Upload folder using huggingface_hub
Browse files- README.md +48 -0
- config.json +192 -0
- generation_config.json +8 -0
- model.safetensors +3 -0
- model.safetensors.index.json +823 -0
- preprocessor_config.json +27 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +6 -0
- tokenizer.json +0 -0
- tokenizer_config.json +205 -0
README.md
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---
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license: cc-by-4.0
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tags:
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- vision
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- nougat
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pipeline_tag: image-to-text
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---
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# Nougat model, small-sized version
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Nougat model trained on PDF-to-markdown. It was introduced in the paper [Nougat: Neural Optical Understanding for Academic Documents](https://arxiv.org/abs/2308.13418) by Blecher et al. and first released in [this repository](https://github.com/facebookresearch/nougat/tree/main).
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Disclaimer: The team releasing Nougat did not write a model card for this model so this model card has been written by the Hugging Face team.
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Note: this model corresponds to the "0.1.0-small" version of the original repository.
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## Model description
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Nougat is a [Donut](https://huggingface.co/docs/transformers/model_doc/donut) model trained to transcribe scientific PDFs into an easy-to-use markdown format. The model consists of a Swin Transformer as vision encoder, and an mBART model as text decoder.
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The model is trained to autoregressively predict the markdown given only the pixels of the PDF image as input.
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/nougat_architecture.jpg"
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alt="drawing" width="600"/>
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<small> Nougat high-level overview. Taken from the <a href="https://arxiv.org/abs/2308.13418">original paper</a>. </small>
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## Intended uses & limitations
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You can use the raw model for transcribing a PDF into Markdown. See the [model hub](https://huggingface.co/models?search=nougat) to look for other
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fine-tuned versions that may interest you.
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### How to use
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We refer to the [docs](https://huggingface.co/docs/transformers/main/en/model_doc/nougat).
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### BibTeX entry and citation info
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```bibtex
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@misc{blecher2023nougat,
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title={Nougat: Neural Optical Understanding for Academic Documents},
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author={Lukas Blecher and Guillem Cucurull and Thomas Scialom and Robert Stojnic},
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year={2023},
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eprint={2308.13418},
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archivePrefix={arXiv},
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primaryClass={cs.LG}
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}
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```
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config.json
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{
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"architectures": [
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"VisionEncoderDecoderModel"
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],
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"decoder": {
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"_name_or_path": "",
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"activation_dropout": 0.0,
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"activation_function": "gelu",
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"add_cross_attention": true,
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 0,
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"eos_token_id": 2,
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"pad_token_id": 1,
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"transformers_version": "4.34.0.dev0"
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:e4dc450183a0929f98d5359dfb3bda9d2234ffa87378d36febb1cfdb9fdcd82f
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size 310482872
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model.safetensors.index.json
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