metadata
library_name: transformers
language:
- en
base_model: Hartunka/bert_base_km_100_v2
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: bert_base_km_100_v2_mrpc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
type: glue
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.7058823529411765
- name: F1
type: f1
value: 0.8170731707317073
bert_base_km_100_v2_mrpc
This model is a fine-tuned version of Hartunka/bert_base_km_100_v2 on the GLUE MRPC dataset. It achieves the following results on the evaluation set:
- Loss: 0.6165
- Accuracy: 0.7059
- F1: 0.8171
- Combined Score: 0.7615
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 |
|---|---|---|---|---|---|---|
| 0.6278 | 1.0 | 15 | 0.6243 | 0.6814 | 0.7988 | 0.7401 |
| 0.5651 | 2.0 | 30 | 0.6165 | 0.7059 | 0.8171 | 0.7615 |
| 0.489 | 3.0 | 45 | 0.6588 | 0.6961 | 0.8075 | 0.7518 |
| 0.3881 | 4.0 | 60 | 0.7228 | 0.6814 | 0.7811 | 0.7313 |
| 0.2718 | 5.0 | 75 | 0.8792 | 0.6005 | 0.7009 | 0.6507 |
| 0.165 | 6.0 | 90 | 1.0607 | 0.625 | 0.7311 | 0.6781 |
| 0.0868 | 7.0 | 105 | 1.1697 | 0.625 | 0.7330 | 0.6790 |
Framework versions
- Transformers 4.50.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.21.1