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huolongshe b8ff8b4a25 | 2 months ago | |
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app | 3 months ago | |
demo_data | 3 months ago | |
docs | 3 months ago | |
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Dockerfile | 3 months ago | |
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README.md | 2 months ago | |
application.yml | 3 months ago | |
build-docker.sh | 3 months ago | |
pack_model.py | 3 months ago | |
pip-install-reqs.sh | 3 months ago | |
requirements.txt | 3 months ago | |
run_model_server.py | 3 months ago |
Visual language data such as plots, charts, and infographics are ubiquitous in the human world. However, state-of-the-art visionlanguage models do not perform well on these data. We propose MATCHA (Math reasoning and Chart derendering pretraining) to enhance visual language models’ capabilities jointly modeling charts/plots and language data. Specifically we propose several pretraining tasks that cover plot deconstruction and numerical reasoning which are the key capabilities in visual language modeling. We perform the MATCHA pretraining starting from Pix2Struct, a recently proposed imageto-text visual language model. On standard benchmarks such as PlotQA and ChartQA, MATCHA model outperforms state-of-the-art methods by as much as nearly 20%. We also examine how well MATCHA pretraining transfers to domains such as screenshot, textbook diagrams, and document figures and observe overall improvement, verifying the usefulness of MATCHA pretraining on broader visual language tasks.
模型来源: https://hf-mirror.com/google/matcha-plotqa-v1
本模型基于 ServiceBoot微服务引擎 进行服务化封装,参见: 《CubeAI模型开发指南》
$ sh pip-install-reqs.sh
$ serviceboot start
或
$ python3 run_model_server.py
一键式本地容器化部署和运行,参见: 《CubeAI模型独立部署指南》 或 CubeAI Docker Builder
本模型服务可一键发布至 CubeAI智立方平台 进行共享和部署,参见: 《CubeAI模型发布指南》
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