---
library_name: transformers
language:
- en
base_model: Hartunka/bert_base_km_50_v2
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: bert_base_km_50_v2_rte
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: GLUE RTE
      type: glue
      args: rte
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.5342960288808665
---

<!-- 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_km_50_v2_rte

This model is a fine-tuned version of [Hartunka/bert_base_km_50_v2](https://huggingface.co/Hartunka/bert_base_km_50_v2) on the GLUE RTE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7080
- Accuracy: 0.5343

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7063        | 1.0   | 10   | 0.7113          | 0.4910   |
| 0.6591        | 2.0   | 20   | 0.7080          | 0.5343   |
| 0.618         | 3.0   | 30   | 0.7252          | 0.5018   |
| 0.5474        | 4.0   | 40   | 0.7644          | 0.5162   |
| 0.4529        | 5.0   | 50   | 0.8655          | 0.5271   |
| 0.3237        | 6.0   | 60   | 1.0290          | 0.4982   |
| 0.2168        | 7.0   | 70   | 1.3163          | 0.4982   |


### Framework versions

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