---
base_model: microsoft/dit-base-finetuned-rvlcdip
tags:
- image-classification
- vision
- generated_from_trainer
metrics:
- f1
model-index:
- name: donut-base-beans
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# donut-base-beans

This model is a fine-tuned version of [microsoft/dit-base-finetuned-rvlcdip](https://huggingface.co/microsoft/dit-base-finetuned-rvlcdip) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0404
- F1: 0.6134

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000

### Training results

| Training Loss | Epoch  | Step | F1     | Validation Loss |
|:-------------:|:------:|:----:|:------:|:---------------:|
| 0.1165        | 0.0126 | 50   | 0.4177 | 0.0642          |
| 0.0942        | 0.0252 | 100  | 0.4772 | 0.0485          |
| 0.1076        | 0.0379 | 150  | 0.4643 | 0.0584          |
| 0.1103        | 0.0505 | 200  | nan    | 0.0446          |
| 0.0873        | 0.0631 | 250  | 0.5313 | 0.0518          |
| 0.1053        | 0.0757 | 300  | 0.5329 | 0.0736          |
| 0.0797        | 0.0884 | 350  | 0.5326 | 0.0726          |
| 0.0857        | 0.1010 | 400  | 0.5498 | 0.0693          |
| 0.0885        | 0.1136 | 450  | nan    | 0.0917          |
| 0.102         | 0.1262 | 500  | 0.5649 | 0.0580          |
| 0.0716        | 0.1389 | 550  | 0.5381 | 0.0797          |
| 0.0854        | 0.1515 | 600  | 0.5718 | 0.0744          |
| 0.089         | 0.1641 | 650  | 0.5790 | 0.0504          |
| 0.0721        | 0.1767 | 700  | 0.5727 | 0.0618          |
| 0.0721        | 0.1893 | 750  | 0.5904 | 0.0703          |
| 0.0865        | 0.2020 | 800  | 0.5953 | 0.0588          |
| 0.0767        | 0.2146 | 850  | 0.5918 | 0.0437          |
| 0.0773        | 0.2272 | 900  | 0.5957 | 0.0568          |
| 0.0748        | 0.2398 | 950  | 0.5942 | 0.0465          |
| 0.0761        | 0.2525 | 1000 | nan    | 0.0660          |
| 0.0855        | 0.2651 | 1050 | 0.5964 | 0.0491          |
| 0.0832        | 0.2777 | 1100 | 0.6048 | 0.0498          |
| 0.0821        | 0.2903 | 1150 | 0.6032 | 0.0597          |
| 0.0715        | 0.3030 | 1200 | nan    | 0.0643          |
| 0.085         | 0.3156 | 1250 | 0.6054 | 0.0659          |
| 0.0826        | 0.3282 | 1300 | 0.6012 | 0.0556          |
| 0.064         | 0.3408 | 1350 | nan    | 0.0564          |
| 0.0854        | 0.3534 | 1400 | nan    | 0.0552          |
| 0.0702        | 0.3661 | 1450 | 0.6061 | 0.0675          |
| 0.0771        | 0.3787 | 1500 | nan    | 0.0578          |
| 0.08          | 0.3913 | 1550 | nan    | 0.0492          |
| 0.0804        | 0.4039 | 1600 | 0.6112 | 0.0538          |
| 0.083         | 0.4166 | 1650 | 0.6048 | 0.0579          |
| 0.0701        | 0.4292 | 1700 | 0.6045 | 0.0674          |
| 0.0721        | 0.4418 | 1750 | 0.5979 | 0.0491          |
| 0.0765        | 0.4544 | 1800 | nan    | 0.0439          |
| 0.0692        | 0.4671 | 1850 | 0.6058 | 0.0468          |
| 0.0761        | 0.4797 | 1900 | 0.6125 | 0.0574          |
| 0.0757        | 0.4923 | 1950 | 0.6126 | 0.0569          |
| 0.0654        | 0.5049 | 2000 | 0.6095 | 0.0549          |
| 0.0736        | 0.5175 | 2050 | 0.0538 | 0.6122          |
| 0.0685        | 0.5302 | 2100 | 0.0485 | 0.6104          |
| 0.0726        | 0.5428 | 2150 | 0.0566 | 0.6120          |
| 0.0731        | 0.5554 | 2200 | 0.0585 | 0.6112          |
| 0.0722        | 0.5680 | 2250 | 0.0589 | 0.6140          |
| 0.0819        | 0.5807 | 2300 | 0.0505 | 0.6122          |
| 0.0694        | 0.5933 | 2350 | 0.0537 | 0.6101          |
| 0.0705        | 0.6059 | 2400 | 0.0646 | 0.6130          |
| 0.0702        | 0.6185 | 2450 | 0.0462 | 0.6124          |
| 0.0709        | 0.6312 | 2500 | 0.0404 | 0.6134          |
| 0.0804        | 0.6438 | 2550 | 0.0478 | 0.6123          |
| 0.0666        | 0.6564 | 2600 | 0.0455 | 0.6104          |
| 0.0749        | 0.6690 | 2650 | 0.0479 | 0.6132          |
| 0.067         | 0.6816 | 2700 | 0.0558 | 0.6132          |
| 0.068         | 0.6943 | 2750 | 0.0539 | 0.6108          |


### Framework versions

- Transformers 4.43.0.dev0
- Pytorch 2.1.2+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1