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	---
	language: es
	license: cc-by-4.0
	library_name: span-marker
	tags:
	- span-marker
	- token-classification
	- ner
	- named-entity-recognition
	- generated_from_span_marker_trainer
	datasets:
	- conll2002
	metrics:
	- precision
	- recall
	- f1
	widget:
	- text: George Washington fue a Washington.
	pipeline_tag: token-classification
	base_model: xlm-roberta-large
	model-index:
	- name: SpanMarker with xlm-roberta-large on conll2002
	results:
	- task:
	type: token-classification
	name: Named Entity Recognition
	dataset:
	name: conll2002
	type: unknown
	split: eval
	metrics:
	- type: f1
	value: 0.8911398300151355
	name: F1
	- type: precision
	value: 0.8981459751232105
	name: Precision
	- type: recall
	value: 0.8842421441774492
	name: Recall
	---

	# SpanMarker with xlm-roberta-large on conll2002

	This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model that can be used for Named Entity Recognition. This SpanMarker model uses [xlm-roberta-large](https://huggingface.co/models/xlm-roberta-large) as the underlying encoder.

	## Model Details

	### Model Description

	- Model Type: SpanMarker
	- Encoder: [xlm-roberta-large](https://huggingface.co/models/xlm-roberta-large)
	- Maximum Sequence Length: 256 tokens
	- Maximum Entity Length: 8 words
	- Training Dataset: [conll2002](https://huggingface.co/datasets/conll2002)
	- Language: es
	- License: cc-by-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 \|
	\|:------\|:------------------------------------------------------------------\|
	\| LOC \| "Melbourne", "Australia", "Victoria" \|
	\| MISC \| "CrimeNet", "Ciudad", "Ley" \|
	\| ORG \| "Commonwealth", "Tribunal Supremo", "EFE" \|
	\| PER \| "Abogado General del Estado", "Daryl Williams", "Abogado General" \|

	## Uses

	### Direct Use for Inference

	```python
	from span_marker import SpanMarkerModel

	# Download from the 🤗 Hub
	model = SpanMarkerModel.from_pretrained("alvarobartt/span-marker-xlm-roberta-large-conll-2002-es")
	# Run inference
	entities = model.predict("George Washington fue a Washington.")
	```
	</details>

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:----------------------\|:----\|:--------\|:-----\|
	\| Sentence length \| 1 \| 31.8052 \| 1238 \|
	\| Entities per sentence \| 0 \| 2.2586 \| 160 \|

	### Training Hyperparameters
	- learning_rate: 1e-05
	- train_batch_size: 16
	- eval_batch_size: 8
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_ratio: 0.1
	- num_epochs: 2

