---
library_name: transformers
language:
- en
base_model: Hartunka/bert_base_rand_10_v1
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: bert_base_rand_10_v1_mnli
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: GLUE MNLI
      type: glue
      args: mnli
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.6679210740439382
---

<!-- 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_10_v1_mnli

This model is a fine-tuned version of [Hartunka/bert_base_rand_10_v1](https://huggingface.co/Hartunka/bert_base_rand_10_v1) on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7815
- Accuracy: 0.6679

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9793        | 1.0   | 1534  | 0.9059          | 0.5710   |
| 0.8721        | 2.0   | 3068  | 0.8626          | 0.6047   |
| 0.7892        | 3.0   | 4602  | 0.8121          | 0.6435   |
| 0.7112        | 4.0   | 6136  | 0.8098          | 0.6464   |
| 0.6397        | 5.0   | 7670  | 0.7899          | 0.6647   |
| 0.5684        | 6.0   | 9204  | 0.8457          | 0.6695   |
| 0.4954        | 7.0   | 10738 | 0.8743          | 0.6684   |
| 0.4255        | 8.0   | 12272 | 0.9793          | 0.6591   |
| 0.3614        | 9.0   | 13806 | 1.1505          | 0.6596   |
| 0.3036        | 10.0  | 15340 | 1.2117          | 0.6593   |


### Framework versions

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