bert-large-uncased-finetuned-vi-infovqa

This model is a fine-tuned version of bert-large-uncased on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 7.4878

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: 2e-05
• train_batch_size: 2
• eval_batch_size: 2
• seed: 250500
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 6

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.11	100	4.6256
No log	0.21	200	4.4042
No log	0.32	300	5.0021
No log	0.43	400	4.2825
4.6758	0.53	500	4.3886
4.6758	0.64	600	4.2519
4.6758	0.75	700	4.2977
4.6758	0.85	800	3.9916
4.6758	0.96	900	4.1650
4.1715	1.07	1000	4.5001
4.1715	1.17	1100	4.0898
4.1715	1.28	1200	4.1623
4.1715	1.39	1300	4.3271
4.1715	1.49	1400	3.9661
3.7926	1.6	1500	3.8727
3.7926	1.71	1600	3.8934
3.7926	1.81	1700	3.7262
3.7926	1.92	1800	3.7701
3.7926	2.03	1900	3.7653
3.5041	2.13	2000	3.9261
3.5041	2.24	2100	4.0915
3.5041	2.35	2200	4.0348
3.5041	2.45	2300	4.0212
3.5041	2.56	2400	4.4653
2.8475	2.67	2500	4.2959
2.8475	2.77	2600	4.1039
2.8475	2.88	2700	3.8037
2.8475	2.99	2800	3.7552
2.8475	3.09	2900	4.2476
2.5488	3.2	3000	4.6716
2.5488	3.3	3100	4.7058
2.5488	3.41	3200	4.6266
2.5488	3.52	3300	4.5697
2.5488	3.62	3400	5.1017
2.0347	3.73	3500	4.6254
2.0347	3.84	3600	4.4822
2.0347	3.94	3700	4.9413
2.0347	4.05	3800	5.3600
2.0347	4.16	3900	5.7323
1.6566	4.26	4000	5.8822
1.6566	4.37	4100	6.0173
1.6566	4.48	4200	5.6688
1.6566	4.58	4300	6.0617
1.6566	4.69	4400	6.6631
1.3348	4.8	4500	6.0290
1.3348	4.9	4600	6.2455
1.3348	5.01	4700	6.0963
1.3348	5.12	4800	7.0983
1.3348	5.22	4900	7.5483
1.0701	5.33	5000	7.7187
1.0701	5.44	5100	7.4630
1.0701	5.54	5200	7.1394
1.0701	5.65	5300	7.0703
1.0701	5.76	5400	7.5611
0.9414	5.86	5500	7.6038
0.9414	5.97	5600	7.4878

Framework versions

• Transformers 4.15.0
• Pytorch 1.10.0+cu111
• Datasets 1.17.0
• Tokenizers 0.10.3