README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - glue
metrics:
  - accuracy
  - f1
model-index:
  - name: natural-language-inference
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: glue
          type: glue
          config: mrpc
          split: train
          args: mrpc
        metrics:
          - type: accuracy
            value: 0.8284313725490197
            name: Accuracy
          - type: f1
            value: 0.8821548821548822
            name: F1
      - task:
          type: natural-language-inference
          name: Natural Language Inference
        dataset:
          name: glue
          type: glue
          config: mrpc
          split: validation
        metrics:
          - type: accuracy
            value: 0.8284313725490197
            name: Accuracy
            verified: true
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          - type: precision
            value: 0.8317460317460318
            name: Precision
            verified: true
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          - type: recall
            value: 0.9390681003584229
            name: Recall
            verified: true
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          - type: auc
            value: 0.8847906421049706
            name: AUC
            verified: true
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          - type: f1
            value: 0.8821548821548822
            name: F1
            verified: true
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          - type: loss
            value: 0.4118819236755371
            name: loss
            verified: true
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natural-language-inference

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

• Loss: 0.4120
• Accuracy: 0.8284
• F1: 0.8822

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: 16
• eval_batch_size: 16
• 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	230	0.4288	0.8039	0.8644
No log	2.0	460	0.4120	0.8284	0.8822

Framework versions

• Transformers 4.21.1
• Pytorch 1.12.1+cu113
• Datasets 2.4.0
• Tokenizers 0.12.1