| | --- |
| | license: mit |
| | base_model: microsoft/git-base |
| | tags: |
| | - generated_from_trainer |
| | model-index: |
| | - name: GenerativeImage2Text-naruto |
| | 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. --> |
| |
|
| | # GenerativeImage2Text-naruto |
| |
|
| | This model is a fine-tuned version of [microsoft/git-base](https://huggingface.co/microsoft/git-base) on an unknown dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.0698 |
| | - Wer Score: 5.7759 |
| |
|
| | ## 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: 2 |
| | - eval_batch_size: 2 |
| | - seed: 42 |
| | - gradient_accumulation_steps: 2 |
| | - total_train_batch_size: 4 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - num_epochs: 100 |
| | - mixed_precision_training: Native AMP |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Wer Score | |
| | |:-------------:|:-------:|:----:|:---------------:|:---------:| |
| | | 7.3056 | 3.7037 | 50 | 4.4313 | 24.2069 | |
| | | 2.1534 | 7.4074 | 100 | 0.3158 | 0.4310 | |
| | | 0.0855 | 11.1111 | 150 | 0.0458 | 0.4655 | |
| | | 0.0153 | 14.8148 | 200 | 0.0447 | 0.4655 | |
| | | 0.0109 | 18.5185 | 250 | 0.0492 | 0.4483 | |
| | | 0.0098 | 22.2222 | 300 | 0.0529 | 0.4483 | |
| | | 0.0077 | 25.9259 | 350 | 0.0547 | 0.4828 | |
| | | 0.0069 | 29.6296 | 400 | 0.0567 | 0.4483 | |
| | | 0.0058 | 33.3333 | 450 | 0.0595 | 0.7414 | |
| | | 0.0042 | 37.0370 | 500 | 0.0620 | 2.5517 | |
| | | 0.0036 | 40.7407 | 550 | 0.0654 | 3.1379 | |
| | | 0.0032 | 44.4444 | 600 | 0.0614 | 9.7414 | |
| | | 0.0026 | 48.1481 | 650 | 0.0664 | 6.5517 | |
| | | 0.001 | 51.8519 | 700 | 0.0670 | 7.4828 | |
| | | 0.0006 | 55.5556 | 750 | 0.0662 | 7.5172 | |
| | | 0.0006 | 59.2593 | 800 | 0.0670 | 8.7586 | |
| | | 0.0003 | 62.9630 | 850 | 0.0678 | 7.7414 | |
| | | 0.0003 | 66.6667 | 900 | 0.0685 | 6.7931 | |
| | | 0.0002 | 70.3704 | 950 | 0.0688 | 6.3103 | |
| | | 0.0002 | 74.0741 | 1000 | 0.0689 | 6.4828 | |
| | | 0.0002 | 77.7778 | 1050 | 0.0691 | 6.1724 | |
| | | 0.0002 | 81.4815 | 1100 | 0.0694 | 6.0862 | |
| | | 0.0002 | 85.1852 | 1150 | 0.0695 | 6.1034 | |
| | | 0.0002 | 88.8889 | 1200 | 0.0697 | 5.9828 | |
| | | 0.0002 | 92.5926 | 1250 | 0.0698 | 5.8276 | |
| | | 0.0002 | 96.2963 | 1300 | 0.0698 | 5.7759 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.40.2 |
| | - Pytorch 2.3.0+cu121 |
| | - Datasets 2.19.1 |
| | - Tokenizers 0.19.1 |
| | |
