transformers tensorboard safetensors bert token-classification generated_from_trainer base_model:elnasharomar2/AraBert_arabic_keyword_extraction autotrain_compatible endpoints_compatible region:us
---
base_model: elnasharomar2/AraBert_arabic_keyword_extraction
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: AraBert_arabic_keyword_extraction
  results: []
---

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

This model is a fine-tuned version of [aubmindlab/bert-base-arabertv2](https://huggingface.co/aubmindlab/bert-base-arabertv2)n) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4622
- Precision: 0.5583
- Recall: 0.6294
- F1: 0.5917
- Accuracy: 0.9297

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2524        | 1.0   | 750   | 0.1969          | 0.3638    | 0.4022 | 0.3820 | 0.9161   |
| 0.1705        | 2.0   | 1500  | 0.1793          | 0.4424    | 0.4749 | 0.4581 | 0.9240   |
| 0.1386        | 3.0   | 2250  | 0.1834          | 0.4547    | 0.5438 | 0.4953 | 0.9240   |
| 0.1091        | 4.0   | 3000  | 0.1987          | 0.4805    | 0.5650 | 0.5193 | 0.9243   |
| 0.0892        | 5.0   | 3750  | 0.2164          | 0.4951    | 0.5661 | 0.5282 | 0.9259   |
| 0.0737        | 6.0   | 4500  | 0.2130          | 0.5101    | 0.5635 | 0.5355 | 0.9282   |
| 0.0579        | 7.0   | 5250  | 0.2301          | 0.4890    | 0.5810 | 0.5311 | 0.9266   |
| 0.0481        | 8.0   | 6000  | 0.2479          | 0.5025    | 0.6041 | 0.5486 | 0.9269   |
| 0.0411        | 9.0   | 6750  | 0.2496          | 0.5353    | 0.5739 | 0.5539 | 0.9298   |
| 0.0348        | 10.0  | 7500  | 0.2719          | 0.5150    | 0.6063 | 0.5570 | 0.9286   |
| 0.0304        | 11.0  | 8250  | 0.2881          | 0.5252    | 0.6015 | 0.5608 | 0.9283   |
| 0.0258        | 12.0  | 9000  | 0.3088          | 0.5129    | 0.6093 | 0.5569 | 0.9266   |
| 0.0231        | 13.0  | 9750  | 0.3110          | 0.5230    | 0.5922 | 0.5555 | 0.9284   |
| 0.0199        | 14.0  | 10500 | 0.3196          | 0.5243    | 0.6030 | 0.5609 | 0.9282   |
| 0.0188        | 15.0  | 11250 | 0.3194          | 0.5169    | 0.6041 | 0.5571 | 0.9279   |
| 0.0146        | 16.0  | 750   | 0.3524          | 0.5119    | 0.5993 | 0.5522 | 0.9237   |
| 0.011         | 17.0  | 1500  | 0.3849          | 0.4895    | 0.6410 | 0.5551 | 0.9214   |
| 0.0087        | 18.0  | 2250  | 0.3469          | 0.5353    | 0.6153 | 0.5725 | 0.9311   |
| 0.0113        | 19.0  | 3000  | 0.3471          | 0.5150    | 0.6212 | 0.5631 | 0.9268   |
| 0.0088        | 20.0  | 3750  | 0.3677          | 0.5493    | 0.5929 | 0.5703 | 0.9302   |
| 0.0068        | 21.0  | 4500  | 0.3867          | 0.5313    | 0.6071 | 0.5667 | 0.9270   |
| 0.0056        | 22.0  | 5250  | 0.3843          | 0.5435    | 0.6186 | 0.5786 | 0.9293   |
| 0.0049        | 23.0  | 6000  | 0.4145          | 0.5491    | 0.6272 | 0.5855 | 0.9295   |
| 0.0043        | 24.0  | 6750  | 0.4290          | 0.5396    | 0.6339 | 0.5830 | 0.9280   |
| 0.0035        | 25.0  | 7500  | 0.4532          | 0.5322    | 0.6369 | 0.5799 | 0.9274   |
| 0.0033        | 26.0  | 8250  | 0.4273          | 0.5570    | 0.6227 | 0.5880 | 0.9309   |
| 0.0032        | 27.0  | 9000  | 0.4415          | 0.5541    | 0.6317 | 0.5903 | 0.9297   |
| 0.0025        | 28.0  | 9750  | 0.4509          | 0.5518    | 0.6272 | 0.5871 | 0.9291   |
| 0.0021        | 29.0  | 10500 | 0.4652          | 0.5668    | 0.6179 | 0.5912 | 0.9308   |
| 0.0026        | 30.0  | 11250 | 0.4622          | 0.5583    | 0.6294 | 0.5917 | 0.9297   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
