README.md

metadata

tags:
  - generated_from_trainer
datasets:
  - emotion
metrics:
  - accuracy
  - f1
model-index:
  - name: bertweet-base-finetuned-emotion
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: emotion
          type: emotion
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9365
          - name: F1
            type: f1
            value: 0.9371
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: emotion
          type: emotion
          config: default
          split: test
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.923
            verified: true
          - name: Precision Macro
            type: precision
            value: 0.8676576686813523
            verified: true
          - name: Precision Micro
            type: precision
            value: 0.923
            verified: true
          - name: Precision Weighted
            type: precision
            value: 0.9268406401714973
            verified: true
          - name: Recall Macro
            type: recall
            value: 0.8945488803260702
            verified: true
          - name: Recall Micro
            type: recall
            value: 0.923
            verified: true
          - name: Recall Weighted
            type: recall
            value: 0.923
            verified: true
          - name: F1 Macro
            type: f1
            value: 0.8798961895301041
            verified: true
          - name: F1 Micro
            type: f1
            value: 0.923
            verified: true
          - name: F1 Weighted
            type: f1
            value: 0.9241278880972197
            verified: true
          - name: loss
            type: loss
            value: 0.24626904726028442
            verified: true

distilbert-base-uncased-finetuned-emotion

This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.1995
• Accuracy: 0.9365
• F1: 0.9371

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: 64
• eval_batch_size: 64
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.475	1.0	503	0.2171	0.928	0.9292
0.1235	2.0	1006	0.1764	0.9365	0.9372
0.0802	3.0	1509	0.1788	0.938	0.9388
0.0531	4.0	2012	0.2005	0.938	0.9388
0.0367	5.0	2515	0.1995	0.9365	0.9371

Framework versions

• Transformers 4.13.0
• Pytorch 1.11.0+cu113
• Datasets 1.16.1
• Tokenizers 0.10.3