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-emotions-dataset-2
    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: 0.937
          - name: F1
            type: f1
            value: 0.9369365562537507

distilbert-base-uncased-finetuned-emotions-dataset-2

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: 0.2689
Accuracy: 0.937
F1: 0.9369

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: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.6095	1.0	500	0.2145	0.9245	0.9250
0.1686	2.0	1000	0.1670	0.933	0.9316
0.1164	3.0	1500	0.1631	0.939	0.9394
0.0924	4.0	2000	0.1829	0.938	0.9366
0.072	5.0	2500	0.1929	0.9355	0.9354
0.0545	6.0	3000	0.2117	0.9355	0.9357
0.0432	7.0	3500	0.2222	0.934	0.9340
0.0298	8.0	4000	0.2553	0.939	0.9385
0.0248	9.0	4500	0.2667	0.936	0.9358
0.0199	10.0	5000	0.2689	0.937	0.9369

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.16.0
Tokenizers 0.15.0