README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - sentiment140
metrics:
  - accuracy
model-index:
  - name: Sentiment140_ALBERT_5E
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: sentiment140
          type: sentiment140
          config: sentiment140
          split: train
          args: sentiment140
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.8533333333333334

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