Instructions to use google/matcha-chart2text-pew with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/matcha-chart2text-pew with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="google/matcha-chart2text-pew")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("google/matcha-chart2text-pew") model = AutoModelForImageTextToText.from_pretrained("google/matcha-chart2text-pew") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -57,6 +57,8 @@ This model was originally contributed by Fangyu Liu, Francesco Piccinno et al. a
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# Citation
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@misc{liu2022matcha,
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title={MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering},
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author={Fangyu Liu and Francesco Piccinno and Syrine Krichene and Chenxi Pang and Kenton Lee and Mandar Joshi and Yasemin Altun and Nigel Collier and Julian Martin Eisenschlos},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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# Citation
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If you want to cite this work, please consider citing the original paper:
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```
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@misc{liu2022matcha,
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title={MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering},
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author={Fangyu Liu and Francesco Piccinno and Syrine Krichene and Chenxi Pang and Kenton Lee and Mandar Joshi and Yasemin Altun and Nigel Collier and Julian Martin Eisenschlos},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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