transformers safetensors bert token-classification generated_from_trainer ar dataset:unimelb-nlp/wikiann base_model:CAMeL-Lab/bert-base-arabic-camelbert-mix base_model:finetune:CAMeL-Lab/bert-base-arabic-camelbert-mix license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: CAMeL-Lab/bert-base-arabic-camelbert-mix
datasets:
- unimelb-nlp/wikiann
language:
- ar
library_name: transformers
license: apache-2.0
metrics:
- precision
- recall
- f1
- accuracy
pipeline_tag: token-classification
tags:
- generated_from_trainer
model-index:
- name: camelbert-ner-arabic
  results:
  - task:
      type: token-classification
      name: Named Entity Recognition
    dataset:
      name: WikiAnn Arabic
      type: unimelb-nlp/wikiann
    metrics:
    - type: precision
      value: 0.8884
      name: Precision
    - type: recall
      value: 0.8955
      name: Recall
    - type: f1
      value: 0.8919
      name: F1
    - type: accuracy
      value: 0.9513
      name: Accuracy
---

<!-- 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 = "يعمل محمد في شركة جوجل في دبي"
results = nlp(text)
print(results)

```

### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3