---
library_name: transformers
language:
- en
base_model: Hartunka/bert_base_rand_10_v2
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: bert_base_rand_10_v2_wnli
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: GLUE WNLI
      type: glue
      args: wnli
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.5352112676056338
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# bert_base_rand_10_v2_wnli

This model is a fine-tuned version of [Hartunka/bert_base_rand_10_v2](https://huggingface.co/Hartunka/bert_base_rand_10_v2) on the GLUE WNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7152
- Accuracy: 0.5352

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7358        | 1.0   | 3    | 0.7191          | 0.3662   |
| 0.6956        | 2.0   | 6    | 0.7297          | 0.3944   |
| 0.6954        | 3.0   | 9    | 0.7152          | 0.5352   |
| 0.7021        | 4.0   | 12   | 0.7388          | 0.1972   |
| 0.6918        | 5.0   | 15   | 0.7654          | 0.1972   |
| 0.6855        | 6.0   | 18   | 0.7880          | 0.2113   |
| 0.6912        | 7.0   | 21   | 0.8414          | 0.1972   |
| 0.6828        | 8.0   | 24   | 0.9093          | 0.1831   |


### Framework versions

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