metadata

language:
  - en
tags:
  - generated_from_trainer
datasets:
  - glue
metrics:
  - accuracy
  - f1
model-index:
  - name: add_BERT_no_pretrain_mrpc
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: GLUE MRPC
          type: glue
          config: mrpc
          split: validation
          args: mrpc
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.6838235294117647
          - name: F1
            type: f1
            value: 0.8122270742358079

add_BERT_no_pretrain_mrpc

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

Loss: 0.6240
Accuracy: 0.6838
F1: 0.8122
Combined Score: 0.7480

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: 0.0005
train_batch_size: 128
eval_batch_size: 128
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
1.154	1.0	29	0.6856	0.6838	0.8122	0.7480
0.6781	2.0	58	0.6609	0.6838	0.8122	0.7480
0.6458	3.0	87	0.6348	0.6838	0.8122	0.7480
0.6395	4.0	116	19.6354	0.3186	0.0071	0.1629
1.1486	5.0	145	0.6657	0.6838	0.8122	0.7480
0.6446	6.0	174	0.6277	0.6838	0.8122	0.7480
0.644	7.0	203	0.6242	0.6838	0.8122	0.7480
0.6337	8.0	232	0.6242	0.6838	0.8122	0.7480
0.6388	9.0	261	0.6253	0.6838	0.8122	0.7480
0.634	10.0	290	0.6242	0.6838	0.8122	0.7480
0.6346	11.0	319	0.6264	0.6838	0.8122	0.7480
0.6338	12.0	348	0.6273	0.6838	0.8122	0.7480
0.6343	13.0	377	0.6262	0.6838	0.8122	0.7480
0.6339	14.0	406	0.6240	0.6838	0.8122	0.7480
0.635	15.0	435	0.6244	0.6838	0.8122	0.7480
0.6331	16.0	464	0.6240	0.6838	0.8122	0.7480
0.6328	17.0	493	0.6267	0.6838	0.8122	0.7480
0.6338	18.0	522	0.6257	0.6838	0.8122	0.7480
0.6321	19.0	551	0.6240	0.6838	0.8122	0.7480

Framework versions

Transformers 4.29.2
Pytorch 1.14.0a0+410ce96
Datasets 2.12.0
Tokenizers 0.13.3