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---
language:
- ar
license: other
library_name: span-marker
tags:
- span-marker
- token-classification
- ner
- named-entity-recognition
- generated_from_span_marker_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
widget:
- text: جامعة بيزا (إيطاليا).
- text: تعلم في جامعة أوكسفورد، جامعة برنستون، جامعة كولومبيا.
- text: موطنها بلاد الشام تركيا.
- text: عادل إمام - نور الشريف
- text: فوكسي و بورتشا ضد مونكي دي لوفي و نامي
pipeline_tag: token-classification
base_model: xlm-roberta-base
model-index:
- name: SpanMarker with xlm-roberta-base on wikiann
results:
- task:
type: token-classification
name: Named Entity Recognition
dataset:
name: Unknown
type: wikiann
split: eval
metrics:
- type: f1
value: 0.8965362325351544
name: F1
- type: precision
value: 0.9077510917030568
name: Precision
- type: recall
value: 0.8855951007366646
name: Recall
---
# SpanMarker(Arabic) with xlm-roberta-base on wikiann
This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [wikiann](https://huggingface.co/datasets/wikiann) dataset that can be used for Named Entity Recognition. This SpanMarker model uses [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) as the underlying encoder.
## Model Details
### Model Description
- **Model Type:** SpanMarker
- **Encoder:** [xlm-roberta-base](https://huggingface.co/xlm-roberta-base)
- **Maximum Sequence Length:** 512 tokens
- **Maximum Entity Length:** 30 words
- **Training Dataset:** [wikiann](https://huggingface.co/datasets/wikiann)
- **Languages:** ar
- **License:** other
### Model Sources
- **Repository:** [SpanMarker on GitHub](https://github.com/tomaarsen/SpanMarkerNER)
- **Thesis:** [SpanMarker For Named Entity Recognition](https://raw.githubusercontent.com/tomaarsen/SpanMarkerNER/main/thesis.pdf)
### Model Labels
| Label | Examples |
|:------|:-----------------------------------------------------------------------|
| LOC | "شور بلاغ ( مقاطعة غرمي )", "دهنو ( تایباد )", "أقاليم ما وراء البحار" |
| ORG | "الحزب الاشتراكي", "نادي باسوش دي فيريرا", "دايو ( شركة )" |
| PER | "فرنسوا ميتيران،", "ديفيد نالبانديان", "حكم ( كرة قدم )" |
## Uses
### Direct Use for Inference
```python
from span_marker import SpanMarkerModel
# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("span_marker_model_id")
# Run inference
entities = model.predict("موطنها بلاد الشام تركيا.")
```
### Downstream Use
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
```python
from span_marker import SpanMarkerModel, Trainer
# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("span_marker_model_id")
# Specify a Dataset with "tokens" and "ner_tag" columns
dataset = load_dataset("conll2003") # For example CoNLL2003
# Initialize a Trainer using the pretrained model & dataset
trainer = Trainer(
model=model,
train_dataset=dataset["train"],
eval_dataset=dataset["validation"],
)
trainer.train()
trainer.save_model("span_marker_model_id-finetuned")
```
</details>
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:----------------------|:----|:-------|:----|
| Sentence length | 3 | 6.4592 | 63 |
| Entities per sentence | 1 | 1.1251 | 13 |
### Training Hyperparameters
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training Results
| Epoch | Step | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy |
|:------:|:-----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:|
| 0.1989 | 500 | 0.1735 | 0.2667 | 0.0011 | 0.0021 | 0.4103 |
| 0.3979 | 1000 | 0.0808 | 0.7283 | 0.5314 | 0.6145 | 0.7716 |
| 0.5968 | 1500 | 0.0595 | 0.7876 | 0.6872 | 0.7340 | 0.8546 |
| 0.7957 | 2000 | 0.0532 | 0.8148 | 0.7600 | 0.7865 | 0.8823 |
| 0.9946 | 2500 | 0.0478 | 0.8485 | 0.8028 | 0.8250 | 0.9085 |
| 1.1936 | 3000 | 0.0419 | 0.8586 | 0.8084 | 0.8327 | 0.9101 |
| 1.3925 | 3500 | 0.0390 | 0.8628 | 0.8367 | 0.8495 | 0.9237 |
| 1.5914 | 4000 | 0.0456 | 0.8559 | 0.8299 | 0.8427 | 0.9231 |
| 1.7903 | 4500 | 0.0375 | 0.8682 | 0.8469 | 0.8574 | 0.9282 |
| 1.9893 | 5000 | 0.0323 | 0.8821 | 0.8635 | 0.8727 | 0.9348 |
| 2.1882 | 5500 | 0.0346 | 0.8781 | 0.8632 | 0.8706 | 0.9346 |
| 2.3871 | 6000 | 0.0318 | 0.8953 | 0.8523 | 0.8733 | 0.9345 |
| 2.5860 | 6500 | 0.0311 | 0.8861 | 0.8691 | 0.8775 | 0.9373 |
| 2.7850 | 7000 | 0.0323 | 0.89 | 0.8689 | 0.8793 | 0.9383 |
| 2.9839 | 7500 | 0.0310 | 0.8892 | 0.8780 | 0.8836 | 0.9419 |
| 3.1828 | 8000 | 0.0320 | 0.8817 | 0.8762 | 0.8790 | 0.9397 |
| 3.3817 | 8500 | 0.0291 | 0.8981 | 0.8778 | 0.8878 | 0.9438 |
| 3.5807 | 9000 | 0.0336 | 0.8972 | 0.8792 | 0.8881 | 0.9450 |
| 3.7796 | 9500 | 0.0323 | 0.8927 | 0.8757 | 0.8841 | 0.9424 |
| 3.9785 | 10000 | 0.0315 | 0.9028 | 0.8748 | 0.8886 | 0.9436 |
| 4.1774 | 10500 | 0.0330 | 0.8984 | 0.8855 | 0.8919 | 0.9458 |
| 4.3764 | 11000 | 0.0315 | 0.9023 | 0.8844 | 0.8933 | 0.9469 |
| 4.5753 | 11500 | 0.0305 | 0.9029 | 0.8886 | 0.8957 | 0.9486 |
| 4.6171 | 11605 | 0.0323 | 0.9078 | 0.8856 | 0.8965 | 0.9487 |
### Framework Versions
- Python: 3.10.12
- SpanMarker: 1.4.0
- Transformers: 4.34.1
- PyTorch: 2.1.0+cu118
- Datasets: 2.14.6
- Tokenizers: 0.14.1
## Citation
If you use this model, please cite:
```
@InProceedings{iahlt2023WikiANNArabicNER,
author = "iahlt",
title = "Arabic NER on WikiANN",
year = "2023",
publisher = "",
location = "",
}
```
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