---
language:
- en
base_model: Hartunka/tiny_bert_rand_100_v1
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: tiny_bert_rand_100_v1_qnli
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: GLUE QNLI
      type: glue
      args: qnli
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.61925681859784
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# tiny_bert_rand_100_v1_qnli

This model is a fine-tuned version of [Hartunka/tiny_bert_rand_100_v1](https://huggingface.co/Hartunka/tiny_bert_rand_100_v1) on the GLUE QNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6503
- Accuracy: 0.6193

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6657        | 1.0   | 410  | 0.6503          | 0.6193   |
| 0.6367        | 2.0   | 820  | 0.6539          | 0.6213   |
| 0.5927        | 3.0   | 1230 | 0.6680          | 0.6129   |
| 0.531         | 4.0   | 1640 | 0.7184          | 0.6116   |
| 0.4631        | 5.0   | 2050 | 0.8040          | 0.6059   |
| 0.3972        | 6.0   | 2460 | 0.9131          | 0.6070   |


### Framework versions

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