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

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

bert-base-uncased-sst2-jpqd-ov-int8 is a machine learning model from helenai. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

Library: transformers
License: apache-2.0
Model type: bert
Access: Public
Created: Jan 26, 2023
Updated: Sep 12, 2023
SHA: a9b3ee6d

Base models

Datasets

glue

Model card

From the Hugging Face model README

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

Library: transformers
Type: bert
License: apache-2.0
Languages: en
Created: Jan 26, 2023
Updated: Sep 12, 2023