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:
- 1392ec6117dd8a4e83bde08619d03aca369bdea5255948033ded6d81effc42c0
- Size of remote file:
- 1.78 kB
- SHA256:
- c40436438061ccf66a8957fdcd8c98609c5fdaf1de16d8f8494299fe29d11a06
路
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