README.md

metadata

language:
  - en
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - glue
metrics:
  - accuracy
  - f1
base_model: bert-base-uncased
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: 0.8578431372549019
            name: Accuracy
          - type: f1
            value: 0.9023569023569024
            name: F1
      - task:
          type: natural-language-inference
          name: Natural Language Inference
        dataset:
          name: glue
          type: glue
          config: mrpc
          split: validation
        metrics:
          - type: accuracy
            value: 0.8578431372549019
            name: Accuracy
            verified: true
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          - type: precision
            value: 0.8507936507936508
            name: Precision
            verified: true
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          - type: recall
            value: 0.9605734767025089
            name: Recall
            verified: true
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          - type: auc
            value: 0.8931260592926008
            name: AUC
            verified: true
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          - type: f1
            value: 0.9023569023569024
            name: F1
            verified: true
            verifyToken: >-
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          - type: loss
            value: 0.5572634935379028
            name: loss
            verified: true
            verifyToken: >-
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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: 0.5572
• Accuracy: 0.8578
• F1: 0.9024
• Combined Score: 0.8801

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

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Combined Score
No log	1.0	230	0.4111	0.8088	0.8704	0.8396
No log	2.0	460	0.3762	0.8480	0.8942	0.8711
0.4287	3.0	690	0.5572	0.8578	0.9024	0.8801
0.4287	4.0	920	0.6087	0.8554	0.8977	0.8766
0.1172	5.0	1150	0.6524	0.8456	0.8901	0.8678

Framework versions

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