metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - sentiment140
metrics:
  - accuracy
model-index:
  - name: Sentiment140_DistilBERT_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: 0.8333333333333334

Sentiment140_DistilBERT_5E

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

Loss: 0.4897
Accuracy: 0.8333

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
0.6784	0.08	50	0.6516	0.6933
0.6301	0.16	100	0.5384	0.7533
0.5438	0.24	150	0.4559	0.8
0.4625	0.32	200	0.4287	0.8133
0.4528	0.4	250	0.4056	0.8267
0.4609	0.48	300	0.3883	0.8333
0.4705	0.56	350	0.3886	0.8067
0.4539	0.64	400	0.3967	0.82
0.4483	0.72	450	0.3758	0.82
0.4699	0.8	500	0.4003	0.8133
0.467	0.88	550	0.4021	0.8267
0.454	0.96	600	0.3735	0.8333
0.4227	1.04	650	0.3840	0.8267
0.3584	1.12	700	0.3775	0.8333
0.3618	1.2	750	0.4026	0.8267
0.3634	1.28	800	0.3891	0.8133
0.3751	1.36	850	0.3895	0.8267
0.3484	1.44	900	0.3919	0.8267
0.3764	1.52	950	0.3770	0.84
0.3488	1.6	1000	0.4028	0.82
0.3665	1.68	1050	0.3779	0.8333
0.3925	1.76	1100	0.3726	0.84
0.3624	1.84	1150	0.3655	0.84
0.3876	1.92	1200	0.3648	0.8133
0.3935	2.0	1250	0.3633	0.8467
0.2944	2.08	1300	0.3808	0.8333
0.2957	2.16	1350	0.3836	0.8333
0.266	2.24	1400	0.3940	0.8267
0.2747	2.32	1450	0.3952	0.84
0.314	2.4	1500	0.4060	0.8133
0.3419	2.48	1550	0.4025	0.8133
0.2782	2.56	1600	0.4218	0.82
0.3218	2.64	1650	0.4039	0.8333
0.2863	2.72	1700	0.4130	0.8267
0.3336	2.8	1750	0.4026	0.8133
0.3224	2.88	1800	0.3910	0.8267
0.2709	2.96	1850	0.3979	0.84
0.2701	3.04	1900	0.4127	0.8333
0.2782	3.12	1950	0.4335	0.82
0.2425	3.2	2000	0.4229	0.8333
0.2457	3.28	2050	0.4168	0.8333
0.217	3.36	2100	0.4264	0.8267
0.2522	3.44	2150	0.4250	0.8333
0.2402	3.52	2200	0.4371	0.8333
0.2465	3.6	2250	0.4429	0.8333
0.2427	3.68	2300	0.4435	0.8333
0.2408	3.76	2350	0.4500	0.84
0.1976	3.84	2400	0.4536	0.8333
0.23	3.92	2450	0.4645	0.8333
0.2449	4.0	2500	0.4557	0.8467
0.1933	4.08	2550	0.4672	0.84
0.213	4.16	2600	0.4717	0.84
0.1772	4.24	2650	0.4843	0.8267
0.1917	4.32	2700	0.4690	0.8467
0.2094	4.4	2750	0.4728	0.8467
0.1903	4.48	2800	0.4755	0.8467
0.2541	4.56	2850	0.4791	0.84
0.1805	4.64	2900	0.4877	0.84
0.2183	4.72	2950	0.4940	0.8267
0.2257	4.8	3000	0.4905	0.8333
0.2496	4.88	3050	0.4883	0.84
0.1846	4.96	3100	0.4897	0.8333

Framework versions

Transformers 4.24.0
Pytorch 1.12.1+cu113
Datasets 2.6.1
Tokenizers 0.13.1