metadata

license: apache-2.0
base_model: distilbert-base-uncased
tags:
  - generated_from_trainer
datasets:
  - emotion
metrics:
  - accuracy
  - f1
model-index:
  - name: distilbert-base-uncased-finetuned-emotion
    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: 0.9385
          - name: F1
            type: f1
            value: 0.938496854642063

distilbert-base-uncased-finetuned-emotion

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: 0.1910
Accuracy: 0.9385
F1: 0.9385

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: 2e-05
train_batch_size: 256
eval_batch_size: 256
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
No log	1.0	63	0.3110	0.907	0.9066
No log	2.0	126	0.2093	0.9255	0.9264
No log	3.0	189	0.1683	0.931	0.9312
0.2667	4.0	252	0.1564	0.932	0.9319
0.2667	5.0	315	0.1541	0.9325	0.9328
0.2667	6.0	378	0.1577	0.9375	0.9378
0.2667	7.0	441	0.1547	0.9355	0.9357
0.0894	8.0	504	0.1528	0.9385	0.9386
0.0894	9.0	567	0.1630	0.9395	0.9394
0.0894	10.0	630	0.1745	0.9425	0.9427
0.0894	11.0	693	0.1635	0.9385	0.9385
0.0567	12.0	756	0.1706	0.938	0.9381
0.0567	13.0	819	0.1740	0.941	0.9413
0.0567	14.0	882	0.1766	0.94	0.9403
0.0567	15.0	945	0.1832	0.938	0.9382
0.0397	16.0	1008	0.1871	0.9385	0.9388
0.0397	17.0	1071	0.1889	0.938	0.9382
0.0397	18.0	1134	0.1908	0.935	0.9354
0.0397	19.0	1197	0.1907	0.94	0.9399
0.0284	20.0	1260	0.1910	0.9385	0.9385

Framework versions

Transformers 4.38.1
Pytorch 2.2.0+cu121
Datasets 2.17.1
Tokenizers 0.15.2