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library_name: transformers
language:
- en
base_model: Hartunka/bert_base_rand_5_v2
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: bert_base_rand_5_v2_qqp
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE QQP
type: glue
args: qqp
metrics:
- name: Accuracy
type: accuracy
value: 0.8173880781597823
- name: F1
type: f1
value: 0.7595192339011758
---
<!-- 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_5_v2_qqp
This model is a fine-tuned version of [Hartunka/bert_base_rand_5_v2](https://huggingface.co/Hartunka/bert_base_rand_5_v2) on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3907
- Accuracy: 0.8174
- F1: 0.7595
- Combined Score: 0.7885
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
| 0.4731 | 1.0 | 1422 | 0.4262 | 0.7942 | 0.6965 | 0.7453 |
| 0.3673 | 2.0 | 2844 | 0.3907 | 0.8174 | 0.7595 | 0.7885 |
| 0.2918 | 3.0 | 4266 | 0.4051 | 0.8279 | 0.7746 | 0.8013 |
| 0.2294 | 4.0 | 5688 | 0.4142 | 0.8363 | 0.7705 | 0.8034 |
| 0.1819 | 5.0 | 7110 | 0.4686 | 0.8400 | 0.7795 | 0.8097 |
| 0.146 | 6.0 | 8532 | 0.5030 | 0.8407 | 0.7827 | 0.8117 |
| 0.1182 | 7.0 | 9954 | 0.5409 | 0.8411 | 0.7835 | 0.8123 |
### Framework versions
- Transformers 4.50.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.21.1
|