metadata
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: finetuned-bert-mrpc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: mrpc
split: validation
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8676470588235294
- name: F1
type: f1
value: 0.9065743944636677
finetuned-bert-mrpc
This model is a fine-tuned version of bert-base-cased on the glue dataset. It achieves the following results on the evaluation set:
- Loss: 0.3874
- Accuracy: 0.8676
- F1: 0.9066
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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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 |
|---|---|---|---|---|---|
| 0.5751 | 1.0 | 230 | 0.3812 | 0.8284 | 0.8768 |
| 0.327 | 2.0 | 460 | 0.4207 | 0.8505 | 0.8992 |
| 0.176 | 3.0 | 690 | 0.3874 | 0.8676 | 0.9066 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.13.3