---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: ennedendahakotubert
  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. -->

# ennedendahakotubert

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co./bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6561
- Accuracy: 0.5476
- F1: 0.4969
- Precision: 0.5597
- Recall: 0.4468

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 2.5202        | 1.0   | 771  | 0.6304          | 0.5729   | 0.3810 | 0.6921    | 0.2628 |
| 2.52          | 2.0   | 1542 | 0.6230          | 0.5768   | 0.6437 | 0.5559    | 0.7644 |
| 2.399         | 3.0   | 2313 | 0.6289          | 0.5682   | 0.6491 | 0.5467    | 0.7988 |
| 2.4829        | 4.0   | 3084 | 0.6366          | 0.5552   | 0.6149 | 0.5422    | 0.7101 |
| 2.4135        | 5.0   | 3855 | 0.6561          | 0.5476   | 0.4969 | 0.5597    | 0.4468 |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0