metadata

language:
  - en
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - glue
metrics:
  - accuracy
  - f1
model-index:
  - name: distilbert_sa_GLUE_Experiment_data_aug_qqp_256
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: GLUE QQP
          type: glue
          args: qqp
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.7887954489240663
          - name: F1
            type: f1
            value: 0.7301115711621732

distilbert_sa_GLUE_Experiment_data_aug_qqp_256

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

Loss: 0.5126
Accuracy: 0.7888
F1: 0.7301
Combined Score: 0.7595

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: 256
eval_batch_size: 256
seed: 10
distributed_type: multi-GPU
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 50
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Combined Score
0.3952	1.0	29671	0.5126	0.7888	0.7301	0.7595
0.2233	2.0	59342	0.5941	0.7960	0.7346	0.7653
0.147	3.0	89013	0.6603	0.7997	0.7340	0.7668
0.1067	4.0	118684	0.7091	0.8012	0.7376	0.7694
0.082	5.0	148355	0.8757	0.8000	0.7377	0.7688
0.0652	6.0	178026	0.8332	0.8044	0.7379	0.7711

Framework versions

Transformers 4.26.0
Pytorch 1.14.0a0+410ce96
Datasets 2.9.0
Tokenizers 0.13.2