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library_name: transformers
language:
- en
base_model: Hartunka/bert_base_rand_10_v2
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: bert_base_rand_10_v2_qqp
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE QQP
type: glue
args: qqp
metrics:
- name: Accuracy
type: accuracy
value: 0.8202572347266881
- name: F1
type: f1
value: 0.7589318294907945
---
<!-- 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_rand_10_v2_qqp
This model is a fine-tuned version of [Hartunka/bert_base_rand_10_v2](https://huggingface.co/Hartunka/bert_base_rand_10_v2) on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3871
- Accuracy: 0.8203
- F1: 0.7589
- Combined Score: 0.7896
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
| 0.4757 | 1.0 | 1422 | 0.4355 | 0.7913 | 0.6756 | 0.7335 |
| 0.3701 | 2.0 | 2844 | 0.3871 | 0.8203 | 0.7589 | 0.7896 |
| 0.294 | 3.0 | 4266 | 0.3957 | 0.8242 | 0.7747 | 0.7995 |
| 0.2331 | 4.0 | 5688 | 0.4476 | 0.8343 | 0.7689 | 0.8016 |
| 0.1845 | 5.0 | 7110 | 0.4730 | 0.8396 | 0.7799 | 0.8098 |
| 0.1496 | 6.0 | 8532 | 0.4950 | 0.8421 | 0.7814 | 0.8118 |
| 0.1215 | 7.0 | 9954 | 0.6163 | 0.8422 | 0.7848 | 0.8135 |
### Framework versions
- Transformers 4.50.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.21.1
|