---
language:
- en
base_model: Hartunka/tiny_bert_km_100_v1
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: tiny_bert_km_100_v1_qqp
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: GLUE QQP
      type: glue
      args: qqp
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8003957457333664
    - name: F1
      type: f1
      value: 0.7175951847704367
---

<!-- 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_v1_qqp

This model is a fine-tuned version of [Hartunka/tiny_bert_km_100_v1](https://huggingface.co/Hartunka/tiny_bert_km_100_v1) on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4242
- Accuracy: 0.8004
- F1: 0.7176
- Combined Score: 0.7590

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Combined Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
| 0.4988        | 1.0   | 1422 | 0.4509          | 0.7821   | 0.6736 | 0.7278         |
| 0.4121        | 2.0   | 2844 | 0.4242          | 0.8004   | 0.7176 | 0.7590         |
| 0.3559        | 3.0   | 4266 | 0.4249          | 0.8094   | 0.7312 | 0.7703         |
| 0.3085        | 4.0   | 5688 | 0.4430          | 0.8128   | 0.7229 | 0.7679         |
| 0.2704        | 5.0   | 7110 | 0.4338          | 0.8163   | 0.7592 | 0.7878         |
| 0.2367        | 6.0   | 8532 | 0.4462          | 0.8190   | 0.7594 | 0.7892         |
| 0.2093        | 7.0   | 9954 | 0.4795          | 0.8209   | 0.7585 | 0.7897         |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.19.1