| | --- |
| | license: mit |
| | tags: |
| | - generated_from_trainer |
| | base_model: microsoft/git-base |
| | datasets: |
| | - imagefolder |
| | model-index: |
| | - name: git-base-captioning |
| | 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. --> |
| |
|
| | # git-base-captioning |
| |
|
| | This model is a fine-tuned version of [microsoft/git-base](https://huggingface.co/microsoft/git-base) on the imagefolder dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.3853 |
| | - Wer Score: 0.8299 |
| |
|
| | ## 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: 5e-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: 10 |
| | - mixed_precision_training: Native AMP |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Wer Score | |
| | |:-------------:|:------:|:----:|:---------------:|:---------:| |
| | | 1.6087 | 0.4202 | 50 | 0.4992 | 0.8373 | |
| | | 0.435 | 0.8403 | 100 | 0.3918 | 0.8254 | |
| | | 0.3572 | 1.2605 | 150 | 0.3769 | 0.8254 | |
| | | 0.3207 | 1.6807 | 200 | 0.3699 | 0.8130 | |
| | | 0.2761 | 2.1008 | 250 | 0.3683 | 0.8130 | |
| | | 0.2635 | 2.5210 | 300 | 0.3684 | 0.8102 | |
| | | 0.2551 | 2.9412 | 350 | 0.3662 | 0.8350 | |
| | | 0.225 | 3.3613 | 400 | 0.3681 | 0.8113 | |
| | | 0.2364 | 3.7815 | 450 | 0.3683 | 0.8249 | |
| | | 0.2091 | 4.2017 | 500 | 0.3720 | 3.0723 | |
| | | 0.1939 | 4.6218 | 550 | 0.3740 | 0.8215 | |
| | | 0.1839 | 5.0420 | 600 | 0.3723 | 1.8362 | |
| | | 0.1713 | 5.4622 | 650 | 0.3769 | 0.8243 | |
| | | 0.1664 | 5.8824 | 700 | 0.3753 | 0.8209 | |
| | | 0.1634 | 6.3025 | 750 | 0.3786 | 0.8249 | |
| | | 0.1444 | 6.7227 | 800 | 0.3805 | 0.8220 | |
| | | 0.1536 | 7.1429 | 850 | 0.3807 | 0.8322 | |
| | | 0.1469 | 7.5630 | 900 | 0.3832 | 0.8350 | |
| | | 0.1303 | 7.9832 | 950 | 0.3834 | 0.8271 | |
| | | 0.1341 | 8.4034 | 1000 | 0.3841 | 0.8232 | |
| | | 0.1268 | 8.8235 | 1050 | 0.3836 | 0.8277 | |
| | | 0.1348 | 9.2437 | 1100 | 0.3852 | 0.8260 | |
| | | 0.1247 | 9.6639 | 1150 | 0.3853 | 0.8299 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.41.2 |
| | - Pytorch 2.3.0+cu121 |
| | - Datasets 2.19.2 |
| | - Tokenizers 0.19.1 |
| | |
