metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - clinc_oos
metrics:
  - accuracy
model-index:
  - name: distilbert-base-uncased-distilled-clinc
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: clinc_oos
          type: clinc_oos
          args: plus
        metrics:
          - type: accuracy
            value: 0.9332258064516129
            name: Accuracy
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: clinc_oos
          type: clinc_oos
          config: small
          split: test
        metrics:
          - type: accuracy
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            name: Accuracy
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          - type: precision
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          - type: precision
            value: 0.8797945801452213
            name: Precision Weighted
            verified: true
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          - type: recall
            value: 0.9359690949227375
            name: Recall Macro
            verified: true
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          - type: recall
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          - type: recall
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          - type: f1
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          - type: loss
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            name: loss
            verified: true
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distilbert-base-uncased-distilled-clinc

This model is a fine-tuned version of distilbert-base-uncased on the clinc_oos dataset. It achieves the following results on the evaluation set:

Loss: 0.1259
Accuracy: 0.9332

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: 48
eval_batch_size: 48
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 7

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	318	0.5952	0.7355
0.7663	2.0	636	0.3130	0.8742
0.7663	3.0	954	0.2024	0.9206
0.3043	4.0	1272	0.1590	0.9235
0.181	5.0	1590	0.1378	0.9303
0.181	6.0	1908	0.1287	0.9329
0.1468	7.0	2226	0.1259	0.9332

Framework versions

Transformers 4.16.2
Pytorch 1.10.2+cu102
Datasets 1.18.3
Tokenizers 0.11.0