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- library_name: transformers
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- tags: []
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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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- - **Model type:** [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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- ### 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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- ## Uses
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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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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical 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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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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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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- ### 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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- ### 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 [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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  ---
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+ license: cc-by-nc-4.0
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+ inference: false
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+ base_model: naver-clova-ix/donut-base
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+ tags:
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+ - donut
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+ - image-to-text
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+ - vision
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+ model-index:
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+ - name: donut-receipts-extract
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+ results:
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+ - task:
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+ type: image-to-text
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+ name: Image to text
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+ metrics:
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+ - type: loss
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+ value: 0.326069
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+ - type: accuracy
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+ value: 0.895219
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+ name: Accuracy
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+ - type: cer
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+ value: 0.158358
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+ name: CER
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+ - type: wer
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+ value: 1.673989
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+ name: WER
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+ - type: edit distance
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+ value: 0.145293
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+ name: Edit_distance
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+ metrics:
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+ - cer
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+ - wer
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+ - accuracy
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+ datasets:
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+ - AdamCodd/donut-receipts
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+ pipeline_tag: image-to-text
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  ---
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+ # Note
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+ This model was forked from [AdamCodd/donut-receipts-extract](https://huggingface.co/AdamCodd/donut-receipts-extract).
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+
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+ # Donut-receipts-extract
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+ Donut model was introduced in the paper [OCR-free Document Understanding Transformer](https://arxiv.org/abs/2111.15664) by Geewok et al. and first released in [this repository](https://github.com/clovaai/donut).
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+ ## === V2 ===
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+ This model has been retrained on an improved version of the [AdamCodd/donut-receipts](https://huggingface.co/datasets/AdamCodd/donut-receipts) dataset (deduplicated, manually corrected). The new license for the V2 model is **cc-by-nc-4.0**. For commercial use rights, please contact me ([email protected]). Meanwhile, the V1 model remains available under the MIT license (under v1 branch).
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+ It achieves the following results on the evaluation set:
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+ * Loss: 0.326069
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+ * Edit distance: 0.145293
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+ * CER: 0.158358
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+ * WER: 1.673989
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+ * Mean accuracy: 0.895219
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+ * F1: 0.977897
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+
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+ The task_prompt has been changed to ``<s_receipt>`` for the V2 (previously ``<s_cord-v2>`` for V1). Two new keys ``<s_svc>`` and ``<s_discount>`` have been added, ``<s_telephone>`` has been renamed to ``<s_phone>``.
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+ The V2 performs way better than the V1 as it has been trained on twice the resolution for the receipts, using a better dataset. Despite that, it's not perfect due to a lack of diverse receipts (the training dataset is still ~1100 receipts); for a future version, that will be the main focus.
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+
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+ ## === V1 ====
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+
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+ This model is a finetune of the [donut base model](https://huggingface.co/naver-clova-ix/donut-base/) on the [AdamCodd/donut-receipts](https://huggingface.co/datasets/AdamCodd/donut-receipts) dataset. Its purpose is to efficiently extract text from receipts.
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+ It achieves the following results on the evaluation set:
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+ * Loss: 0.498843
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+ * Edit distance: 0.198315
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+ * CER: 0.213929
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+ * WER: 7.634032
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+ * Mean accuracy: 0.843472
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+
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+ ## Model description
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+
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+ Donut consists of a vision encoder (Swin Transformer) and a text decoder (BART). Given an image, the encoder first encodes the image into a tensor of embeddings (of shape batch_size, seq_len, hidden_size), after which the decoder autoregressively generates text, conditioned on the encoding of the encoder.
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+ ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/donut_architecture.jpg)
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+ ### How to use
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+ ```python
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+ import torch
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+ import re
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+ from PIL import Image
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+ from transformers import DonutProcessor, VisionEncoderDecoderModel
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+
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+ device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
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+ processor = DonutProcessor.from_pretrained("AdamCodd/donut-receipts-extract")
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+ model = VisionEncoderDecoderModel.from_pretrained("AdamCodd/donut-receipts-extract")
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+ model.to(device)
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+
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+ def load_and_preprocess_image(image_path: str, processor):
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+ """
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+ Load an image and preprocess it for the model.
