File size: 1,133 Bytes
e2d9688
 
 
adda8d8
5db87a1
adda8d8
 
 
 
 
 
 
 
 
5db87a1
 
adda8d8
 
5db87a1
adda8d8
 
5db87a1
adda8d8
5db87a1
adda8d8
 
5db87a1
adda8d8
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
---
{}
---
# DONUT Merit

<a href="https://x.com/nearcyan/status/1706914605262684394">
  <div style="text-align: center;">
    <picture>
      <source media="(prefers-color-scheme: dark)" srcset="https://huggingface.co/de-Rodrigo/donut-merit/resolve/main/assets/dragon_huggingface.png">
      <source media="(prefers-color-scheme: light)" srcset="https://huggingface.co/de-Rodrigo/donut-merit/resolve/main/assets/dragon_huggingface.png">
      <img alt="DragonHuggingFace" src="https://huggingface.co/de-Rodrigo/donut-merit/resolve/main/assets/dragon_huggingface.png" style="width: 200px;">
    </picture>
  </div>
</a>


## Model Architecture
**This model is based on the Donut architecture and fine-tuned on the Merit dataset for form understanding tasks.**

- Backbone: [Donut](https://huggingface.co/naver-clova-ix/donut-base)
- Training Data: [Merit](https://huggingface.co/datasets/de-Rodrigo/merit)

## Example Usage

```python
from transformers import AutoProcessor, AutoModel

processor = AutoProcessor.from_pretrained("de-Rodrigo/donut-merit")
model = AutoModel.from_pretrained("de-Rodrigo/donut-merit")
```
**WIP** 🛠️