README.md

metadata

language:
  - en
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - glue
metrics:
  - accuracy
  - f1
model-index:
  - name: bert-base-uncased-mrpc
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: GLUE MRPC
          type: glue
          args: mrpc
        metrics:
          - type: accuracy
            value: 0.8602941176470589
            name: Accuracy
          - type: f1
            value: 0.9042016806722689
            name: F1
      - task:
          type: natural-language-inference
          name: Natural Language Inference
        dataset:
          name: glue
          type: glue
          config: mrpc
          split: validation
        metrics:
          - type: accuracy
            value: 0.8602941176470589
            name: Accuracy
            verified: true
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          - type: precision
            value: 0.8512658227848101
            name: Precision
            verified: true
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          - type: recall
            value: 0.96415770609319
            name: Recall
            verified: true
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          - type: auc
            value: 0.8985718651885194
            name: AUC
            verified: true
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          - type: f1
            value: 0.9042016806722689
            name: F1
            verified: true
            verifyToken: >-
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          - type: loss
            value: 0.6978028416633606
            name: loss
            verified: true
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bert-base-uncased-mrpc

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

• Loss: 0.6978
• Accuracy: 0.8603
• F1: 0.9042
• Combined Score: 0.8822

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 16
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 5.0

Framework versions

• Transformers 4.17.0
• Pytorch 1.10.0+cu102
• Datasets 1.14.0
• Tokenizers 0.11.6