README.md

metadata

language:
  - en
license: mit
tags:
  - generated_from_trainer
datasets:
  - glue
metrics:
  - accuracy
model-index:
  - name: roberta-base-rte
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: GLUE RTE
          type: glue
          args: rte
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.7978339350180506
      - task:
          type: natural-language-inference
          name: Natural Language Inference
        dataset:
          name: glue
          type: glue
          config: rte
          split: validation
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.7906137184115524
            verified: true
          - name: Precision
            type: precision
            value: 0.7552447552447552
            verified: true
          - name: Recall
            type: recall
            value: 0.8244274809160306
            verified: true
          - name: AUC
            type: auc
            value: 0.8564258078008994
            verified: true
          - name: F1
            type: f1
            value: 0.7883211678832117
            verified: true
          - name: loss
            type: loss
            value: 0.5560466051101685
            verified: true

roberta-base-rte

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

• Loss: 0.5446
• Accuracy: 0.7978

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: 1e-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
No log	1.0	156	0.7023	0.4729
No log	2.0	312	0.6356	0.6895
No log	3.0	468	0.5177	0.7617
0.6131	4.0	624	0.6238	0.7473
0.6131	5.0	780	0.5446	0.7978
0.6131	6.0	936	0.9697	0.7545
0.2528	7.0	1092	1.1004	0.7690
0.2528	8.0	1248	1.1937	0.7726
0.2528	9.0	1404	1.3313	0.7726
0.1073	10.0	1560	1.3534	0.7726

Framework versions

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