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DeBERTav2 finetuned for Named Entity Recognition.

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Model description

The DEBERTA_CIEL is a Multilingual Named Entity Recognition (NER) model fine-tuned from the [deberta-v3-large] model, a DeBERTa base model pre-trained on English language data collected from publicly available corpora and crawlers.

It has been trained with CEIL, Catalan Entity Identification and Linking, a Catalan-language dataset that contains 9 main types and 52 subtypes on all kinds of short texts, with almost 59K documents.

The model is able to detect and classify entities in at least 3 languages: Spanish, Catalan and English, although no formal evaluation has been carried out yet.

Intended uses and limitations

How to use

from transformers import pipeline

pipe = pipeline("ner", model="projecte-aina/DEBERTA_CIEL")
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")

[{'entity_group': 'person-other', 'score': 0.55470794, 'word': 'George Smith Patton', 'start': 0, 'end': 19}, {'entity_group': 'organization-other', 'score': 0.8435558, 'word': 'Ejército de los Estados Unidos', 'start': 38, 'end': 69}, {'entity_group': 'location-other', 'score': 0.8351533, 'word': 'Europa', 'start': 72, 'end': 79}, {'entity_group': 'event-attack/terrorism/militaryconflict', 'score': 0.8960169, 'word': 'Segunda Guerra Mundial', 'start': 90, 'end': 113}]

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.


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


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

Major types

Type num. Instances in dev set
CW * 4551
GPE 19751
Other 2824
building 2188
event 3000
location 3408
organization 17285
person 21689
product 1038

*: Cultural Work


Type num. Instances in dev set
CW-broadcastprogram 765
CW-film 549
CW-music 1027
CW-other 555
CW-painting 205
CW-writtenart 1450
GPE 19751
Other 2824
building-airport 176
building-governmentfacility 72
building-hospital 113
building-hotel 32
building-other 1585
building-restaurant 48
building-shops 34
building-sportsfacility 127
event-attack/terrorism/militaryconflict 411
event-disaster 23
event-other 1069
event-political 444
event-protest 29
event-sportsevent 1024
location-bodiesofwater 673
location-island 140
location-mountain 515
location-other 1602
location-park 93
location-road/railway/highway/transit 385
organization-education 2097
organization-government 2939
organization-media 1963
organization-onlinebusiness 197
organization-other 4733
organization-politicalparty 2272
organization-privatecompany 1809
organization-religious 210
organization-sportsteam 1065
person-actor/director 1480
person-artist/author 5812
person-athlete 1306
person-group 699
person-influencer 17
person-other 8444
person-politician 3259
person-scholar/scientist 672
product-E-device 102
product-clothing 27
product-consumer_good 20
product-food 324
product-other 69
product-software 382
product-vehicle 114

Additional information


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 (c) 2023 Language Technologies Unit (LangTech) at Barcelona Supercomputing Center

Licensing Information

Apache License, Version 2.0


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


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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.

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