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roberta-base-mrpc

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roberta-base-mrpc / README.md
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lewtun HF staff
Add evaluation results on the mrpc config and validation split of glue
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metadata
language:
  - en
license: mit
tags:
  - generated_from_trainer
datasets:
  - glue
metrics:
  - accuracy
  - f1
model-index:
  - name: roberta-base-mrpc
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: GLUE MRPC
          type: glue
          args: mrpc
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9019607843137255
          - name: F1
            type: f1
            value: 0.9295774647887324
      - task:
          type: natural-language-inference
          name: Natural Language Inference
        dataset:
          name: glue
          type: glue
          config: mrpc
          split: validation
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9019607843137255
            verified: true
          - name: Precision
            type: precision
            value: 0.9134948096885813
            verified: true
          - name: Recall
            type: recall
            value: 0.946236559139785
            verified: true
          - name: AUC
            type: auc
            value: 0.9536411880747964
            verified: true
          - name: F1
            type: f1
            value: 0.9295774647887324
            verified: true
          - name: loss
            type: loss
            value: 0.48942330479621887
            verified: true

mrpc

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

  • Loss: 0.4898
  • Accuracy: 0.9020
  • F1: 0.9296
  • Combined Score: 0.9158

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: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.06
  • num_epochs: 5.0

Training results

Framework versions

  • Transformers 4.20.0.dev0
  • Pytorch 1.11.0+cu113
  • Datasets 2.1.0
  • Tokenizers 0.12.1