README.md

metadata

language:
  - en
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - glue
metrics:
  - accuracy
  - f1
model-index:
  - name: bert-finetuned-mrpc
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: GLUE MRPC
          type: glue
          args: mrpc
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.8602941176470589
          - name: F1
            type: f1
            value: 0.9032258064516129
      - task:
          type: natural-language-inference
          name: Natural Language Inference
        dataset:
          name: glue
          type: glue
          config: mrpc
          split: validation
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.8602941176470589
            verified: true
          - name: Precision
            type: precision
            value: 0.8580645161290322
            verified: true
          - name: Recall
            type: recall
            value: 0.953405017921147
            verified: true
          - name: AUC
            type: auc
            value: 0.9257731099441527
            verified: true
          - name: F1
            type: f1
            value: 0.9032258064516129
            verified: true
          - name: loss
            type: loss
            value: 0.5150377154350281
            verified: true

bert-finetuned-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: 0.5152
• Accuracy: 0.8603
• F1: 0.9032
• Combined Score: 0.8818

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: 8
• eval_batch_size: 8
• seed: 42
• distributed_type: multi-GPU
• num_devices: 2
• total_train_batch_size: 16
• total_eval_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Combined Score
No log	1.0	230	0.3668	0.8431	0.8881	0.8656
No log	2.0	460	0.3751	0.8578	0.9017	0.8798
0.4264	3.0	690	0.5152	0.8603	0.9032	0.8818

Framework versions

• Transformers 4.11.0.dev0
• Pytorch 1.8.1+cu111
• Datasets 1.10.3.dev0
• Tokenizers 0.10.3