metadata

language:
  - en
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - glue
metrics:
  - accuracy
  - f1
model-index:
  - name: bert-finetuned-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: 0.9032258064516129
            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: 0.8580645161290322
            name: Precision
            verified: true
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          - type: recall
            value: 0.953405017921147
            name: Recall
            verified: true
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          - type: auc
            value: 0.9257731099441527
            name: AUC
            verified: true
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          - type: f1
            value: 0.9032258064516129
            name: F1
            verified: true
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          - type: loss
            value: 0.5150377154350281
            name: loss
            verified: true
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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