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GOT-OCR-2.0-hf
阶跃星辰General OCR Theory: Towards OCR-2.0 via a Unified End-to-end Model - HF Transformers 🤗 implementation GOT-OCR2 works on a wide range of tasks, including plain document OCR, scene text OCR, formatted document OCR, and even OCR for tables, charts, mathematical formulas, geometric shapes, molecular formulas and sheet music. While this implementation of the model will only output plain text, the outputs can be further processed to render the desired format, with packages like `pdftex`, `mathpix`, `matplotlib`, `tikz`, `verovio` or `pyecharts`. The model can also be used for interactive OCR, where the user can specify the region to be recognized by providing the coordinates or the color of the region's bounding box. This model was contributed by yonigozlan. The original code can be found here.
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阶跃星辰Inference using Huggingface transformers on NVIDIA GPUs. Requirements tested on python 3.10： More details about 'ocr_type', 'ocr_box', 'ocr_color', and 'render' can be found at our GitHub. Our training codes are available at our GitHub. If you find our work helpful, please consider citing our papers 📝 and liking this project ❤️！
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GottBERTGottBERT is the first German-only RoBERTa model, pre-trained on the German portion of the first released OSCAR dataset. This model aims to provide enhanced natural language processing (NLP) performance for the German language across various tasks, including Named Entity Recognition (NER), text classification, and natural language inference (NLI). GottBERT has been developed in two versions: a **base model** and a **large model**, tailored specifically for German-language tasks. GottBERT was evaluated across various downstream tasks: - **NER**: CoNLL 2003, GermEval 2014 - **Text Classification**: GermEval 2018 (coarse & fine), 10kGNAD - **NLI**: German subset of XNLI Details: - **bold** values indicate the best performing model within one architecure (base, large), undescored values the second best.
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