git-base-pokemon

This model is a fine-tuned version of microsoft/git-base on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 0.0429
• Wer Score: 1.9591

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
• gradient_accumulation_steps: 2
• total_train_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 50
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer Score
7.3666	1.06	50	4.4430	21.5287
2.1581	2.13	100	0.2911	0.9783
0.0896	3.19	150	0.0328	0.3665
0.0269	4.26	200	0.0274	0.3487
0.0208	5.32	250	0.0284	0.4189
0.0168	6.38	300	0.0287	1.1673
0.0133	7.45	350	0.0296	6.0881
0.0106	8.51	400	0.0306	1.7969
0.0076	9.57	450	0.0322	7.1852
0.0053	10.64	500	0.0329	14.8889
0.0039	11.7	550	0.0338	12.2720
0.0027	12.77	600	0.0356	5.1533
0.0016	13.83	650	0.0371	8.4253
0.001	14.89	700	0.0379	6.7344
0.0006	15.96	750	0.0385	7.7586
0.0005	17.02	800	0.0392	9.0294
0.0004	18.09	850	0.0385	7.5083
0.0004	19.15	900	0.0394	5.1188
0.0004	20.21	950	0.0397	5.0600
0.0004	21.28	1000	0.0399	4.4125
0.0003	22.34	1050	0.0405	3.7803
0.0003	23.4	1100	0.0406	3.3397
0.0003	24.47	1150	0.0408	3.3218
0.0003	25.53	1200	0.0411	2.8212
0.0003	26.6	1250	0.0411	2.7165
0.0003	27.66	1300	0.0414	2.7625
0.0003	28.72	1350	0.0416	2.4330
0.0003	29.79	1400	0.0416	2.2350
0.0003	30.85	1450	0.0419	2.1699
0.0003	31.91	1500	0.0421	2.0026
0.0003	32.98	1550	0.0420	2.1609
0.0003	34.04	1600	0.0421	2.0307
0.0003	35.11	1650	0.0422	1.9668
0.0003	36.17	1700	0.0423	1.9387
0.0003	37.23	1750	0.0425	1.9464
0.0003	38.3	1800	0.0427	1.8761
0.0003	39.36	1850	0.0427	1.8940
0.0003	40.43	1900	0.0428	1.9068
0.0003	41.49	1950	0.0428	1.8774
0.0003	42.55	2000	0.0429	1.8352
0.0002	43.62	2050	0.0428	2.0907
0.0002	44.68	2100	0.0429	2.0319
0.0002	45.74	2150	0.0429	2.0179
0.0002	46.81	2200	0.0429	1.9706
0.0002	47.87	2250	0.0429	1.9604
0.0002	48.94	2300	0.0429	1.9540
0.0002	50.0	2350	0.0429	1.9591

Framework versions

• Transformers 4.28.0
• Pytorch 2.0.0+cu118
• Datasets 2.11.0
• Tokenizers 0.13.3