---
library_name: transformers
language:
- en
base_model: Hartunka/bert_base_rand_100_v2
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: bert_base_rand_100_v2_qnli
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: GLUE QNLI
      type: glue
      args: qnli
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.637195680029288
---

<!-- 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_rand_100_v2_qnli

This model is a fine-tuned version of [Hartunka/bert_base_rand_100_v2](https://huggingface.co/Hartunka/bert_base_rand_100_v2) on the GLUE QNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6396
- Accuracy: 0.6372

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6628        | 1.0   | 410  | 0.6418          | 0.6247   |
| 0.6243        | 2.0   | 820  | 0.6396          | 0.6372   |
| 0.559         | 3.0   | 1230 | 0.6660          | 0.6323   |
| 0.4565        | 4.0   | 1640 | 0.7500          | 0.6456   |
| 0.3424        | 5.0   | 2050 | 0.7934          | 0.6518   |
| 0.2421        | 6.0   | 2460 | 0.9497          | 0.6560   |
| 0.1726        | 7.0   | 2870 | 1.1924          | 0.6502   |


### Framework versions

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