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library_name: transformers
language:
- en
base_model: Hartunka/tiny_bert_km_100_v2
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: tiny_bert_km_100_v2_qqp
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE QQP
type: glue
args: qqp
metrics:
- name: Accuracy
type: accuracy
value: 0.8119218402176601
- name: F1
type: f1
value: 0.7341630541183052
---
<!-- 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. -->
# tiny_bert_km_100_v2_qqp
This model is a fine-tuned version of [Hartunka/tiny_bert_km_100_v2](https://huggingface.co/Hartunka/tiny_bert_km_100_v2) on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4115
- Accuracy: 0.8119
- F1: 0.7342
- Combined Score: 0.7730
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.5003 | 1.0 | 1422 | 0.4546 | 0.7805 | 0.6752 | 0.7279 |
| 0.413 | 2.0 | 2844 | 0.4231 | 0.8009 | 0.7183 | 0.7596 |
| 0.3574 | 3.0 | 4266 | 0.4115 | 0.8119 | 0.7342 | 0.7730 |
| 0.3115 | 4.0 | 5688 | 0.4349 | 0.8157 | 0.7338 | 0.7748 |
| 0.2734 | 5.0 | 7110 | 0.4389 | 0.8202 | 0.7519 | 0.7860 |
| 0.2411 | 6.0 | 8532 | 0.4614 | 0.8222 | 0.7591 | 0.7906 |
| 0.2126 | 7.0 | 9954 | 0.4832 | 0.8210 | 0.7576 | 0.7893 |
| 0.1876 | 8.0 | 11376 | 0.5213 | 0.8209 | 0.7587 | 0.7898 |
### Framework versions
- Transformers 4.50.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.21.1
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