---
library_name: transformers
language:
- en
base_model: Hartunka/bert_base_km_5_v1
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: bert_base_km_5_v1_sst2
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: GLUE SST2
      type: glue
      args: sst2
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.805045871559633
---

<!-- 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_5_v1_sst2

This model is a fine-tuned version of [Hartunka/bert_base_km_5_v1](https://huggingface.co/Hartunka/bert_base_km_5_v1) on the GLUE SST2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4150
- Accuracy: 0.8050

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3823        | 1.0   | 264  | 0.4150          | 0.8050   |
| 0.2144        | 2.0   | 528  | 0.4730          | 0.8211   |
| 0.1506        | 3.0   | 792  | 0.5715          | 0.8005   |
| 0.1096        | 4.0   | 1056 | 0.5841          | 0.8177   |
| 0.0829        | 5.0   | 1320 | 0.5981          | 0.8119   |
| 0.0625        | 6.0   | 1584 | 0.7033          | 0.8154   |


### Framework versions

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