---
base_model: qarib/bert-base-qarib60_860k
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Qarib_arabic_keyword_extraction
  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. -->

# Qarib_arabic_keyword_extraction

This model is a fine-tuned version of [qarib/bert-base-qarib60_860k](https://huggingface.co/qarib/bert-base-qarib60_860k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4027
- Precision: 0.5369
- Recall: 0.5937
- F1: 0.5638
- Accuracy: 0.9408

## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2196        | 1.0   | 750   | 0.1674          | 0.4656    | 0.4190 | 0.4411 | 0.9327   |
| 0.1374        | 2.0   | 1500  | 0.1559          | 0.4741    | 0.5255 | 0.4985 | 0.9366   |
| 0.0976        | 3.0   | 2250  | 0.1711          | 0.4901    | 0.5650 | 0.5249 | 0.9378   |
| 0.0676        | 4.0   | 3000  | 0.1928          | 0.4884    | 0.5557 | 0.5199 | 0.9363   |
| 0.0474        | 5.0   | 3750  | 0.2109          | 0.5313    | 0.5438 | 0.5375 | 0.9402   |
| 0.0342        | 6.0   | 4500  | 0.2414          | 0.5259    | 0.5754 | 0.5495 | 0.9389   |
| 0.024         | 7.0   | 5250  | 0.2527          | 0.5076    | 0.5881 | 0.5449 | 0.9382   |
| 0.0186        | 8.0   | 6000  | 0.3029          | 0.5379    | 0.5654 | 0.5513 | 0.9400   |
| 0.0143        | 9.0   | 6750  | 0.3154          | 0.5307    | 0.5862 | 0.5571 | 0.9398   |
| 0.0108        | 10.0  | 7500  | 0.3490          | 0.5491    | 0.5810 | 0.5646 | 0.9403   |
| 0.0078        | 11.0  | 8250  | 0.3550          | 0.5475    | 0.5929 | 0.5693 | 0.9412   |
| 0.0068        | 12.0  | 9000  | 0.3681          | 0.5360    | 0.6019 | 0.5670 | 0.9406   |
| 0.0049        | 13.0  | 9750  | 0.3873          | 0.5264    | 0.6048 | 0.5629 | 0.9402   |
| 0.004         | 14.0  | 10500 | 0.3987          | 0.5380    | 0.5937 | 0.5644 | 0.9407   |
| 0.0034        | 15.0  | 11250 | 0.4027          | 0.5369    | 0.5937 | 0.5638 | 0.9408   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0