---
library_name: transformers
language:
- en
base_model: Hartunka/tiny_bert_km_100_v2
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: tiny_bert_km_100_v2_wnli
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: GLUE WNLI
      type: glue
      args: wnli
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.4225352112676056
---

<!-- 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_wnli

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 WNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7123
- Accuracy: 0.4225

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7076        | 1.0   | 3    | 0.7217          | 0.3521   |
| 0.6992        | 2.0   | 6    | 0.7123          | 0.4225   |
| 0.6946        | 3.0   | 9    | 0.7192          | 0.3944   |
| 0.691         | 4.0   | 12   | 0.7306          | 0.3099   |
| 0.6924        | 5.0   | 15   | 0.7442          | 0.2958   |
| 0.6922        | 6.0   | 18   | 0.7585          | 0.2958   |
| 0.6857        | 7.0   | 21   | 0.7636          | 0.2817   |


### Framework versions

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