transformers tensorboard safetensors vision-encoder-decoder image-text-to-text generated_from_trainer base_model:naver-clova-ix/donut-base base_model:finetune:naver-clova-ix/donut-base license:mit endpoints_compatible region:us
---
base_model: naver-clova-ix/donut-base
license: mit
metrics:
- bleu
- wer
tags:
- generated_from_trainer
model-index:
- name: donut_experiment_bayesian_trial_7
  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_experiment_bayesian_trial_7

This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3786
- Bleu: 0.0669
- Precisions: [0.8477801268498943, 0.7836538461538461, 0.7465181058495822, 0.7052980132450332]
- Brevity Penalty: 0.0870
- Length Ratio: 0.2905
- Translation Length: 473
- Reference Length: 1628
- Cer: 0.7532
- Wer: 0.8192

## 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: 3.540464175534869e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Bleu   | Precisions                                                                       | Brevity Penalty | Length Ratio | Translation Length | Reference Length | Cer    | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|:------:|:------:|
| 0.5323        | 1.0   | 253  | 0.4204          | 0.0580 | [0.7710084033613446, 0.6778042959427207, 0.6132596685082873, 0.5639344262295082] | 0.0889          | 0.2924       | 476                | 1628             | 0.7617 | 0.8431 |
| 0.2487        | 2.0   | 506  | 0.3788          | 0.0609 | [0.8123667377398721, 0.7402912621359223, 0.6929577464788732, 0.6476510067114094] | 0.0845          | 0.2881       | 469                | 1628             | 0.7561 | 0.8279 |
| 0.1746        | 3.0   | 759  | 0.3551          | 0.0652 | [0.836864406779661, 0.7759036144578313, 0.729050279329609, 0.6843853820598007]   | 0.0864          | 0.2899       | 472                | 1628             | 0.7541 | 0.8213 |
| 0.1191        | 4.0   | 1012 | 0.3690          | 0.0680 | [0.8547368421052631, 0.784688995215311, 0.7451523545706371, 0.7039473684210527]  | 0.0883          | 0.2918       | 475                | 1628             | 0.7514 | 0.8192 |
| 0.1072        | 5.0   | 1265 | 0.3786          | 0.0669 | [0.8477801268498943, 0.7836538461538461, 0.7465181058495822, 0.7052980132450332] | 0.0870          | 0.2905       | 473                | 1628             | 0.7532 | 0.8192 |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.1.0
- Datasets 2.18.0
- Tokenizers 0.19.1
