---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: AraBERT_token_classification__AraEval24_trun_concat
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# AraBERT_token_classification__AraEval24_trun_concat

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: 1.3885
- Precision: 0.0303
- Recall: 0.0554
- F1: 0.0392
- Accuracy: 0.6335

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 268  | 0.9800          | 0.0034    | 0.0002 | 0.0005 | 0.8620   |
| 1.5757        | 2.0   | 536  | 0.9902          | 0.0222    | 0.0176 | 0.0196 | 0.8193   |
| 1.5757        | 3.0   | 804  | 1.0814          | 0.0194    | 0.0282 | 0.0230 | 0.7296   |
| 1.1715        | 4.0   | 1072 | 1.2076          | 0.0216    | 0.0386 | 0.0277 | 0.6544   |
| 1.1715        | 5.0   | 1340 | 1.2372          | 0.0313    | 0.0645 | 0.0422 | 0.6457   |
| 0.9092        | 6.0   | 1608 | 1.2709          | 0.0303    | 0.0579 | 0.0397 | 0.6474   |
| 0.9092        | 7.0   | 1876 | 1.3102          | 0.0303    | 0.0575 | 0.0397 | 0.6427   |
| 0.7262        | 8.0   | 2144 | 1.3638          | 0.0290    | 0.0537 | 0.0376 | 0.6311   |
| 0.7262        | 9.0   | 2412 | 1.4512          | 0.0290    | 0.0561 | 0.0383 | 0.6109   |
| 0.635         | 10.0  | 2680 | 1.3885          | 0.0303    | 0.0554 | 0.0392 | 0.6335   |


### Framework versions

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