End of training

b47975b about 2 years ago

7.22 kB

	---
	license: mit
	base_model: microsoft/git-base-coco
	tags:
	- generated_from_trainer
	model-index:
	- name: git-base-coco-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-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

	---
	license: mit
	base_model: microsoft/git-base-coco
	tags:
	- generated_from_trainer
	model-index:
	- name: git-base-coco-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-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