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: 0.936
          - name: F1
            type: f1
            value: 0.9361019232595149

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: 0.1538
Accuracy: 0.936
F1: 0.9361

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: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
No log	1.0	63	0.8113	0.745	0.6916
No log	2.0	126	0.2909	0.911	0.9107
No log	3.0	189	0.2003	0.928	0.9277
0.5629	4.0	252	0.1698	0.938	0.9376
0.5629	5.0	315	0.1561	0.9365	0.9364
0.5629	6.0	378	0.1531	0.933	0.9334
0.5629	7.0	441	0.1584	0.9355	0.9345
0.1065	8.0	504	0.1493	0.9325	0.9321
0.1065	9.0	567	0.1504	0.936	0.9364
0.1065	10.0	630	0.1481	0.9395	0.9395
0.1065	11.0	693	0.1501	0.935	0.9353
0.0684	12.0	756	0.1504	0.936	0.9360
0.0684	13.0	819	0.1526	0.935	0.9352
0.0684	14.0	882	0.1526	0.9355	0.9357
0.0684	15.0	945	0.1538	0.936	0.9361

Framework versions

Transformers 4.37.2
Pytorch 2.1.0+cu121
Datasets 2.14.6
Tokenizers 0.15.2