Instructions to use impira/layoutlm-document-qa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use impira/layoutlm-document-qa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="impira/layoutlm-document-qa")# Load model directly from transformers import AutoTokenizer, AutoModelForDocumentQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("impira/layoutlm-document-qa") model = AutoModelForDocumentQuestionAnswering.from_pretrained("impira/layoutlm-document-qa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from impira/layoutlm-document-qa: direct link, hf CLI and curl.
- Browser
- Download file 779 Bytes
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https://huggingface.co/impira/layoutlm-document-qa/resolve/73a21c855abce87699f47515f48cf35ee357a451/README.md
- Command line
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hf download hf://impira/layoutlm-document-qa@73a21c855abce87699f47515f48cf35ee357a451/README.md
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curl -L -o README.md https://huggingface.co/impira/layoutlm-document-qa/resolve/73a21c855abce87699f47515f48cf35ee357a451/README.md
779 Bytes
metadata
language: en
thumbnail: >-
https://uploads-ssl.webflow.com/5e3898dff507782a6580d710/614a23fcd8d4f7434c765ab9_logo.png
license: mit
LayoutLM for Visual Question Answering
This is a fine-tuned version of the multi-modal LayoutLM model for the task of question answering on documents. It has been fine-tuned on
Model details
The LayoutLM model was developed at Microsoft (paper) as a general purpose tool for understanding documents. This model is a fine-tuned checkpoint of LayoutLM-Base-Cased, using both the SQuAD2.0 and DocVQA datasets.