---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: AraBERT_token_classification__AraEval24_fixed
  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. -->

# AraBERT_token_classification__AraEval24_fixed

This model is a fine-tuned version of [aubmindlab/bert-base-arabert](https://huggingface.co/aubmindlab/bert-base-arabert) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8758
- Precision: 0.0901
- Recall: 0.0234
- F1: 0.0371
- Accuracy: 0.8606

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.6563        | 1.0   | 2851  | 0.7705          | 0.0391    | 0.0006 | 0.0012 | 0.8632   |
| 0.5865        | 2.0   | 5702  | 0.8071          | 0.0909    | 0.0028 | 0.0055 | 0.8636   |
| 0.5382        | 3.0   | 8553  | 0.7815          | 0.0578    | 0.0012 | 0.0024 | 0.8634   |
| 0.5043        | 4.0   | 11404 | 0.7883          | 0.0798    | 0.0021 | 0.0041 | 0.8633   |
| 0.4445        | 5.0   | 14255 | 0.8188          | 0.0801    | 0.0031 | 0.0060 | 0.8637   |
| 0.4295        | 6.0   | 17106 | 0.8070          | 0.0877    | 0.0155 | 0.0263 | 0.8610   |
| 0.4096        | 7.0   | 19957 | 0.8184          | 0.0949    | 0.0135 | 0.0236 | 0.8627   |
| 0.3827        | 8.0   | 22808 | 0.8362          | 0.0818    | 0.0181 | 0.0296 | 0.8600   |
| 0.3525        | 9.0   | 25659 | 0.8458          | 0.0893    | 0.0254 | 0.0395 | 0.8599   |
| 0.3434        | 10.0  | 28510 | 0.8758          | 0.0901    | 0.0234 | 0.0371 | 0.8606   |


### Framework versions

- Transformers 4.30.2
- Pytorch 1.12.1
- Datasets 2.13.2
- Tokenizers 0.13.3