transformers pytorch bert token-classification generated_from_trainer base_model:CAMeL-Lab/bert-base-arabic-camelbert-ca-ner license:cc-by-nc-3.0 autotrain_compatible endpoints_compatible region:us
---
license: cc-by-nc-3.0
base_model: CAMeL-Lab/bert-base-arabic-camelbert-ca-ner
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-arabic-camelbert-ca-ner_oknashar
  results: []
---

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# bert-base-arabic-camelbert-ca-ner_oknashar

This model is a fine-tuned version of [CAMeL-Lab/bert-base-arabic-camelbert-ca-ner](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-ca-ner) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2492
- Precision: 0.7007
- Recall: 0.7475
- F1: 0.7234
- Accuracy: 0.9683

## 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: 15

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1376        | 1.0   | 1357  | 0.1260          | 0.5569    | 0.5220 | 0.5389 | 0.9565   |
| 0.0874        | 2.0   | 2714  | 0.1150          | 0.6264    | 0.6198 | 0.6231 | 0.9616   |
| 0.0578        | 3.0   | 4071  | 0.1162          | 0.6598    | 0.6567 | 0.6583 | 0.9663   |
| 0.0372        | 4.0   | 5428  | 0.1336          | 0.6695    | 0.6837 | 0.6765 | 0.9662   |
| 0.0257        | 5.0   | 6785  | 0.1667          | 0.6276    | 0.6989 | 0.6613 | 0.9617   |
| 0.0183        | 6.0   | 8142  | 0.1537          | 0.6876    | 0.7258 | 0.7062 | 0.9684   |
| 0.0139        | 7.0   | 9499  | 0.1813          | 0.6860    | 0.7258 | 0.7054 | 0.9672   |
| 0.0096        | 8.0   | 10856 | 0.1935          | 0.6802    | 0.7165 | 0.6979 | 0.9673   |
| 0.0076        | 9.0   | 12213 | 0.1880          | 0.7335    | 0.7270 | 0.7302 | 0.9699   |
| 0.005         | 10.0  | 13570 | 0.2266          | 0.7070    | 0.7352 | 0.7209 | 0.9683   |
| 0.0041        | 11.0  | 14927 | 0.2340          | 0.7011    | 0.7405 | 0.7202 | 0.9681   |
| 0.0032        | 12.0  | 16284 | 0.2335          | 0.7146    | 0.7364 | 0.7253 | 0.9687   |
| 0.0031        | 13.0  | 17641 | 0.2461          | 0.7060    | 0.7499 | 0.7273 | 0.9683   |
| 0.0027        | 14.0  | 18998 | 0.2421          | 0.7088    | 0.7428 | 0.7254 | 0.9686   |
| 0.0015        | 15.0  | 20355 | 0.2492          | 0.7007    | 0.7475 | 0.7234 | 0.9683   |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
