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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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