metadata

language:
  - en
license: mit
tags:
  - generated_from_trainer
datasets:
  - glue
metrics:
  - accuracy
base_model: roberta-base
model-index:
  - name: roberta-base-qnli
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: GLUE QNLI
          type: glue
          args: qnli
        metrics:
          - type: accuracy
            value: 0.9245835621453414
            name: Accuracy
      - task:
          type: natural-language-inference
          name: Natural Language Inference
        dataset:
          name: glue
          type: glue
          config: qnli
          split: validation
        metrics:
          - type: accuracy
            value: 0.924400512538898
            name: Accuracy
            verified: true
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          - type: precision
            value: 0.9171997157071784
            name: Precision
            verified: true
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          - type: recall
            value: 0.9348062296269467
            name: Recall
            verified: true
            verifyToken: >-
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          - type: auc
            value: 0.9744865501321541
            name: AUC
            verified: true
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          - type: f1
            value: 0.9259192825112107
            name: F1
            verified: true
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          - type: loss
            value: 0.2990749478340149
            name: loss
            verified: true
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roberta-base-qnli

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

Loss: 0.2992
Accuracy: 0.9246

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
0.2986	1.0	6547	0.2215	0.9171
0.243	2.0	13094	0.2321	0.9173
0.2048	3.0	19641	0.2992	0.9246
0.1629	4.0	26188	0.3538	0.9220
0.1308	5.0	32735	0.3533	0.9209
0.0846	6.0	39282	0.4277	0.9229

Framework versions

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