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gokuls/model_v1_complete_training_wt_init_48_tiny_emb_comp

sourceHugging Faceupdated 3y agoView on Hugging Face

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modelv1completetrainingwtinit48tinyemb_comp

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: 3.7768
—Accuracy: 0.3787

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: 64
—evalbatchsize: 64
—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: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
6.0945	0.33	30000	6.0802	0.1412
5.2818	0.66	60000	5.2151	0.2395
4.8774	0.98	90000	4.8105	0.2760
4.7096	1.31	120000	4.6474	0.2894
4.6109	1.64	150000	4.5460	0.2985
4.5415	1.97	180000	4.4761	0.3050
4.4884	2.29	210000	4.4231	0.3101
4.446	2.62	240000	4.3791	0.3144
4.4072	2.95	270000	4.3416	0.3179
4.3755	3.28	300000	4.3064	0.3218
4.3455	3.6	330000	4.2724	0.3254
4.3172	3.93	360000	4.2410	0.3291
4.2921	4.26	390000	4.2130	0.3324
4.2718	4.59	420000	4.1892	0.3348
4.2485	4.92	450000	4.1688	0.3370
4.2267	5.24	480000	4.1500	0.3394
4.2081	5.57	510000	4.1314	0.3412
4.198	5.9	540000	4.1117	0.3435
4.1666	6.23	570000	4.0949	0.3451
4.1498	6.55	600000	4.0786	0.3464
4.1104	6.88	630000	4.0465	0.3499
4.0715	7.21	660000	4.0078	0.3539
4.0298	7.54	690000	3.9722	0.3576
4.0085	7.87	720000	3.9520	0.3599
3.99	8.19	750000	3.9390	0.3615
3.9799	8.52	780000	3.9272	0.3627
3.9766	8.85	810000	3.9138	0.3641
3.9534	9.18	840000	3.9034	0.3651
3.9521	9.5	870000	3.8918	0.3662
3.9314	9.83	900000	3.8817	0.3670
3.9096	10.16	930000	3.8709	0.3683
3.904	10.49	960000	3.8604	0.3695
3.8965	10.81	990000	3.8509	0.3704
3.8788	11.14	1020000	3.8406	0.3717
3.8748	11.47	1050000	3.8329	0.3728
3.8638	11.8	1080000	3.8250	0.3733
3.8586	12.13	1110000	3.8203	0.3739
3.8495	12.45	1140000	3.8146	0.3746
3.8469	12.78	1170000	3.8054	0.3753
3.8352	13.11	1200000	3.8007	0.3761
3.8339	13.44	1230000	3.7949	0.3766
3.8215	13.76	1260000	3.7894	0.3772
3.8175	14.09	1290000	3.7835	0.3779
3.817	14.42	1320000	3.7768	0.3787

Framework versions

—Transformers 4.30.2
—Pytorch 1.14.0a0+410ce96
—Datasets 2.13.1
—Tokenizers 0.13.3