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+ """
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+ image = Image.open(image_path).convert("RGB")
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+ pixel_values = processor(image, return_tensors="pt").pixel_values
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+ return pixel_values
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+
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+ def generate_text_from_image(model, image_path: str, processor, device):
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+ """
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+ Generate text from an image using the trained model.
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+ """
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+ # Load and preprocess the image
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+ pixel_values = load_and_preprocess_image(image_path, processor)
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+ pixel_values = pixel_values.to(device)
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+
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+ # Generate output using model
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+ model.eval()
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+ with torch.no_grad():
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+ task_prompt = "<s_receipt>" # <s_cord-v2> for v1
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+ decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids
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+ decoder_input_ids = decoder_input_ids.to(device)
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+ generated_outputs = model.generate(
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+ pixel_values,
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+ decoder_input_ids=decoder_input_ids,
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+ max_length=model.decoder.config.max_position_embeddings,
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+ pad_token_id=processor.tokenizer.pad_token_id,
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+ eos_token_id=processor.tokenizer.eos_token_id,
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+ early_stopping=True,
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+ bad_words_ids=[[processor.tokenizer.unk_token_id]],
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+ return_dict_in_generate=True
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+ )
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+
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+ # Decode generated output
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+ decoded_text = processor.batch_decode(generated_outputs.sequences)[0]
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+ decoded_text = decoded_text.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "")
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+ decoded_text = re.sub(r"<.*?>", "", decoded_text, count=1).strip() # remove first task start token
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+ decoded_text = processor.token2json(decoded_text)
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+ return decoded_text
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+
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+ # Example usage
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+ image_path = "path_to_your_image" # Replace with your image path
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+ extracted_text = generate_text_from_image(model, image_path, processor, device)
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+ print("Extracted Text:", extracted_text)
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+ ```
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+
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+ Refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/donut) for more code examples.
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+
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+ ## Intended uses & limitations
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+
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+ This fine-tuned model is specifically designed for extracting text from receipts and may not perform optimally on other types of documents. The dataset used is still suboptimal (numerous errors are still there) so this model will need to be retrained at a later date to improve its performance.
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 3e-05
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+ - train_batch_size: 2
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+ - eval_batch_size: 4
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+ - seed: 42
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+ - optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_steps: 300
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+ - num_epochs: 35
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+ - weight_decay: 0.01
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+
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+ ### Framework versions
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+
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+ - Transformers 4.36.2
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+ - Datasets 2.16.1
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+ - Tokenizers 0.15.0
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+ - Evaluate 0.4.1
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+
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+ If you want to support me, you can [here](https://ko-fi.com/adamcodd).
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+ ### BibTeX entry and citation info
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+ ```bibtex
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+ @article{DBLP:journals/corr/abs-2111-15664,
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+ author = {Geewook Kim and
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+ Teakgyu Hong and
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+ Moonbin Yim and
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+ Jinyoung Park and
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+ Jinyeong Yim and
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+ Wonseok Hwang and
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+ Sangdoo Yun and
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+ Dongyoon Han and
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+ Seunghyun Park},
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+ title = {Donut: Document Understanding Transformer without {OCR}},
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+ journal = {CoRR},
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+ volume = {abs/2111.15664},
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+ year = {2021},
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+ url = {https://arxiv.org/abs/2111.15664},
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+ eprinttype = {arXiv},
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+ eprint = {2111.15664},
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+ timestamp = {Thu, 02 Dec 2021 10:50:44 +0100},
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+ biburl = {https://dblp.org/rec/journals/corr/abs-2111-15664.bib},
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+ bibsource = {dblp computer science bibliography, https://dblp.org}
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+ }
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+ ```