Instructions to use google/matcha-plotqa-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/matcha-plotqa-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="google/matcha-plotqa-v2")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("google/matcha-plotqa-v2") model = AutoModelForImageTextToText.from_pretrained("google/matcha-plotqa-v2") - Notebooks
- Google Colab
- Kaggle
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# Model card for MatCha - fine-tuned on PlotQA-v2 dataset
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This model is the MatCha model, fine-tuned on plotQA-v2 dataset. This fine-tuned checkpoint might be better suited for plots question answering tasks.
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# Model card for MatCha - fine-tuned on PlotQA-v2 dataset
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/matcha_architecture.jpg"
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alt="drawing" width="600"/>
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This model is the MatCha model, fine-tuned on plotQA-v2 dataset. This fine-tuned checkpoint might be better suited for plots question answering tasks.
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