metadata

library_name: transformers
language:
  - en
license: apache-2.0
base_model: google/bert_uncased_L-2_H-128_A-2
tags:
  - generated_from_trainer
datasets:
  - glue
metrics:
  - accuracy
  - f1
model-index:
  - name: bert_uncased_L-2_H-128_A-2_mrpc
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: GLUE MRPC
          type: glue
          args: mrpc
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.7328431372549019
          - name: F1
            type: f1
            value: 0.8233387358184764

bert_uncased_L-2_H-128_A-2_mrpc

This model is a fine-tuned version of google/bert_uncased_L-2_H-128_A-2 on the GLUE MRPC dataset. It achieves the following results on the evaluation set:

Loss: 0.5352
Accuracy: 0.7328
F1: 0.8233
Combined Score: 0.7781

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
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Combined Score
0.6493	1.0	15	0.6227	0.6838	0.8122	0.7480
0.6257	2.0	30	0.6134	0.6838	0.8122	0.7480
0.6126	3.0	45	0.6052	0.6838	0.8122	0.7480
0.6036	4.0	60	0.5954	0.6961	0.8176	0.7569
0.5897	5.0	75	0.5879	0.6985	0.8167	0.7576
0.5781	6.0	90	0.5741	0.7034	0.8158	0.7596
0.5635	7.0	105	0.5711	0.7108	0.8201	0.7655
0.5429	8.0	120	0.5674	0.7132	0.8208	0.7670
0.5228	9.0	135	0.5685	0.7206	0.8252	0.7729
0.5057	10.0	150	0.5497	0.7304	0.8281	0.7793
0.4856	11.0	165	0.5438	0.7377	0.8293	0.7835
0.4657	12.0	180	0.5352	0.7328	0.8233	0.7781
0.4447	13.0	195	0.5435	0.7402	0.8323	0.7862
0.4175	14.0	210	0.5562	0.7402	0.8328	0.7865
0.4039	15.0	225	0.5759	0.7426	0.8357	0.7892
0.3964	16.0	240	0.5610	0.7377	0.8299	0.7838
0.3735	17.0	255	0.5587	0.7377	0.8283	0.7830

Framework versions

Transformers 4.46.3
Pytorch 2.2.1+cu118
Datasets 2.17.0
Tokenizers 0.20.3