---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: AraBERT_token_classification__AraEval24_truncated_rand
  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_truncated_rand

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: 2.0021
- Precision: 0.1263
- Recall: 0.1171
- F1: 0.1215
- Accuracy: 0.5544

## 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   | 479  | 1.7309          | 0.0864    | 0.0189 | 0.0310 | 0.5847   |
| 1.6653        | 2.0   | 958  | 1.6503          | 0.0855    | 0.0393 | 0.0539 | 0.5793   |
| 1.3608        | 3.0   | 1437 | 1.6761          | 0.1075    | 0.0579 | 0.0753 | 0.5869   |
| 1.1267        | 4.0   | 1916 | 1.7633          | 0.1003    | 0.0786 | 0.0882 | 0.5442   |
| 0.9119        | 5.0   | 2395 | 1.7995          | 0.1050    | 0.0877 | 0.0956 | 0.5442   |
| 0.783         | 6.0   | 2874 | 1.8613          | 0.1151    | 0.0937 | 0.1033 | 0.5607   |
| 0.6667        | 7.0   | 3353 | 1.9148          | 0.1155    | 0.1061 | 0.1106 | 0.5472   |
| 0.5967        | 8.0   | 3832 | 1.9480          | 0.1267    | 0.1175 | 0.1219 | 0.5511   |
| 0.5397        | 9.0   | 4311 | 1.9909          | 0.1235    | 0.1126 | 0.1178 | 0.5487   |
| 0.4948        | 10.0  | 4790 | 2.0021          | 0.1263    | 0.1171 | 0.1215 | 0.5544   |


### Framework versions

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