to use piEsposito/braquad-bert-qna with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use piEsposito/braquad-bert-qna with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="piEsposito/braquad-bert-qna")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("piEsposito/braquad-bert-qna") model = AutoModelForQuestionAnswering.from_pretrained("piEsposito/braquad-bert-qna", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4075840b2c0fde3c88d65b5f1d198d86b90716a15b84ccde4479f6ff2d049bc3
- Size of remote file:
- 433 MB
- SHA256:
- 8b565d1bf4de20caad07acb8e46a3f0fac5111dfd0eeb9af0183d75102dc2d7a
路
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