metadata

license: apache-2.0
base_model: bert-base-uncased
tags:
  - generated_from_trainer
datasets:
  - super_glue
metrics:
  - accuracy
model-index:
  - name: superglue_rte-bert-base-uncased
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: super_glue
          type: super_glue
          config: rte
          split: validation
          args: rte
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.6739130434782609

superglue_rte-bert-base-uncased

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

Loss: 1.5070
Accuracy: 0.6739

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: 5e-05
train_batch_size: 4
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.704	1.0	623	0.6653	0.6159
0.6848	2.0	1246	0.7144	0.4203
0.7083	3.0	1869	0.6922	0.5797
0.7014	4.0	2492	0.7327	0.6232
0.6528	5.0	3115	0.6727	0.6522
0.6471	6.0	3738	0.8413	0.6159
0.5872	7.0	4361	0.8780	0.5507
0.5954	8.0	4984	0.7604	0.6377
0.5566	9.0	5607	0.8578	0.6812
0.5576	10.0	6230	2.0498	0.5362
0.4923	11.0	6853	1.4097	0.6304
0.5688	12.0	7476	1.4146	0.6667
0.433	13.0	8099	1.3354	0.6594
0.4259	14.0	8722	1.3271	0.6957
0.3869	15.0	9345	1.2881	0.6812
0.3641	16.0	9968	1.4485	0.6739
0.3292	17.0	10591	1.3445	0.6739
0.3734	18.0	11214	1.4917	0.6739
0.3227	19.0	11837	1.5281	0.6739
0.3133	20.0	12460	1.5070	0.6739

Framework versions

Transformers 4.32.1
Pytorch 1.13.0+cu117
Datasets 2.15.0
Tokenizers 0.13.3