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gokuls/model_v1_complete_training_wt_init_48_mini

sourceHugging Faceupdated 3y agoView on Hugging Face

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modelv1completetrainingwtinit48_mini

This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set:

—Loss: 2.7920
—Accuracy: 0.4992

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: 1e-05
—trainbatchsize: 48
—evalbatchsize: 48
—seed: 10
—distributed_type: multi-GPU
—optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
—lrschedulertype: linear
—lrschedulerwarmup_steps: 10000
—num_epochs: 25

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
5.9411	0.25	30000	5.8833	0.1518
5.6408	0.49	60000	5.5265	0.1908
4.5385	0.74	90000	4.3133	0.3138
4.1015	0.98	120000	3.8996	0.3583
3.9119	1.23	150000	3.7199	0.3783
3.7832	1.47	180000	3.6039	0.3920
3.6686	1.72	210000	3.5057	0.4033
3.5793	1.97	240000	3.4227	0.4137
3.5128	2.21	270000	3.3645	0.4209
3.4597	2.46	300000	3.3219	0.4261
3.4263	2.7	330000	3.2841	0.4312
3.3909	2.95	360000	3.2547	0.4348
3.3635	3.2	390000	3.2284	0.4379
3.3488	3.44	420000	3.2060	0.4409
3.3239	3.69	450000	3.1872	0.4436
3.3062	3.93	480000	3.1660	0.4462
3.2841	4.18	510000	3.1493	0.4485
3.2663	4.42	540000	3.1355	0.4503
3.259	4.67	570000	3.1229	0.4519
3.2429	4.92	600000	3.1096	0.4535
3.2234	5.16	630000	3.0947	0.4554
3.2115	5.41	660000	3.0818	0.4573
3.2011	5.65	690000	3.0685	0.4590
3.1898	5.9	720000	3.0464	0.4619
3.1651	6.14	750000	3.0226	0.4658
3.1477	6.39	780000	3.0025	0.4689
3.1276	6.64	810000	2.9838	0.4718
3.1102	6.88	840000	2.9690	0.4740
3.1046	7.13	870000	2.9563	0.4757
3.0817	7.37	900000	2.9477	0.4771
3.0813	7.62	930000	2.9397	0.4785
3.0709	7.87	960000	2.9259	0.4804
3.0528	8.11	990000	2.9208	0.4812
3.0541	8.36	1020000	2.9089	0.4829
3.0469	8.6	1050000	2.9015	0.4839
3.0377	8.85	1080000	2.8960	0.4848
3.0284	9.09	1110000	2.8859	0.4861
3.0224	9.34	1140000	2.8819	0.4867
3.019	9.59	1170000	2.8731	0.4878
3.0094	9.83	1200000	2.8687	0.4885
3.0065	10.08	1230000	2.8635	0.4893
2.9983	10.32	1260000	2.8561	0.4900
2.9834	10.57	1290000	2.8524	0.4907
2.9873	10.81	1320000	2.8484	0.4911
2.978	11.06	1350000	2.8414	0.4924
2.9709	11.31	1380000	2.8375	0.4927
2.9695	11.55	1410000	2.8353	0.4932
2.9607	11.8	1440000	2.8290	0.4941
2.9636	12.04	1470000	2.8267	0.4944
2.9584	12.29	1500000	2.8247	0.4946
2.9546	12.54	1530000	2.8196	0.4951
2.9544	12.78	1560000	2.8146	0.4959
2.9486	13.03	1590000	2.8132	0.4964
2.9413	13.27	1620000	2.8099	0.4967
2.9381	13.52	1650000	2.8081	0.4968
2.9389	13.76	1680000	2.8057	0.4973
2.9374	14.01	1710000	2.8028	0.4977
2.9341	14.26	1740000	2.8000	0.4978
2.9275	14.5	1770000	2.7978	0.4984
2.9319	14.75	1800000	2.7947	0.4989
2.9304	14.99	1830000	2.7920	0.4992

Framework versions

—Transformers 4.30.2
—Pytorch 1.14.0a0+410ce96
—Datasets 2.13.0
—Tokenizers 0.13.3