lakshya-rawat
/

document-qa-model

@@ -1,199 +1,123 @@
 ---
 library_name: transformers
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 ---
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 ---
 library_name: transformers
+tags:
+- document-question-answering
+- layoutlmv3
+- ocr
+- document-understanding
+- paddleocr
+- multilingual
+- layout-aware
+- lakshya-singh
+license: apache-2.0
+language:
+- en
+base_model:
+- microsoft/layoutlmv3-base
+datasets:
+- nielsr/docvqa_1200_examples
 ---
+# Document QA Model
+This is a fine-tuned **document question-answering model** based on `layoutlmv3-base`. It is trained to understand documents using OCR data (via PaddleOCR) and accurately answer questions related to structured information in the document layout.
+---
 ## Model Details
 ### Model Description
+- **Model Name:** `document-qa-model`
+- **Base Model:** [`microsoft/layoutlmv3-base`](https://huggingface.co/microsoft/layoutlmv3-base)
+- **Fine-tuned by:** Lakshya Singh (solo contributor)
+- **Languages:** English, Spanish, Chinese
+- **License:** Apache-2.0 (inherited from base model)
+- **Intended Use:** Extract answers to structured queries from scanned documents
+- **Not funded** — this project was completed independently.
+---
+## Model Sources
+- **Repository:** [https://github.com/Lakshyasinghrawat12]
+- **Trained on:** Adapted version of [`nielsr/docvqa_1200_examples`](https://huggingface.co/datasets/nielsr/docvqa_1200_examples)
+- **Model metrics:** See ![training_history.png](https://cdn-uploads.huggingface.co/production/uploads/66a7331438fbd9075584523f/MtMe5CZy3wb2nEG1wTRMc.png)
+---
 ## Uses
 ### Direct Use
+This model can be used for:
+- Question Answering on document images (PDFs, invoices, utility bills)
+- Information extraction tasks using OCR and layout-aware understanding
 ### Out-of-Scope Use
+- Not suitable for conversational QA
+- Not suitable for images with no OCR-processed text
+---
 ## Training Details
+### Dataset
+The dataset consisted of:
+- **Images** of utility bills and documents
+- **OCR data** with bounding boxes (from PaddleOCR)
+- **Queries** in English, Spanish, and Chinese
+- **Answer spans** with match scores and positions
 ### Training Procedure
+- Preprocessing: PaddleOCR was used to extract tokens, positions, and structure
+- Model: LayoutLMv3-base
+- Epochs: 4
+- Learning rate schedule: Shown in image below
+### Training Metrics
+- **F1 Score** (validation): ![training_history.png](https://cdn-uploads.huggingface.co/production/uploads/66a7331438fbd9075584523f/MtMe5CZy3wb2nEG1wTRMc.png)
+- **Loss & Learning Rate Chart**: ![training_history.png](https://cdn-uploads.huggingface.co/production/uploads/66a7331438fbd9075584523f/MtMe5CZy3wb2nEG1wTRMc.png)
+---
 ## Evaluation
+### Metrics Used
+- F1 score
+- Match score of predicted spans
+- Token overlap vs ground truth
+### Summary
+The model performs well on document-style QA tasks, especially with:
+- Clearly structured OCR results
+- Document types similar to utility bills, invoices, and forms
+---
+## How to Use
+```python
+from transformers import LayoutLMv3Processor, LayoutLMv3ForQuestionAnswering
+from PIL import Image
+import torch
+processor = LayoutLMv3Processor.from_pretrained("lakshya-singh/document-qa-model")
+model = LayoutLMv3ForQuestionAnswering.from_pretrained("lakshya-singh/document-qa-model")
+image = Image.open("your_document.png")
+question = "What is the total amount due?"
+inputs = processor(image, question, return_tensors="pt")
+outputs = model(**inputs)
+start_idx = torch.argmax(outputs.start_logits)
+end_idx = torch.argmax(outputs.end_logits)
+answer = processor.tokenizer.decode(inputs["input_ids"][0][start_idx:end_idx+1])
+print("Answer:", answer)