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	---
	language:
	- en
	license: cc-by-sa-4.0
	library_name: span-marker
	tags:
	- span-marker
	- token-classification
	- ner
	- named-entity-recognition
	- generated_from_span_marker_trainer
	datasets:
	- tomaarsen/ner-orgs
	metrics:
	- precision
	- recall
	- f1
	widget:
	- text: De Napoli played for FC Luzern in the second half of the 2005–06 Swiss Super
	League campaign, scoring five times in fifteen games and helping Luzern to promotion
	from the Swiss Challenge League.
	- text: The issue continued to simmer while full-communion agreements with the Presbyterian
	Church USA, Reformed Church in America, United Church of Christ, and Episcopal
	Church (United States) were debated and adopted in 1997 and 1999.
	- text: Rune Gerhardsen (born 13 June 1946) is a Norwegian politician, representing
	the Norwegian Labour Party and a former sports leader at Norwegian Skating Association
	representing from Aktiv SK.
	- text: Konstantin Vladimirovich Pushkaryov (; born February 12, 1985) is a Kazakhstani
	professional ice hockey winger who is currently playing with HK Kurbads of the
	Latvian Hockey League (LAT).
	- text: SCL claims that its methodology has been approved or endorsed by agencies
	of the Government of the United Kingdom and the Federal government of the United
	States, among others.
	pipeline_tag: token-classification
	base_model: microsoft/xtremedistil-l12-h384-uncased
	model-index:
	- name: SpanMarker with microsoft/xtremedistil-l12-h384-uncased on FewNERD, CoNLL2003,
	and OntoNotes v5
	results:
	- task:
	type: token-classification
	name: Named Entity Recognition
	dataset:
	name: FewNERD, CoNLL2003, and OntoNotes v5
	type: tomaarsen/ner-orgs
	split: test
	metrics:
	- type: f1
	value: 0.7558602090122487
	name: F1
	- type: precision
	value: 0.7620428694430598
	name: Precision
	- type: recall
	value: 0.749777064383806
	name: Recall
	---

	# SpanMarker with microsoft/xtremedistil-l12-h384-uncased on FewNERD, CoNLL2003, and OntoNotes v5

	This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [FewNERD, CoNLL2003, and OntoNotes v5](https://huggingface.co/datasets/tomaarsen/ner-orgs) dataset that can be used for Named Entity Recognition. This SpanMarker model uses [microsoft/xtremedistil-l12-h384-uncased](https://huggingface.co/microsoft/xtremedistil-l12-h384-uncased) as the underlying encoder.

	## Model Details

	### Model Description
	- Model Type: SpanMarker
	- Encoder: [microsoft/xtremedistil-l12-h384-uncased](https://huggingface.co/microsoft/xtremedistil-l12-h384-uncased)
	- Maximum Sequence Length: 256 tokens
	- Maximum Entity Length: 8 words
	- Training Dataset: [FewNERD, CoNLL2003, and OntoNotes v5](https://huggingface.co/datasets/tomaarsen/ner-orgs)
	- Language: en
	- License: cc-by-sa-4.0

	### 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 \|
	\|:------\|:---------------------------------------------\|
	\| ORG \| "Texas Chicken", "IAEA", "Church 's Chicken" \|

	## Evaluation

	### Metrics
	\| Label \| Precision \| Recall \| F1 \|
	\|:--------\|:----------\|:-------\|:-------\|
	\| all \| 0.7620 \| 0.7498 \| 0.7559 \|
	\| ORG \| 0.7620 \| 0.7498 \| 0.7559 \|

	## Uses

	### Direct Use for Inference

	```python
	from span_marker import SpanMarkerModel

	# Download from the 🤗 Hub
	model = SpanMarkerModel.from_pretrained("nbroad/span-marker-xdistil-l12-h384-orgs-v3")
	# Run inference
	entities = model.predict("SCL claims that its methodology has been approved or endorsed by agencies of the Government of the United Kingdom and the Federal government of the United States, among others.")
	```

	### 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("nbroad/span-marker-xdistil-l12-h384-orgs-v3")

	# 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("nbroad/span-marker-xdistil-l12-h384-orgs-v3-finetuned")
	```
	</details>

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:----------------------\|:----\|:--------\|:----\|
	\| Sentence length \| 1 \| 23.5706 \| 263 \|
	\| Entities per sentence \| 0 \| 0.7865 \| 39 \|

	### Training Hyperparameters
	- learning_rate: 0.0003
	- train_batch_size: 128
	- eval_batch_size: 128
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_ratio: 0.05
	- num_epochs: 3
	- mixed_precision_training: Native AMP

	### Training Results
	\| Epoch \| Step \| Validation Loss \| Validation Precision \| Validation Recall \| Validation F1 \| Validation Accuracy \|
	\|:------:\|:----:\|:---------------:\|:--------------------:\|:-----------------:\|:-------------:\|:-------------------:\|
	\| 0.5720 \| 600 \| 0.0086 \| 0.7150 \| 0.7095 \| 0.7122 \| 0.9660 \|
	\| 1.1439 \| 1200 \| 0.0074 \| 0.7556 \| 0.7253 \| 0.7401 \| 0.9682 \|
	\| 1.7159 \| 1800 \| 0.0073 \| 0.7482 \| 0.7619 \| 0.7550 \| 0.9702 \|
	\| 2.2879 \| 2400 \| 0.0072 \| 0.7761 \| 0.7573 \| 0.7666 \| 0.9713 \|
	\| 2.8599 \| 3000 \| 0.0070 \| 0.7691 \| 0.7688 \| 0.7689 \| 0.9720 \|

	### Framework Versions
	- Python: 3.10.12
	- SpanMarker: 1.5.0
	- Transformers: 4.35.2
	- PyTorch: 2.1.0a0+32f93b1
	- Datasets: 2.15.0
	- Tokenizers: 0.15.0

	## Citation

	### BibTeX
	```
	@software{Aarsen_SpanMarker,
	author = {Aarsen, Tom},
	license = {Apache-2.0},
	title = {{SpanMarker for Named Entity Recognition}},
	url = {https://github.com/tomaarsen/SpanMarkerNER}
	}
	```

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