helenai/bert-base-uncased-sst2-jpqd-ov-int8

jpqd-bert-base-ft-sst2

This model is a fine-tuned version of bert-base-uncased on the GLUE SST2 dataset.

It was compressed with NNCF following the Optimum JPQD text-classification example

It achieves the following results on the evaluation set:

• Loss: 0.2798
• Accuracy: 0.9163

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 32
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 5.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.392	0.12	250	0.4535	0.8888
0.4413	0.24	500	0.4671	0.8899
0.29	0.36	750	0.3285	0.9128
0.2851	0.48	1000	0.2498	0.9151
0.3717	0.59	1250	0.2037	0.9243
0.2467	0.71	1500	0.2840	0.9174
0.2114	0.83	1750	0.2239	0.9243
0.1777	0.95	2000	0.1968	0.9266
2.6501	1.07	2250	2.8219	0.9255
6.4768	1.19	2500	6.5765	0.8979
9.3594	1.31	2750	9.4648	0.8819
11.5481	1.43	3000	11.5391	0.8567
12.7541	1.54	3250	12.8359	0.8578
13.6184	1.66	3500	13.6519	0.8429
13.9171	1.78	3750	14.0734	0.8475
13.9601	1.9	4000	14.1024	0.8578
0.2701	2.02	4250	0.3354	0.9048
0.2689	2.14	4500	0.3320	0.9048
0.1775	2.26	4750	0.2838	0.9163
0.1648	2.38	5000	0.2842	0.9128
0.1316	2.49	5250	0.2750	0.9163
0.2349	2.61	5500	0.2405	0.9232
0.066	2.73	5750	0.2695	0.9174
0.1285	2.85	6000	0.3017	0.9094
0.1813	2.97	6250	0.3472	0.9106
0.078	3.09	6500	0.2915	0.9140
0.0886	3.21	6750	0.2853	0.9151
0.117	3.33	7000	0.2689	0.9186
0.0894	3.44	7250	0.2748	0.9174
0.1023	3.56	7500	0.3279	0.9094
0.0495	3.68	7750	0.2988	0.9151
0.0899	3.8	8000	0.2796	0.9174
0.1102	3.92	8250	0.2667	0.9163
0.061	4.04	8500	0.2837	0.9174
0.0594	4.16	8750	0.2766	0.9151
0.1062	4.28	9000	0.2777	0.9140
0.0751	4.39	9250	0.2690	0.9220
0.0386	4.51	9500	0.2668	0.9163
0.0284	4.63	9750	0.2812	0.9186
0.1016	4.75	10000	0.2825	0.9163
0.0507	4.87	10250	0.2805	0.9140
0.0709	4.99	10500	0.2855	0.9140

Framework versions

• Transformers 4.26.1
• Pytorch 1.13.1+cu117
• Datasets 2.8.0
• Tokenizers 0.13.2