| | --- |
| | license: mit |
| | base_model: naver-clova-ix/donut-base |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - bleu |
| | - wer |
| | 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 |
| | should probably proofread and complete it, then remove this comment. --> |
| |
|
| | # 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 |
| | |
