--- language: - ca license: apache-2.0 tags: - "catalan" - "named entity recognition" - "ner" - "CaText" - "Catalan Textual Corpus" datasets: - "projecte-aina/ancora-ca-ner" metrics: - f1 model-index: - name: roberta-base-ca-v2-cased-ner results: - task: type: token-classification dataset: type: projecte-aina/ancora-ca-ner name: Ancora-ca-NER metrics: - name: F1 type: f1 value: 0.8929 widget: - text: "Em dic Lluïsa i visc a Santa Maria del Camí." - text: "L'Aina, la Berta i la Norma són molt amigues." - text: "El Martí llegeix el Cavall Fort." --- # Catalan BERTa-v2 (roberta-base-ca-v2) finetuned for Named Entity Recognition. ## Table of Contents
Click to expand - [Model description](#model-description) - [Intended uses and limitations](#intended-use) - [How to use](#how-to-use) - [Limitations and bias](#limitations-and-bias) - [Training](#training) - [Training data](#training-data) - [Training procedure](#training-procedure) - [Evaluation](#evaluation) - [Variable and metrics](#variable-and-metrics) - [Evaluation results](#evaluation-results) - [Additional information](#additional-information) - [Author](#author) - [Contact information](#contact-information) - [Copyright](#copyright) - [Licensing information](#licensing-information) - [Funding](#funding) - [Citing information](#citing-information) - [Disclaimer](#disclaimer)
## Model description The **roberta-base-ca-v2-cased-ner** is a Named Entity Recognition (NER) model for the Catalan language fine-tuned from the [roberta-base-ca-v2](https://huggingface.co/projecte-aina/roberta-base-ca-v2) model, a [RoBERTa](https://arxiv.org/abs/1907.11692) base model pre-trained on a medium-size corpus collected from publicly available corpora and crawlers (check the roberta-base-ca-v2 model card for more details). ## Intended uses and limitations **roberta-base-ca-v2-cased-ner** model can be used to recognize Named Entities in the provided text. The model is limited by its training dataset and may not generalize well for all use cases. ## How to use Here is how to use this model: ```python from transformers import pipeline from pprint import pprint nlp = pipeline("ner", model="projecte-aina/roberta-base-ca-v2-cased-ner") example = "Em dic Lluïsa i visc a Santa Maria del Camí." ner_results = nlp(example) pprint(ner_results) [{'entity_group': 'person-other', 'score': 0.90749526, 'word': ' George Smith Patton', 'start': 0, 'end': 19}, {'entity_group': 'organization-other', 'score': 0.9260813, 'word': ' Ejército de los Estados Unidos', 'start': 39, 'end': 69}, {'entity_group': 'location-other', 'score': 0.992481, 'word': ' Europa', 'start': 73, 'end': 79}, {'entity_group': 'event-attack/terrorism/militaryconflict', 'score': 0.99319077, 'word': ' Segunda Guerra Mundial', 'start': 91, '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. ## Training ### Training data We used the NER dataset in Catalan called [AnCora-Ca-NER](https://huggingface.co/datasets/projecte-aina/ancora-ca-ner) for training and evaluation. ### