Awarebeyond
/

receipt-donut

@@ -1,199 +1,84 @@
 ---
-library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
 ## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- 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. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-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).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

 ---
+language:
+- en
+tags:
+- image-to-text
+- document-ai
+- donut
+- receipt-extraction
+pipeline_tag: image-to-text
+widget:
+  - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/receipt.jpg
+    example_title: Sample Receipt
 ---
+# Receipt Donut (Fine-tuned Document UI)
+This model extracts structured JSON data directly from receipt images without needing a separate OCR engine. Fine-tuned on the `naver-clova-ix/donut-base-finetuned-cord-v2` base model.
 ## Model Details
+- **Architecture:** Donut (Document Understanding Transformer)
+- **Task:** Image-to-JSON extraction
+- **Extracted Fields:** `merchant`, `date`, `subtotal`, `tax`, `total`, `address`
+- **Training Data:** 8,615 heavily augmented receipt images sourced from 8 diverse public datasets (CORD, WildReceipts, SROIE variants, etc.)
+## Try it out!
+Use the **Hosted Inference API** widget on the right.
+Drag and drop any receipt image, and it will output a JSON string with the extracted fields.
+## How to Use (Python)
+### Installation
+```bash
+pip install transformers Pillow torch
+```
+### Inference Code (Single & Batch)
+```python
+import torch
+from transformers import DonutProcessor, VisionEncoderDecoderModel
+from PIL import Image
+# 1. Load Model & Processor
+repo_id = "YOUR_HF_USERNAME/receipt-donut-v1"
+processor = DonutProcessor.from_pretrained(repo_id)
+model = VisionEncoderDecoderModel.from_pretrained(repo_id)
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model.to(device)
+def process_receipts(image_paths):
+    images = [Image.open(path).convert("RGB") for path in image_paths]
+    # Prepare inputs
+    pixel_values = processor(images, return_tensors="pt").pixel_values.to(device)
+    # Prepare decoder prompt
+    task_prompt = "<s_cord-v2>"
+    decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids
+    decoder_input_ids = decoder_input_ids.repeat(len(images), 1).to(device)
+    # Generate
+    outputs = model.generate(
+        pixel_values,
+        decoder_input_ids=decoder_input_ids,
+        max_length=model.decoder.config.max_position_embeddings,
+        pad_token_id=processor.tokenizer.pad_token_id,
+        eos_token_id=processor.tokenizer.eos_token_id,
+        use_cache=True,
+        bad_words_ids=[[processor.tokenizer.unk_token_id]],
+        return_dict_in_generate=True,
+    )
+    # Decode
+    results = []
+    for seq in processor.tokenizer.batch_decode(outputs.sequences):
+        seq = seq.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "")
+        seq = seq.split("<s_cord-v2>", 1)[-1].strip()
+        results.append(processor.token2json(seq))
+    return results
+# Run inference
+predictions = process_receipts(["receipt1.jpg", "receipt2.jpg"])
+print(predictions)
+```