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---
library_name: transformers
license: apache-2.0
base_model: CAMeL-Lab/bert-base-arabic-camelbert-mix
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: camelbert-ner-arabic
results:
- task:
name: Named Entity Recognition
type: token-classification
dataset:
name: WikiAnn Arabic
type: unimelb-nlp/wikiann
metrics:
- name: Precision
type: precision
value: 0.8884
- name: Recall
type: recall
value: 0.8955
- name: F1
type: f1
value: 0.8919
- name: Accuracy
type: accuracy
value: 0.9513
datasets:
- unimelb-nlp/wikiann
language:
- ar
pipeline_tag: token-classification
---
<!-- 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. -->
# camelbert-ner-arabic
This model is a fine-tuned version of [CAMeL-Lab/bert-base-arabic-camelbert-mix](https://huggingface.co./CAMeL-Lab/bert-base-arabic-camelbert-mix) on [unimelb-nlp/wikiann](https://huggingface.co./datasets/unimelb-nlp/wikiann) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2111
- Precision: 0.8884
- Recall: 0.8955
- F1: 0.8919
- Accuracy: 0.9513
## Model description
- **Base Model:** CAMeL-Lab/bert-base-arabic-camelbert-mix
- **Task:** Named Entity Recognition (NER)
- **Language:** Arabic
- **Training Data:** WikiAnn dataset for Arabic
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1892 | 1.0 | 1250 | 0.2003 | 0.8653 | 0.8677 | 0.8665 | 0.9430 |
| 0.123 | 2.0 | 2500 | 0.1912 | 0.8802 | 0.8826 | 0.8814 | 0.9493 |
| 0.0809 | 3.0 | 3750 | 0.1942 | 0.8928 | 0.8969 | 0.8948 | 0.9539 |
## Usage
```python
from transformers import pipeline
# Load the NER pipeline
nlp = pipeline("ner", model="Tevfik-istanbullu/camelbert-ner-arabic")
# Example text
text = "ูุนู
ู ู
ุญู
ุฏ ูู ุดุฑูุฉ ุฌูุฌู ูู ุฏุจู"
results = nlp(text)
print(results)
```
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3 |