### Model Description
DocModel is a document understanding model built on the RoBERTa architecture. It captures both textual content and 2D spatial relationships, making it ideal for tasks that require processing complex document layouts, such as forms, tables, and scanned documents.

Developed by: Oluwatobi Adefami, Madison May

Model type: Document Understanding (Information Extraction)

License: Apache-2.0

Model Sources

Repository: https://github.com/Tobiadefami/docmodel


### Uses
DocModel can be directly used for document processing, form understanding, and entity extraction from structured and semi-structured documents.

### Out-of-Scope Use
Not recommended for tasks that involve purely textual data without layout components or heavily distorted document scans.

### Bias, Risks, and Limitations
DocModel’s performance may degrade on highly noisy or poorly structured documents, such as extreme distortions or low-resolution scans.

### Recommendations
Users should be mindful of the model’s limitations, particularly in handling documents with severe layout inconsistencies.

How to Get Started with the Model
``` python
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("tobiadefami/docmodel-base")
model = AutoModel.from_pretrained("tobiadefami/docmodel-base")

# Example usage
inputs = tokenizer("Your document text here...", return_tensors="pt")
outputs = model(**inputs)
``` 
### Evaluation

##### Metrics

Eval Loss: 1.36752

F1-Score: 0.84126

### Results

DocModel has been evaluated on the FUNSD dataset for information extraction tasks, demonstrating competitive performance in both loss and F1-score.