--- license: mit base_model: microsoft/git-base-coco tags: - generated_from_trainer model-index: - name: git-base-coco-pokemon results: [] --- # git-base-coco-pokemon This model is a fine-tuned version of [microsoft/git-base-coco](https://huggingface.co/microsoft/git-base-coco) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0496 - Wer Score: 17.2413 ## 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: 8 - eval_batch_size: 8 - seed: 42 - 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 | |:-------------:|:-----:|:----:|:---------------:|:---------:| | No log | 0.53 | 50 | 4.1410 | 21.7574 | | No log | 1.06 | 100 | 0.2080 | 0.4284 | | No log | 1.6 | 150 | 0.0389 | 0.4503 | | No log | 2.13 | 200 | 0.0314 | 0.4297 | | No log | 2.66 | 250 | 0.0302 | 0.3935 | | No log | 3.19 | 300 | 0.0297 | 3.6761 | | No log | 3.72 | 350 | 0.0302 | 0.9729 | | No log | 4.26 | 400 | 0.0294 | 3.9110 | | No log | 4.79 | 450 | 0.0296 | 0.6968 | | 0.9165 | 5.32 | 500 | 0.0307 | 14.7252 | | 0.9165 | 5.85 | 550 | 0.0306 | 14.0787 | | 0.9165 | 6.38 | 600 | 0.0314 | 17.9974 | | 0.9165 | 6.91 | 650 | 0.0305 | 19.4271 | | 0.9165 | 7.45 | 700 | 0.0313 | 18.76 | | 0.9165 | 7.98 | 750 | 0.0321 | 17.1084 | | 0.9165 | 8.51 | 800 | 0.0322 | 20.9123 | | 0.9165 | 9.04 | 850 | 0.0321 | 21.0026 | | 0.9165 | 9.57 | 900 | 0.0328 | 19.2103 | | 0.9165 | 10.11 | 950 | 0.0336 | 19.4503 | | 0.0124 | 10.64 | 1000 | 0.0354 | 19.6310 | | 0.0124 | 11.17 | 1050 | 0.0350 | 17.3652 | | 0.0124 | 11.7 | 1100 | 0.0355 | 18.2955 | | 0.0124 | 12.23 | 1150 | 0.0375 | 19.4194 | | 0.0124 | 12.77 | 1200 | 0.0362 | 18.2606 | | 0.0124 | 13.3 | 1250 | 0.0375 | 19.8348 | | 0.0124 | 13.83 | 1300 | 0.0380 | 18.6581 | | 0.0124 | 14.36 | 1350 | 0.0383 | 19.2723 | | 0.0124 | 14.89 | 1400 | 0.0407 | 18.8516 | | 0.0124 | 15.43 | 1450 | 0.0406 | 19.0968 | | 0.0049 | 15.96 | 1500 | 0.0406 | 18.4774 | | 0.0049 | 16.49 | 1550 | 0.0419 | 18.5768 | | 0.0049 | 17.02 | 1600 | 0.0435 | 19.8606 | | 0.0049 | 17.55 | 1650 | 0.0437 | 19.6477 | | 0.0049 | 18.09 | 1700 | 0.0445 | 19.2684 | | 0.0049 | 18.62 | 1750 | 0.0443 | 18.6039 | | 0.0049 | 19.15 | 1800 | 0.0432 | 17.8129 | | 0.0049 | 19.68 | 1850 | 0.0455 | 18.9587 | | 0.0049 | 20.21 | 1900 | 0.0448 | 18.28 | | 0.0049 | 20.74 | 1950 | 0.0455 | 18.4477 | | 0.0009 | 21.28 | 2000 | 0.0453 | 18.2542 | | 0.0009 | 21.81 | 2050 | 0.0457 | 18.7458 | | 0.0009 | 22.34 | 2100 | 0.0456 | 18.5239 | | 0.0009 | 22.87 | 2150 | 0.0450 | 18.3523 | | 0.0009 | 23.4 | 2200 | 0.0459 | 18.2658 | | 0.0009 | 23.94 | 2250 | 0.0462 | 18.0916 | | 0.0009 | 24.47 | 2300 | 0.0465 | 18.3265 | | 0.0009 | 25.0 | 2350 | 0.0463 | 18.4245 | | 0.0009 | 25.53 | 2400 | 0.0466 | 18.1948 | | 0.0009 | 26.06 | 2450 | 0.0467 | 18.0090 | | 0.0002 | 26.6 | 2500 | 0.0468 | 18.2155 | | 0.0002 | 27.13 | 2550 | 0.0471 | 18.1639 | | 0.0002 | 27.66 | 2600 | 0.0472 | 17.92 | | 0.0002 | 28.19 | 2650 | 0.0472 | 17.9303 | | 0.0002 | 28.72 | 2700 | 0.0474 | 17.8116 | | 0.0002 | 29.26 | 2750 | 0.0476 | 17.9045 | | 0.0002 | 29.79 | 2800 | 0.0477 | 17.4942 | | 0.0002 | 30.32 | 2850 | 0.0477 | 17.6129 | | 0.0002 | 30.85 | 2900 | 0.0479 | 17.3910 | | 0.0002 | 31.38 | 2950 | 0.0480 | 17.6594 | | 0.0001 | 31.91 | 3000 | 0.0480 | 17.5303 | | 0.0001 | 32.45 | 3050 | 0.0481 | 17.4245 | | 0.0001 | 32.98 | 3100 | 0.0483 | 17.4413 | | 0.0001 | 33.51 | 3150 | 0.0483 | 17.4013 | | 0.0001 | 34.04 | 3200 | 0.0484 | 17.3342 | | 0.0001 | 34.57 | 3250 | 0.0485 | 17.2361 | | 0.0001 | 35.11 | 3300 | 0.0486 | 17.3613 | | 0.0001 | 35.64 | 3350 | 0.0487 | 17.2606 | | 0.0001 | 36.17 | 3400 | 0.0488 | 17.4039 | | 0.0001 | 36.7 | 3450 | 0.0488 | 17.2168 | | 0.0001 | 37.23 | 3500 | 0.0489 | 17.2194 | | 0.0001 | 37.77 | 3550 | 0.0488 | 17.3032 | | 0.0001 | 38.3 | 3600 | 0.0489 | 17.3303 | | 0.0001 | 38.83 | 3650 | 0.0490 | 17.3277 | | 0.0001 | 39.36 | 3700 | 0.0490 | 17.3381 | | 0.0001 | 39.89 | 3750 | 0.0491 | 17.3471 | | 0.0001 | 40.43 | 3800 | 0.0492 | 17.3497 | | 0.0001 | 40.96 | 3850 | 0.0492 | 17.3484 | | 0.0001 | 41.49 | 3900 | 0.0493 | 17.3910 | | 0.0001 | 42.02 | 3950 | 0.0491 | 17.3019 | | 0.0 | 42.55 | 4000 | 0.0492 | 17.2942 | | 0.0 | 43.09 | 4050 | 0.0493 | 17.2645 | | 0.0 | 43.62 | 4100 | 0.0493 | 17.2387 | | 0.0 | 44.15 | 4150 | 0.0493 | 17.2348 | | 0.0 | 44.68 | 4200 | 0.0493 | 17.2490 | | 0.0 | 45.21 | 4250 | 0.0494 | 17.2374 | | 0.0 | 45.74 | 4300 | 0.0495 | 17.2568 | | 0.0 | 46.28 | 4350 | 0.0495 | 17.2619 | | 0.0 | 46.81 | 4400 | 0.0495 | 17.2310 | | 0.0 | 47.34 | 4450 | 0.0496 | 17.2374 | | 0.0 | 47.87 | 4500 | 0.0496 | 17.2426 | | 0.0 | 48.4 | 4550 | 0.0496 | 17.2387 | | 0.0 | 48.94 | 4600 | 0.0496 | 17.2335 | | 0.0 | 49.47 | 4650 | 0.0496 | 17.2387 | | 0.0 | 50.0 | 4700 | 0.0496 | 17.2413 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.0.0+cpu - Datasets 2.15.0 - Tokenizers 0.15.0