README.md

metadata

license: apache-2.0
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
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.8885
          - name: F1
            type: f1
            value: 0.8818845305609924
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: emotion
          type: emotion
          config: default
          split: test
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.892
            verified: true
          - name: Precision Macro
            type: precision
            value: 0.8923475194643138
            verified: true
          - name: Precision Micro
            type: precision
            value: 0.892
            verified: true
          - name: Precision Weighted
            type: precision
            value: 0.894495118514709
            verified: true
          - name: Recall Macro
            type: recall
            value: 0.768240931585822
            verified: true
          - name: Recall Micro
            type: recall
            value: 0.892
            verified: true
          - name: Recall Weighted
            type: recall
            value: 0.892
            verified: true
          - name: F1 Macro
            type: f1
            value: 0.7897026729904524
            verified: true
          - name: F1 Micro
            type: f1
            value: 0.892
            verified: true
          - name: F1 Weighted
            type: f1
            value: 0.8842367889371163
            verified: true
          - name: loss
            type: loss
            value: 0.34626322984695435
            verified: true
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: emotion
          type: emotion
          config: default
          split: validation
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.8885
            verified: true
          - name: Precision Macro
            type: precision
            value: 0.8849064522901132
            verified: true
          - name: Precision Micro
            type: precision
            value: 0.8885
            verified: true
          - name: Precision Weighted
            type: precision
            value: 0.8922726271705158
            verified: true
          - name: Recall Macro
            type: recall
            value: 0.7854833401719518
            verified: true
          - name: Recall Micro
            type: recall
            value: 0.8885
            verified: true
          - name: Recall Weighted
            type: recall
            value: 0.8885
            verified: true
          - name: F1 Macro
            type: f1
            value: 0.8031492596189961
            verified: true
          - name: F1 Micro
            type: f1
            value: 0.8885
            verified: true
          - name: F1 Weighted
            type: f1
            value: 0.8818845305609924
            verified: true
          - name: loss
            type: loss
            value: 0.36373236775398254
            verified: true

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: 0.3663
• Accuracy: 0.8885
• F1: 0.8819

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: 128
• eval_batch_size: 128
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
No log	1.0	125	0.5574	0.822	0.7956
0.7483	2.0	250	0.3663	0.8885	0.8819

Framework versions

• Transformers 4.18.0
• Pytorch 1.10.1+cu111
• Datasets 2.1.0
• Tokenizers 0.12.1