| | --- |
| | license: mit |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - imagefolder |
| | model-index: |
| | - name: git-base-pokemon |
| | 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-pokemon |
| |
|
| | 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: 0.0340 |
| | - Wer Score: 2.1498 |
| |
|
| | ## 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: 16 |
| | - eval_batch_size: 10 |
| | - seed: 42 |
| | - gradient_accumulation_steps: 2 |
| | - total_train_batch_size: 32 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - num_epochs: 50 |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Wer Score | |
| | |:-------------:|:-----:|:----:|:---------------:|:---------:| |
| | | 7.321 | 2.13 | 50 | 4.4679 | 21.5557 | |
| | | 2.2294 | 4.26 | 100 | 0.3441 | 11.8745 | |
| | | 0.1021 | 6.38 | 150 | 0.0283 | 0.5672 | |
| | | 0.0187 | 8.51 | 200 | 0.0251 | 0.6018 | |
| | | 0.0086 | 10.64 | 250 | 0.0272 | 3.6786 | |
| | | 0.0038 | 12.77 | 300 | 0.0288 | 6.7119 | |
| | | 0.0019 | 14.89 | 350 | 0.0300 | 4.2023 | |
| | | 0.0011 | 17.02 | 400 | 0.0308 | 4.0768 | |
| | | 0.0009 | 19.15 | 450 | 0.0310 | 3.5980 | |
| | | 0.0007 | 21.28 | 500 | 0.0315 | 3.5723 | |
| | | 0.0007 | 23.4 | 550 | 0.0323 | 2.8835 | |
| | | 0.0006 | 25.53 | 600 | 0.0325 | 2.8399 | |
| | | 0.0006 | 27.66 | 650 | 0.0330 | 2.6274 | |
| | | 0.0006 | 29.79 | 700 | 0.0331 | 2.5416 | |
| | | 0.0006 | 31.91 | 750 | 0.0334 | 2.4213 | |
| | | 0.0006 | 34.04 | 800 | 0.0335 | 2.3214 | |
| | | 0.0006 | 36.17 | 850 | 0.0330 | 2.2330 | |
| | | 0.0006 | 38.3 | 900 | 0.0337 | 2.2254 | |
| | | 0.0006 | 40.43 | 950 | 0.0338 | 2.1652 | |
| | | 0.0006 | 42.55 | 1000 | 0.0340 | 2.1447 | |
| | | 0.0006 | 44.68 | 1050 | 0.0340 | 2.1767 | |
| | | 0.0006 | 46.81 | 1100 | 0.0340 | 2.1536 | |
| | | 0.0006 | 48.94 | 1150 | 0.0340 | 2.1498 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.30.2 |
| | - Pytorch 2.0.0 |
| | - Datasets 2.12.0 |
| | - Tokenizers 0.13.3 |
| | |
