Catalan BERTa (RoBERTa-large) finetuned for Named Entity Recognition.

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Model description
Intended uses and limitations
How to Use
Training
- Training data
- Training procedure
Evaluation
- Variable and metrics
- Evaluation results
Additional information

Model description

The multiner is a Named Entity Recognition (NER) model for the Catalan language fine-tuned from the [BERTa] model, a RoBERTa base model pre-trained on a medium-size corpus collected from publicly available corpora and crawlers (check the BERTa model card for more details).

It has been trained with a dataset that contains 9 main types and 52 subtypes on all kinds of short texts, with almost 59K documents.

Intended uses and limitations

How to use

from transformers import pipeline

pipe = pipeline("ner", model="projecte-aina/multiner_ceil")
example = "George Smith Patton fué un general del Ejército de los Estados Unidos en Europa durante la Segunda Guerra Mundial. "

ner_entity_results = pipe(example, aggregation_strategy="simple")
print(ner_entity_results)

Limitations and bias

At the time of submission, no measures have been taken to estimate the bias embedded in the model. However, we are well aware that our models may be biased since the corpora have been collected using crawling techniques on multiple web sources. We intend to conduct research in these areas in the future, and if completed, this model card will be updated.

Training

We used the NERC dataset in Catalan called Catalan Entity Identification and Linking for training and evaluation.

Evaluation

Accuracy was calculated using the development set, and reflects the non-balanced nature of the dataset.

Major types

Type	Accuracy	num. Instances in dev set
CW *	0.842	4551
GPE	0.914	19751
Other	0.69	2824
building	0.736	2188
event	0.739	3000
location	0.819	3408
organization	0.895	17285
person	0.903	21689
product	0.64	1038

*: Cultural Work

Subtypes

Type	Accuracy	num. Instances in dev set
CW-broadcastprogram	0.854	765
CW-film	0.809	549
CW-music	0.862	1027
CW-other	0.495	555
CW-painting	0.654	205
CW-writtenart	0.814	1450
GPE	0.914	19751
Other	0.69	2824
building-airport	0.733	176
building-governmentfacility	0.514	72
building-hospital	0.805	113
building-hotel	0.688	32
building-other	0.726	1585
building-religious	0.0	1
building-restaurant	0.458	48
building-shops	0.206	34
building-sportsfacility	0.74	127
event-attack/terrorism/militaryconflict	0.866	411
event-disaster	0.261	23
event-other	0.695	1069
event-political	0.527	444
event-protest	0.207	29
event-sportsevent	0.822	1024
location-bodiesofwater	0.865	673
location-island	0.457	140
location-mountain	0.781	515
location-other	0.757	1602
location-park	0.581	93
location-road/railway/highway/transit	0.805	385
organization-education	0.868	2097
organization-government	0.905	2939
organization-media	0.888	1963
organization-onlinebusiness	0.538	197
organization-other	0.788	4733
organization-politicalparty	0.956	2272
organization-privatecompany	0.849	1809
organization-religious	0.638	210
organization-sportsteam	0.946	1065
person-actor/director	0.797	1480
person-artist/author	0.853	5812
person-athlete	0.871	1306
person-group	0.485	699
person-influencer	0.0	17
person-other	0.811	8444
person-politician	0.863	3259
person-scholar/scientist	0.728	672
product-E-device	0.51	102
product-clothing	0.222	27
product-consumer_good	0.0	20
product-food	0.673	324
product-other	0.0	69
product-software	0.67	382
product-vehicle	0.825	114

Additional information

Author

Language Technologies Unit (LangTech) at the Barcelona Supercomputing Center (langtech@bsc.es)

Contact information

For further information, send an email to langtech@bsc.es

Copyright

Licensing Information

Apache License, Version 2.0

Funding

This work was funded by the Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya within the framework of Projecte AINA.

Citation information

Disclaimer

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The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions.

When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence.

In no event shall the owner and creator of the models (BSC – Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties of these models.

projecte-aina
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multiner_ceil