---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: results
  results: []
---

<!-- 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. -->

# results

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6829
- Accuracy: 0.6045
- Precision: [0.7241379310338585, 0.50549450549395, 0.6101694915243895]
- Recall: [0.6829268292677374, 0.6388888888880015, 0.44444444444389575]
- Micro F1: 0.6125
- Macro F1: 0.5939
- Confusion Matrix: [[155, 100], [110, 166]]

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision                                                    | Recall                                                      | Micro F1 | Macro F1 | Confusion Matrix       |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------------------------------------------------------------:|:-----------------------------------------------------------:|:--------:|:--------:|:----------------------:|
| 0.6005        | 1.0   | 138  | 0.6847          | 0.5729   | [0.8235294117634948, 0.642857142852551, 0.5434782608683837]  | [0.6153846153839391, 0.11999999999984, 0.49999999999899997] | 0.5233   | 0.4758   | [[130, 38], [126, 90]] |
| 0.5435        | 2.0   | 276  | 0.6608          | 0.6276   | [0.790123456789148, 0.6779661016937661, 0.5531914893605251]  | [0.7032967032959304, 0.5333333333326222, 0.51999999999896]  | 0.6452   | 0.6258   | [[111, 57], [86, 130]] |
| 0.5863        | 3.0   | 414  | 0.6713          | 0.6354   | [0.8260869565205419, 0.7272727272714049, 0.5535714285704401] | [0.626373626372938, 0.5333333333326222, 0.61999999999876]   | 0.6465   | 0.6376   | [[116, 52], [88, 128]] |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1