|
--- |
|
license: mit |
|
base_model: naver-clova-ix/donut-base |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- imagefolder |
|
metrics: |
|
- wer |
|
model-index: |
|
- name: donut-base-sroie-metrics-combined |
|
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-metrics-combined |
|
|
|
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.2827 |
|
- Bleu score: 0.0762 |
|
- Precisions: [0.8396396396396396, 0.7886178861788617, 0.7435897435897436, 0.7049180327868853] |
|
- Brevity penalty: 0.0993 |
|
- Length ratio: 0.3021 |
|
- Translation length: 555 |
|
- Reference length: 1837 |
|
- Cer: 0.7452 |
|
- Wer: 0.8162 |
|
- Cer Hugging Face: 0.7544 |
|
- Wer Hugging Face: 0.8233 |
|
|
|
## 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 score | Precisions | Brevity penalty | Length ratio | Translation length | Reference length | Cer | Wer | Cer Hugging Face | Wer Hugging Face | |
|
|:-------------:|:-----:|:----:|:---------------:|:----------:|:--------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|:------:|:------:|:----------------:|:----------------:| |
|
| No log | 0.99 | 62 | 0.3478 | 0.0756 | [0.8178571428571428, 0.7625754527162978, 0.7142857142857143, 0.6711590296495957] | 0.1022 | 0.3048 | 560 | 1837 | 0.7474 | 0.8243 | 0.7570 | 0.8333 | |
|
| 0.2634 | 2.0 | 125 | 0.2873 | 0.0763 | [0.8345323741007195, 0.7829614604462475, 0.7418604651162791, 0.7029972752043597] | 0.0999 | 0.3027 | 556 | 1837 | 0.7435 | 0.8219 | 0.7527 | 0.8288 | |
|
| 0.2634 | 2.99 | 187 | 0.2817 | 0.0777 | [0.8369175627240143, 0.7838383838383839, 0.7476851851851852, 0.7127371273712737] | 0.1011 | 0.3038 | 558 | 1837 | 0.7407 | 0.8152 | 0.7498 | 0.8215 | |
|
| 0.263 | 3.97 | 248 | 0.2827 | 0.0762 | [0.8396396396396396, 0.7886178861788617, 0.7435897435897436, 0.7049180327868853] | 0.0993 | 0.3021 | 555 | 1837 | 0.7452 | 0.8162 | 0.7544 | 0.8233 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.40.0.dev0 |
|
- Pytorch 2.1.0 |
|
- Datasets 2.18.0 |
|
- Tokenizers 0.15.2 |
|
|
