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 # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="rubentito/bert-large-mpdocvqa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("rubentito/bert-large-mpdocvqa") model = AutoModelForQuestionAnswering.from_pretrained("rubentito/bert-large-mpdocvqa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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| [T5 base](https://huggingface.co/rubentito/t5-base-mpdocvqa) | rubentito/t5-base-mpdocvqa | 0.5050 | 0.0000 |
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| Hi-VT5 | TBA | 0.6201 | 79.23 |
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##
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```tex
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@article{tito2022hierarchical,
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| [T5 base](https://huggingface.co/rubentito/t5-base-mpdocvqa) | rubentito/t5-base-mpdocvqa | 0.5050 | 0.0000 |
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| Hi-VT5 | TBA | 0.6201 | 79.23 |
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## Citation Information
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```tex
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@article{tito2022hierarchical,
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