Instructions to use tiennvcs/bert-base-uncased-finetuned-infovqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tiennvcs/bert-base-uncased-finetuned-infovqa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="tiennvcs/bert-base-uncased-finetuned-infovqa")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("tiennvcs/bert-base-uncased-finetuned-infovqa") model = AutoModelForQuestionAnswering.from_pretrained("tiennvcs/bert-base-uncased-finetuned-infovqa") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 501578509849d761b318757f2a288481807e59bd4e694a3681d2f6566c3a6ca9
- Size of remote file:
- 2.67 kB
- SHA256:
- cd6145954f8486e30e66f1a425586fddab0550ef58759cf09c6b06b024af0b61
路
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