	### Training Results
	\| Epoch \| Step \| Validation Loss \| Validation Precision \| Validation Recall \| Validation F1 \| Validation Accuracy \|
	\|:------:\|:----:\|:---------------:\|:--------------------:\|:-----------------:\|:-------------:\|:-------------------:\|
	\| 0.0587 \| 50 \| 0.4612 \| 0.0280 \| 0.0007 \| 0.0014 \| 0.8576 \|
	\| 0.1174 \| 100 \| 0.0512 \| 0.5 \| 0.0002 \| 0.0005 \| 0.8609 \|
	\| 0.1761 \| 150 \| 0.0254 \| 0.7622 \| 0.5494 \| 0.6386 \| 0.9278 \|
	\| 0.2347 \| 200 \| 0.0177 \| 0.7840 \| 0.7135 \| 0.7471 \| 0.9483 \|
	\| 0.2934 \| 250 \| 0.0153 \| 0.8072 \| 0.7944 \| 0.8007 \| 0.9662 \|
	\| 0.3521 \| 300 \| 0.0175 \| 0.8439 \| 0.7544 \| 0.7966 \| 0.9611 \|
	\| 0.4108 \| 350 \| 0.0103 \| 0.8828 \| 0.8108 \| 0.8452 \| 0.9687 \|
	\| 0.4695 \| 400 \| 0.0105 \| 0.8674 \| 0.8433 \| 0.8552 \| 0.9724 \|
	\| 0.5282 \| 450 \| 0.0098 \| 0.8651 \| 0.8477 \| 0.8563 \| 0.9745 \|
	\| 0.5869 \| 500 \| 0.0092 \| 0.8634 \| 0.8306 \| 0.8467 \| 0.9736 \|
	\| 0.6455 \| 550 \| 0.0106 \| 0.8556 \| 0.8581 \| 0.8568 \| 0.9758 \|
	\| 0.7042 \| 600 \| 0.0096 \| 0.8712 \| 0.8521 \| 0.8616 \| 0.9733 \|
	\| 0.7629 \| 650 \| 0.0090 \| 0.8791 \| 0.8420 \| 0.8601 \| 0.9740 \|
	\| 0.8216 \| 700 \| 0.0082 \| 0.8883 \| 0.8799 \| 0.8840 \| 0.9769 \|
	\| 0.8803 \| 750 \| 0.0081 \| 0.8877 \| 0.8604 \| 0.8739 \| 0.9763 \|
	\| 0.9390 \| 800 \| 0.0087 \| 0.8785 \| 0.8738 \| 0.8762 \| 0.9763 \|
	\| 0.9977 \| 850 \| 0.0084 \| 0.8777 \| 0.8653 \| 0.8714 \| 0.9767 \|
	\| 1.0563 \| 900 \| 0.0081 \| 0.8894 \| 0.8713 \| 0.8803 \| 0.9767 \|
	\| 1.1150 \| 950 \| 0.0078 \| 0.8944 \| 0.8708 \| 0.8825 \| 0.9768 \|
	\| 1.1737 \| 1000 \| 0.0079 \| 0.8973 \| 0.8722 \| 0.8846 \| 0.9776 \|
	\| 1.2324 \| 1050 \| 0.0080 \| 0.8792 \| 0.8780 \| 0.8786 \| 0.9783 \|
	\| 1.2911 \| 1100 \| 0.0082 \| 0.8821 \| 0.8574 \| 0.8696 \| 0.9767 \|
	\| 1.3498 \| 1150 \| 0.0075 \| 0.8928 \| 0.8697 \| 0.8811 \| 0.9774 \|
	\| 1.4085 \| 1200 \| 0.0076 \| 0.8919 \| 0.8803 \| 0.8860 \| 0.9792 \|
	\| 1.4671 \| 1250 \| 0.0078 \| 0.8846 \| 0.8695 \| 0.8770 \| 0.9781 \|
	\| 1.5258 \| 1300 \| 0.0074 \| 0.8944 \| 0.8845 \| 0.8894 \| 0.9792 \|
	\| 1.5845 \| 1350 \| 0.0076 \| 0.8922 \| 0.8856 \| 0.8889 \| 0.9796 \|
	\| 1.6432 \| 1400 \| 0.0072 \| 0.9004 \| 0.8799 \| 0.8900 \| 0.9790 \|
	\| 1.7019 \| 1450 \| 0.0076 \| 0.8944 \| 0.8889 \| 0.8916 \| 0.9800 \|
	\| 1.7606 \| 1500 \| 0.0074 \| 0.8962 \| 0.8861 \| 0.8911 \| 0.9800 \|
	\| 1.8192 \| 1550 \| 0.0072 \| 0.8988 \| 0.8886 \| 0.8937 \| 0.9809 \|
	\| 1.8779 \| 1600 \| 0.0074 \| 0.8962 \| 0.8833 \| 0.8897 \| 0.9797 \|
	\| 1.9366 \| 1650 \| 0.0071 \| 0.8976 \| 0.8849 \| 0.8912 \| 0.9799 \|
	\| 1.9953 \| 1700 \| 0.0071 \| 0.8981 \| 0.8842 \| 0.8911 \| 0.9799 \|

	### Framework Versions

	- Python: 3.10.12
	- SpanMarker: 1.3.1.dev
	- Transformers: 4.33.2
	- PyTorch: 2.0.1+cu118
	- Datasets: 2.14.5
	- Tokenizers: 0.13.3

	## 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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