metadata

license: apache-2.0
base_model: distilbert-base-uncased
tags:
  - generated_from_trainer
datasets:
  - emotion
metrics:
  - accuracy
model-index:
  - name: DBERT_Emotions_tuned
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: emotion
          type: emotion
          config: split
          split: validation
          args: split
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.925

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