metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - emotion
metrics:
  - accuracy
model-index:
  - name: xtremedistil-emotion
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: emotion
          type: emotion
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9265
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: emotion
          type: emotion
          config: default
          split: test
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.926
            verified: true
          - name: Precision Macro
            type: precision
            value: 0.8855308537052737
            verified: true
          - name: Precision Micro
            type: precision
            value: 0.926
            verified: true
          - name: Precision Weighted
            type: precision
            value: 0.9281282413639949
            verified: true
          - name: Recall Macro
            type: recall
            value: 0.8969894921856228
            verified: true
          - name: Recall Micro
            type: recall
            value: 0.926
            verified: true
          - name: Recall Weighted
            type: recall
            value: 0.926
            verified: true
          - name: F1 Macro
            type: f1
            value: 0.8903400738742536
            verified: true
          - name: F1 Micro
            type: f1
            value: 0.926
            verified: true
          - name: F1 Weighted
            type: f1
            value: 0.9265018282649476
            verified: true
          - name: loss
            type: loss
            value: 0.2258329838514328
            verified: true

xtremedistil-emotion

This model is a fine-tuned version of microsoft/xtremedistil-l6-h256-uncased on the emotion dataset. It achieves the following results on the evaluation set:

Accuracy: 0.9265

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 3e-05
train_batch_size: 128
eval_batch_size: 8
seed: 42
num_epochs: 24

Training results

Epoch	Training Loss	Validation Loss	Accuracy
1	No log	1.238589	0.609000
2	No log	0.934423	0.714000
3	No log	0.768701	0.742000
4	1.074800	0.638208	0.805500
5	1.074800	0.551363	0.851500
6	1.074800	0.476291	0.875500
7	1.074800	0.427313	0.883500
8	0.531500	0.392633	0.886000
9	0.531500	0.357979	0.892000
10	0.531500	0.330304	0.899500
11	0.531500	0.304529	0.907000
12	0.337200	0.287447	0.918000
13	0.337200	0.277067	0.921000
14	0.337200	0.259483	0.921000
15	0.337200	0.257564	0.916500
16	0.246200	0.241970	0.919500
17	0.246200	0.241537	0.921500
18	0.246200	0.235705	0.924500
19	0.246200	0.237325	0.920500
20	0.201400	0.229699	0.923500
21	0.201400	0.227426	0.923000
22	0.201400	0.228554	0.924000
23	0.201400	0.226941	0.925500
24	0.184300	0.225816	0.926500