metadata

license: mit
tags:
  - generated_from_trainer
datasets:
  - sentiment140
metrics:
  - accuracy
model-index:
  - name: Sentiment140_roBERTa_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: 0.8933333333333333

Sentiment140_roBERTa_5E

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

Loss: 0.4796
Accuracy: 0.8933

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
0.699	0.08	50	0.6734	0.5467
0.6099	0.16	100	0.4322	0.8
0.4906	0.24	150	0.3861	0.84
0.4652	0.32	200	0.4288	0.7933
0.4874	0.4	250	0.3872	0.84
0.4735	0.48	300	0.3401	0.8667
0.3909	0.56	350	0.3484	0.84
0.4277	0.64	400	0.3207	0.88
0.3894	0.72	450	0.3310	0.8733
0.4523	0.8	500	0.3389	0.8667
0.4087	0.88	550	0.3515	0.8467
0.3973	0.96	600	0.3513	0.8467
0.4016	1.04	650	0.3501	0.8667
0.3613	1.12	700	0.3327	0.8667
0.343	1.2	750	0.3518	0.86
0.314	1.28	800	0.3555	0.88
0.3407	1.36	850	0.3849	0.86
0.2944	1.44	900	0.3576	0.8667
0.3267	1.52	950	0.3461	0.8733
0.3251	1.6	1000	0.3411	0.8667
0.321	1.68	1050	0.3371	0.88
0.3057	1.76	1100	0.3322	0.88
0.3335	1.84	1150	0.3106	0.8667
0.3363	1.92	1200	0.3158	0.8933
0.2972	2.0	1250	0.3122	0.88
0.2453	2.08	1300	0.3327	0.8867
0.2467	2.16	1350	0.3767	0.8667
0.273	2.24	1400	0.3549	0.8667
0.2672	2.32	1450	0.3470	0.88
0.2352	2.4	1500	0.4092	0.8667
0.2763	2.48	1550	0.3472	0.9
0.2858	2.56	1600	0.3440	0.9
0.2206	2.64	1650	0.3770	0.88
0.2928	2.72	1700	0.3280	0.8867
0.2478	2.8	1750	0.3426	0.8867
0.2362	2.88	1800	0.3578	0.8933
0.2107	2.96	1850	0.3986	0.8933
0.2191	3.04	1900	0.3819	0.8933
0.2267	3.12	1950	0.4047	0.8867
0.2076	3.2	2000	0.4303	0.8867
0.1868	3.28	2050	0.4385	0.8933
0.2239	3.36	2100	0.4175	0.8933
0.2082	3.44	2150	0.4142	0.8933
0.2423	3.52	2200	0.4002	0.8867
0.1878	3.6	2250	0.4662	0.88
0.1892	3.68	2300	0.4783	0.88
0.2259	3.76	2350	0.4487	0.88
0.1859	3.84	2400	0.4456	0.8933
0.2042	3.92	2450	0.4468	0.8933
0.2096	4.0	2500	0.4153	0.8867
0.178	4.08	2550	0.4100	0.8933
0.1621	4.16	2600	0.4292	0.8933
0.1682	4.24	2650	0.4602	0.8933
0.1813	4.32	2700	0.4680	0.8933
0.2033	4.4	2750	0.4735	0.8933
0.1662	4.48	2800	0.4750	0.88
0.1686	4.56	2850	0.4830	0.8933
0.1603	4.64	2900	0.4909	0.8933
0.148	4.72	2950	0.4784	0.8933
0.162	4.8	3000	0.4750	0.8867
0.153	4.88	3050	0.4759	0.8867
0.1657	4.96	3100	0.4796	0.8933

Framework versions

Transformers 4.24.0
Pytorch 1.13.0
Datasets 2.3.2
Tokenizers 0.13.1