transformers tensorboard safetensors vision-encoder-decoder generated_from_trainer dataset:imagefolder base_model:davelotito/donut-base-sroie license:mit endpoints_compatible region:us
---
license: mit
base_model: davelotito/donut-base-sroie
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- bleu
- wer
model-index:
- name: donut-base-sroie-v2
  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-sroie-v2

This model is a fine-tuned version of [davelotito/donut-base-sroie](https://huggingface.co/davelotito/donut-base-sroie) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4355
- Bleu: 0.8879
- Precisions: [0.943646408839779, 0.9119229045271179, 0.8854285064787452, 0.860009225092251]
- Brevity Penalty: 0.9868
- Length Ratio: 0.9869
- Translation Length: 4525
- Reference Length: 4585
- Cer: 0.0857
- Wer: 0.2978

## 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|:------:|:------:|
| No log        | 0.99  | 62   | 0.4638          | 0.8823 | [0.9399823477493381, 0.9044528977399866, 0.8772128915115751, 0.8514851485148515] | 0.9884          | 0.9884       | 4532               | 4585             | 0.0912 | 0.3085 |
| 0.0043        | 2.0   | 125  | 0.4421          | 0.8853 | [0.9405155320555189, 0.9059428060768543, 0.8794470881486517, 0.8537931034482759] | 0.9899          | 0.9900       | 4539               | 4585             | 0.0889 | 0.3050 |
| 0.0043        | 2.99  | 187  | 0.4328          | 0.8904 | [0.9399122807017544, 0.9068267734044919, 0.8809201623815968, 0.8558682223747426] | 0.9945          | 0.9945       | 4560               | 4585             | 0.0842 | 0.2939 |
| 0.0106        | 3.97  | 248  | 0.4355          | 0.8879 | [0.943646408839779, 0.9119229045271179, 0.8854285064787452, 0.860009225092251]   | 0.9868          | 0.9869       | 4525               | 4585             | 0.0857 | 0.2978 |


### Framework versions

- Transformers 4.40.0.dev0
- Pytorch 2.1.0
- Datasets 2.18.0
- Tokenizers 0.15.2
