Sentiment140_ALBERT_5E

This model is a fine-tuned version of albert-base-v2 on the sentiment140 dataset. It achieves the following results on the evaluation set:

Loss: 0.6103
Accuracy: 0.8533

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
train_batch_size: 16
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

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.6713	0.08	50	0.5704	0.7333
0.5742	0.16	100	0.4620	0.8
0.5104	0.24	150	0.5536	0.74
0.5313	0.32	200	0.5198	0.76
0.5023	0.4	250	0.4286	0.8
0.4871	0.48	300	0.4294	0.8267
0.4513	0.56	350	0.4349	0.8133
0.4647	0.64	400	0.4046	0.8333
0.4827	0.72	450	0.4218	0.8333
0.4517	0.8	500	0.4093	0.82
0.4417	0.88	550	0.3999	0.84
0.4701	0.96	600	0.3779	0.8867
0.397	1.04	650	0.3730	0.8667
0.3377	1.12	700	0.3833	0.8333
0.411	1.2	750	0.3704	0.84
0.3796	1.28	800	0.3472	0.86
0.3523	1.36	850	0.3512	0.8733
0.3992	1.44	900	0.3712	0.84
0.3641	1.52	950	0.3718	0.82
0.3973	1.6	1000	0.3508	0.84
0.3576	1.68	1050	0.3600	0.86
0.3701	1.76	1100	0.3287	0.8667
0.3721	1.84	1150	0.3794	0.82
0.3673	1.92	1200	0.3378	0.8733
0.4223	2.0	1250	0.3508	0.86
0.2745	2.08	1300	0.3835	0.86
0.283	2.16	1350	0.3500	0.8533
0.2769	2.24	1400	0.3334	0.8733
0.2491	2.32	1450	0.3519	0.8867
0.3237	2.4	1500	0.3438	0.86
0.2662	2.48	1550	0.3513	0.8667
0.2423	2.56	1600	0.3413	0.8867
0.2655	2.64	1650	0.3126	0.8933
0.2516	2.72	1700	0.3333	0.8733
0.252	2.8	1750	0.3316	0.88
0.2872	2.88	1800	0.3227	0.9
0.306	2.96	1850	0.3383	0.8733
0.248	3.04	1900	0.3474	0.8733
0.1507	3.12	1950	0.4140	0.8667
0.1994	3.2	2000	0.3729	0.8533
0.167	3.28	2050	0.3782	0.8867
0.1872	3.36	2100	0.4352	0.8867
0.1611	3.44	2150	0.4511	0.8667
0.2338	3.52	2200	0.4244	0.8533
0.1538	3.6	2250	0.4226	0.8733
0.1561	3.68	2300	0.4126	0.88
0.2156	3.76	2350	0.4382	0.86
0.1684	3.84	2400	0.4969	0.86
0.1917	3.92	2450	0.4439	0.8667
0.1584	4.0	2500	0.4759	0.86
0.1038	4.08	2550	0.5042	0.8667
0.0983	4.16	2600	0.5527	0.8533
0.1404	4.24	2650	0.5801	0.84
0.0844	4.32	2700	0.5884	0.86
0.1347	4.4	2750	0.5865	0.8467
0.1373	4.48	2800	0.5915	0.8533
0.1506	4.56	2850	0.5976	0.8467
0.1007	4.64	2900	0.6678	0.82
0.1311	4.72	2950	0.6082	0.8533
0.1402	4.8	3000	0.6180	0.8467
0.1363	4.88	3050	0.6107	0.8533
0.0995	4.96	3100	0.6103	0.8533

Framework versions

Transformers 4.24.0
Pytorch 1.13.0
Datasets 2.3.2
Tokenizers 0.13.1

Etelis/Sentiment140_ALBERT_5E

Sentiment140_ALBERT_5E

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions