---
library_name: transformers
language:
- en
base_model: Hartunka/bert_base_km_100_v1
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: bert_base_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: 0.8175117487014593
    - name: F1
      type: f1
      value: 0.7473460721868365
---

<!-- 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_100_v1_qqp

This model is a fine-tuned version of [Hartunka/bert_base_km_100_v1](https://huggingface.co/Hartunka/bert_base_km_100_v1) on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3926
- Accuracy: 0.8175
- F1: 0.7473
- Combined Score: 0.7824

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
| 0.483         | 1.0   | 1422 | 0.4432          | 0.7904   | 0.6882 | 0.7393         |
| 0.3749        | 2.0   | 2844 | 0.3926          | 0.8175   | 0.7473 | 0.7824         |
| 0.2912        | 3.0   | 4266 | 0.3976          | 0.8258   | 0.7647 | 0.7953         |
| 0.2189        | 4.0   | 5688 | 0.4471          | 0.8304   | 0.7616 | 0.7960         |
| 0.1641        | 5.0   | 7110 | 0.4702          | 0.8310   | 0.7686 | 0.7998         |
| 0.126         | 6.0   | 8532 | 0.5648          | 0.8332   | 0.7700 | 0.8016         |
| 0.1003        | 7.0   | 9954 | 0.6060          | 0.8328   | 0.7638 | 0.7983         |


### Framework versions

- Transformers 4.50.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.21.1