README.md

metadata

language:
  - en
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - glue
metrics:
  - accuracy
base_model: bert-base-uncased
model-index:
  - name: bert-base-uncased-rte
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: GLUE RTE
          type: glue
          args: rte
        metrics:
          - type: accuracy
            value: 0.6895306859205776
            name: Accuracy
      - task:
          type: natural-language-inference
          name: Natural Language Inference
        dataset:
          name: glue
          type: glue
          config: rte
          split: validation
        metrics:
          - type: accuracy
            value: 0.6823104693140795
            name: Accuracy
            verified: true
            verifyToken: >-
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          - type: precision
            value: 0.7047619047619048
            name: Precision
            verified: true
            verifyToken: >-
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          - type: recall
            value: 0.5648854961832062
            name: Recall
            verified: true
            verifyToken: >-
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          - type: auc
            value: 0.7394646031580048
            name: AUC
            verified: true
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          - type: f1
            value: 0.6271186440677967
            name: F1
            verified: true
            verifyToken: >-
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          - type: loss
            value: 0.7001310586929321
            name: loss
            verified: true
            verifyToken: >-
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bert-base-uncased-rte

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

• Loss: 0.6972
• Accuracy: 0.6895

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
• num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	156	0.6537	0.6318
No log	2.0	312	0.6383	0.6534
No log	3.0	468	0.6972	0.6895

Framework versions

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