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:
- 3c82cdfe0d781f63357e7f2921e23d3a11fdfc43a3d16b8db4facb91577f7d68
- Size of remote file:
- 436 MB
- SHA256:
- 59e9b8c477b6c6c54ce0f42d58e03077e042520972bbd3e67268796e213245ca
路
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