DBERT_Emotions_tuned

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

• Loss: 0.1828
• Accuracy: 0.925

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: 5e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.1	100	0.7513	0.7365
No log	0.2	200	0.3693	0.8895
No log	0.3	300	0.3118	0.906
No log	0.4	400	0.3048	0.9055
0.5368	0.5	500	0.2649	0.9225
0.5368	0.6	600	0.2192	0.9235
0.5368	0.7	700	0.2254	0.9245
0.5368	0.8	800	0.2016	0.931
0.5368	0.9	900	0.1685	0.935
0.2254	1.0	1000	0.1926	0.9295
0.2254	1.1	1100	0.2128	0.928
0.2254	1.2	1200	0.2008	0.9325
0.2254	1.3	1300	0.1662	0.9385
0.2254	1.4	1400	0.1945	0.939
0.1315	1.5	1500	0.1652	0.939
0.1315	1.6	1600	0.1820	0.938
0.1315	1.7	1700	0.1660	0.938
0.1315	1.8	1800	0.1590	0.93
0.1315	1.9	1900	0.1601	0.935
0.1295	2.0	2000	0.1645	0.9345
0.1295	2.1	2100	0.1845	0.9305
0.1295	2.2	2200	0.1784	0.9355
0.1295	2.3	2300	0.2042	0.9365
0.1295	2.4	2400	0.1852	0.9365
0.0891	2.5	2500	0.1797	0.94
0.0891	2.6	2600	0.1741	0.9365
0.0891	2.7	2700	0.1758	0.9385
0.0891	2.8	2800	0.1771	0.944
0.0891	2.9	2900	0.1688	0.9385
0.0848	3.0	3000	0.1671	0.94

Framework versions

• Transformers 4.38.2
• Pytorch 2.1.0+cu121
• Datasets 2.18.0
• Tokenizers 0.15.2