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  library_name: transformers
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- tags: []
 
 
 
 
 
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  ---
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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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- ## Model Details
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- ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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-
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- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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- ## Uses
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-
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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- ### Direct Use
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
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- [More Information Needed]
 
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- ### Downstream Use [optional]
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
 
 
 
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- [More Information Needed]
 
 
 
 
 
 
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- ### Out-of-Scope Use
 
 
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
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- [More Information Needed]
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  ## Bias, Risks, and Limitations
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  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- [More Information Needed]
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- ## Training Details
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- ### Training Data
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- <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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- [More Information Needed]
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- ### Training Procedure
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- [More Information Needed]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- #### Speeds, Sizes, Times [optional]
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- [More Information Needed]
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- ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- <!-- This should link to a Dataset Card if possible. -->
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- [More Information Needed]
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- [More Information Needed]
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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- [More Information Needed]
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
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- [More Information Needed]
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- ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- [More Information Needed]
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- ### Compute Infrastructure
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- #### Hardware
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- #### Software
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- ## Citation [optional]
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- <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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- **BibTeX:**
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- **APA:**
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- ## Glossary [optional]
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- <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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- [More Information Needed]
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- ## More Information [optional]
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- ## Model Card Authors [optional]
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- ## Model Card Contact
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- [More Information Needed]
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  library_name: transformers
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+ license: gpl-3.0
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+ language:
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+ - ar
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+ - en
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+ pipeline_tag: image-to-text
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+ pretty_name: Arabic Small Nougat
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  ---
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+ # Arabic Small Nougat
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+ **Sma**ll, **Simp**le **En**d-**t**o-**En**d **Structur**ed **OC**R **fo**r **Arab**ic **boo**ks.
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+ ## How to Get Started with the Model
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+ Use the code below to get started with the model.
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+ ```python
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+ from PIL import Image
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+ import torch
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+ from transformers import (
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+ NougatProcessor,
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+ VisionEncoderDecoderModel,
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+ )
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ processor = NougatProcessor.from_pretrained("MohamedRashad/arabic-small-nougat")
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+ model = VisionEncoderDecoderModel.from_pretrained("MohamedRashad/arabic-small-nougat")
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+ device = "cuda" if torch.cuda.is_available() else "cpu"
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+ model.to(device)
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+ def predict(image):
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+ # prepare PDF image for the model
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+ image = Image.open(image)
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+ pixel_values = processor(image, return_tensors="pt").pixel_values
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+ # generate transcription (here we only generate 30 tokens)
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+ outputs = model.generate(
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+ pixel_values.to(device),
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+ min_length=1,
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+ max_new_tokens=2048,
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+ bad_words_ids=[[processor.tokenizer.unk_token_id]],
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+ )
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+ page_sequence = processor.batch_decode(outputs, skip_special_tokens=True)[0]
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+ page_sequence = processor.post_process_generation(page_sequence, fix_markdown=False)
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+ return page_sequence
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+ print(predict("path/to/page_image.jpg"))
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+ ```
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  ## Bias, Risks, and Limitations
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  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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+ ## Model Details
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+ - **Developed by:** Mohamed Rashad
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+ - **Model type:** VisionEncoderDecoderModel
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+ - **Language(s) (NLP):** Arabic & English
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+ - **License:** GPL 3.0
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+ - **Finetuned from model:** [nougat-small](https://huggingface.co/facebook/nougat-small)