Image-to-Text
Transformers
PyTorch
Safetensors
vision-encoder-decoder
image-text-to-text
vision
nougat
Instructions to use facebook/nougat-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/nougat-base with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="facebook/nougat-base")# Load model directly from transformers import AutoTokenizer, AutoModelForImageTextToText tokenizer = AutoTokenizer.from_pretrained("facebook/nougat-base") model = AutoModelForImageTextToText.from_pretrained("facebook/nougat-base") - Notebooks
- Google Colab
- Kaggle
Update README.md (#3)
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license: apache-2.0
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tags:
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- vision
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pipeline_tag: image-to-text
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---
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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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## 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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license: apache-2.0
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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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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-base" 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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