README.md

metadata

language:
  - en
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - nyu-mll/glue
metrics:
  - accuracy
  - f1
model-index:
  - name: glue-mrpc
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: GLUE MRPC
          type: glue
          args: mrpc
        metrics:
          - type: accuracy
            value: 0.8553921568627451
            name: Accuracy
          - type: f1
            value: 0.897391304347826
            name: F1
      - task:
          type: natural-language-inference
          name: Natural Language Inference
        dataset:
          name: glue
          type: glue
          config: mrpc
          split: validation
        metrics:
          - type: accuracy
            value: 0.8553921568627451
            name: Accuracy
            verified: true
          - type: precision
            value: 0.8716216216216216
            name: Precision
            verified: true
          - type: recall
            value: 0.9247311827956989
            name: Recall
            verified: true
          - type: auc
            value: 0.90464282737351
            name: AUC
            verified: true
          - type: f1
            value: 0.897391304347826
            name: F1
            verified: true
          - type: loss
            value: 0.6564616560935974
            name: loss
            verified: true

glue-mrpc

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

• Loss: 0.6566
• Accuracy: 0.8554
• F1: 0.8974
• Combined Score: 0.8764

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: 5e-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: 3.0

Training results

Framework versions

• Transformers 4.13.0.dev0
• Pytorch 1.10.0+cu102
• Datasets 1.15.2.dev0
• Tokenizers 0.10.3