Question Answering
Transformers
PyTorch
English
bert
DocVQA
Document Question Answering
Document Visual Question Answering
Instructions to use rubentito/bert-large-mpdocvqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rubentito/bert-large-mpdocvqa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="rubentito/bert-large-mpdocvqa")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("rubentito/bert-large-mpdocvqa") model = AutoModelForQuestionAnswering.from_pretrained("rubentito/bert-large-mpdocvqa") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- da00d76664d3a2607908aa688de17190b170818556430ee2590edd1d0796bb7f
- Size of remote file:
- 1.34 GB
- SHA256:
- cbef6fc136134027b326e22c7ebe061443c64140f42c75d6e156560aecbab94d
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