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library_name: transformers
language:
- en
base_model: Hartunka/bert_base_km_5_v2
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
- accuracy
model-index:
- name: bert_base_km_5_v2_cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE COLA
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.006434621036303265
- name: Accuracy
type: accuracy
value: 0.6864812970161438
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert_base_km_5_v2_cola
This model is a fine-tuned version of [Hartunka/bert_base_km_5_v2](https://huggingface.co/Hartunka/bert_base_km_5_v2) on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6109
- Matthews Correlation: 0.0064
- Accuracy: 0.6865
## 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 | Matthews Correlation | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|:--------:|
| 0.6153 | 1.0 | 34 | 0.6196 | 0.0 | 0.6913 |
| 0.5911 | 2.0 | 68 | 0.6109 | 0.0064 | 0.6865 |
| 0.5354 | 3.0 | 102 | 0.6187 | 0.0696 | 0.6817 |
| 0.4723 | 4.0 | 136 | 0.6894 | 0.0819 | 0.6587 |
| 0.4026 | 5.0 | 170 | 0.7198 | 0.1243 | 0.6577 |
| 0.3374 | 6.0 | 204 | 0.7933 | 0.1073 | 0.6347 |
| 0.2903 | 7.0 | 238 | 0.9325 | 0.0947 | 0.6366 |
### Framework versions
- Transformers 4.50.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.21.1
|