README.md

metadata

language:
  - en
license: mit
tags:
  - generated_from_trainer
datasets:
  - glue
metrics:
  - accuracy
  - f1
model-index:
  - name: roberta-base-qqp
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: GLUE QQP
          type: glue
          args: qqp
        metrics:
          - type: accuracy
            value: 0.9152609448429384
            name: Accuracy
          - type: f1
            value: 0.8867138416771377
            name: F1
      - task:
          type: natural-language-inference
          name: Natural Language Inference
        dataset:
          name: glue
          type: glue
          config: qqp
          split: validation
        metrics:
          - type: accuracy
            value: 0.9153104130596093
            name: Accuracy
            verified: true
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          - type: precision
            value: 0.8732009117551286
            name: Precision
            verified: true
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          - type: recall
            value: 0.9007725898555593
            name: Recall
            verified: true
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          - type: auc
            value: 0.9685235648551861
            name: AUC
            verified: true
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          - type: f1
            value: 0.8867724867724867
            name: F1
            verified: true
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          - type: loss
            value: 0.4435121417045593
            name: loss
            verified: true
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roberta-base-qqp

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

• Loss: 0.4435
• Accuracy: 0.9153
• F1: 0.8867
• Combined Score: 0.9010

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: 10.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Combined Score
0.2751	1.0	22741	0.3057	0.8905	0.8512	0.8709
0.2443	2.0	45482	0.2530	0.9005	0.8710	0.8857
0.2157	3.0	68223	0.2643	0.9070	0.8769	0.8919
0.1838	4.0	90964	0.2806	0.9109	0.8815	0.8962
0.146	5.0	113705	0.3277	0.9113	0.8809	0.8961
0.1262	6.0	136446	0.3939	0.9113	0.8812	0.8962
0.0867	7.0	159187	0.4435	0.9153	0.8867	0.9010
0.0757	8.0	181928	0.4812	0.9147	0.8844	0.8996
0.0479	9.0	204669	0.5081	0.9151	0.8871	0.9011
0.0379	10.0	227410	0.5647	0.9149	0.8858	0.9003

Framework versions

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