metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - glue
metrics:
  - accuracy
  - f1
model-index:
  - name: distilbert-mrpc
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: glue
          type: glue
          args: mrpc
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.8480392156862745
          - name: F1
            type: f1
            value: 0.8934707903780068

distilbert-mrpc

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

Loss: 0.6783
Accuracy: 0.8480
F1: 0.8935

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: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.5916	0.22	100	0.5676	0.7157	0.8034
0.5229	0.44	200	0.4534	0.7770	0.8212
0.5055	0.65	300	0.4037	0.8137	0.8762
0.4597	0.87	400	0.3706	0.8407	0.8893
0.4	1.09	500	0.4590	0.8113	0.8566
0.3498	1.31	600	0.4196	0.8554	0.8974
0.2916	1.53	700	0.4606	0.8554	0.8933
0.3309	1.74	800	0.5162	0.8578	0.9027
0.3788	1.96	900	0.3911	0.8529	0.8980
0.2059	2.18	1000	0.5842	0.8554	0.8995
0.1595	2.4	1100	0.5701	0.8578	0.8975
0.1205	2.61	1200	0.6905	0.8407	0.8889
0.174	2.83	1300	0.6783	0.8480	0.8935

Framework versions

Transformers 4.15.0
Pytorch 1.10.1
Datasets 1.17.0
Tokenizers 0.10.3