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_2
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# donut_experiment_bayesian_trial_2

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: 0.4983
- Bleu: 0.0695
- Precisions: [0.8257261410788381, 0.7717647058823529, 0.7255434782608695, 0.6816720257234726]
- Brevity Penalty: 0.0928
- Length Ratio: 0.2961
- Translation Length: 482
- Reference Length: 1628
- Cer: 0.7610
- Wer: 0.8275

## 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: 0.00015752383448484097
- 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: 4
- 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|:------:|:------:|
| 0.3017        | 1.0   | 253  | 0.7248          | 0.0641 | [0.7525150905432596, 0.65, 0.587467362924282, 0.5276073619631901]                | 0.1027          | 0.3053       | 497                | 1628             | 0.7622 | 0.8495 |
| 0.1875        | 2.0   | 506  | 0.6129          | 0.0670 | [0.7914110429447853, 0.7152777777777778, 0.6613333333333333, 0.60062893081761]   | 0.0974          | 0.3004       | 489                | 1628             | 0.7565 | 0.8375 |
| 0.1171        | 3.0   | 759  | 0.5027          | 0.0697 | [0.8202479338842975, 0.7587822014051522, 0.7162162162162162, 0.6741214057507987] | 0.0941          | 0.2973       | 484                | 1628             | 0.7563 | 0.8293 |
| 0.0432        | 4.0   | 1012 | 0.4983          | 0.0695 | [0.8257261410788381, 0.7717647058823529, 0.7255434782608695, 0.6816720257234726] | 0.0928          | 0.2961       | 482                | 1628             | 0.7610 | 0.8275 |


### Framework versions

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