Update README.md

README.md

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----
-library_name: transformers
-tags: []
----
+# 📄 LayoutLMv3 Fine-Tuned on FUNSD for Key-Value Pair Extraction
 
-# Model Card for Model ID
+## Model Details
 
-<!-- Provide a quick summary of what the model is/does. -->
+**Developed by:** nnul
+**Model type:** LayoutLMv3 (`microsoft/layoutlmv3-base`)
+**Language(s):** English
+**License:** Apache 2.0
+**Fine-tuned from:** [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base)
 
+This model is a fine-tuned version of LayoutLMv3 on the [FUNSD](https://huggingface.co/datasets/nielsr/funsd-layoutlmv3) dataset. It has been trained for the task of **form understanding**, specifically **token classification** for extracting structured information from scanned forms (e.g., questions and answers in a key-value format).
 
+---
 
-## Model Details
+## Model Description
 
-### Model Description
+The model performs token-level classification, labeling each token as one of:
 
-<!-- Provide a longer summary of what this model is. -->
+* `QUESTION`
+* `ANSWER`
+* `HEADER`
+* `O` (other)
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+It takes as input a scanned form image and its OCR-extracted tokens and bounding boxes.
 
-- **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]
+## Model Sources
 
-<!-- Provide the basic links for the model. -->
+* **Dataset:** [nielsr/funsd-layoutlmv3](https://huggingface.co/datasets/nielsr/funsd-layoutlmv3)
+* **Base model:** [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base)
 
-- **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]
+* Key-value pair extraction from scanned documents
+* Form understanding
+* Preprocessing step for document-based QA, autofill, or RPA systems
 
-### Downstream Use [optional]
+### Downstream Use
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+* Automating information extraction from forms
+* Fine-tuning on custom form datasets (insurance, tax, invoices, etc.)
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+* Documents not structured like forms
+* Non-English documents (was not trained on multilingual data)
+* Highly noisy OCR (e.g., handwriting)
 
-[More Information Needed]
+---
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+* Biased toward the structure and layout of FUNSD forms (U.S.-centric, clean typewritten documents).
+* May perform poorly on handwritten or low-quality scans.
+* Assumes accurate OCR input.
 
-[More Information Needed]
+---
 
-### Recommendations
+## How to Get Started
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+```python
+from transformers import LayoutLMv3Processor, LayoutLMv3ForTokenClassification
+from PIL import Image
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+# Load model and processor
+model = LayoutLMv3ForTokenClassification.from_pretrained("nnul/layoutlmv3-finetuned-funsd")
+processor = LayoutLMv3Processor.from_pretrained("nnul/layoutlmv3-finetuned-funsd")
 
-## How to Get Started with the Model
+# Load and prepare image + OCR tokens and boxes
+image = Image.open("your_form.jpg").convert("RGB")
+words = ["Name", ":", "John", "Doe"]
+boxes = [[100,100,150,120], [155,100,160,120], [165,100,220,120], [225,100,270,120]]
 
-Use the code below to get started with the model.
+encoding = processor(image, words, boxes=boxes, return_tensors="pt")
+outputs = model(**encoding)
+predictions = outputs.logits.argmax(-1)
+```
 
-[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]
+* [FUNSD Dataset](https://huggingface.co/datasets/nielsr/funsd-layoutlmv3)
+* \~199 forms, annotated with token-level BIO labels
 
+### Training Hyperparameters
 
-#### Training Hyperparameters
+* Epochs: 7
+* Learning rate: default
+* Batch size: 2
+* Optimizer: AdamW
+* Training time: \~5 minutes on A100 (Colab)
 
-- **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. -->
-
-                precision   recall   f1-score   support
-
-    ANSWER        0.90      0.93      0.92       817
-    HEADER        0.67      0.64      0.66       119
-    QUESTION      0.91      0.94      0.93      1077
-
-    micro avg     0.90      0.92      0.91      2013
-    macro avg     0.83      0.84      0.83      2013
-    weighted avg  0.89      0.92      0.91      2013
-
+| Label            | Precision | Recall   | F1-Score | Support |
+| ---------------- | --------- | -------- | -------- | ------- |
+| ANSWER           | 0.90      | 0.93     | 0.92     | 817     |
+| HEADER           | 0.67      | 0.64     | 0.66     | 119     |
+| QUESTION         | 0.91      | 0.94     | 0.93     | 1077    |
+| **Micro Avg**    | **0.90**  | **0.92** | **0.91** | 2013    |
+| **Macro Avg**    | 0.83      | 0.84     | 0.83     | 2013    |
+| **Weighted Avg** | 0.89      | 0.92     | 0.91     | 2013    |
 
-[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
+| Parameter      | Value                   |
+| -------------- | ----------------------- |
+| Hardware Used  | NVIDIA A100 GPU (Colab) |
+| Training Time  | \~5 minutes             |
+| Cloud Provider | Google Colab            |
+| Carbon Emitted | Negligible              |
 
-[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]
+## Citation
+
+```
+@misc{layoutlmv3-funsd,
+  title={LayoutLMv3 Fine-tuned on FUNSD},
+  author={nnul},
+  year={2025},
+  howpublished={\url{https://huggingface.co/your-username/layoutlmv3-finetuned-funsd}},
+  note={Fine-tuned LayoutLMv3 for key-value extraction from forms}
+}
+```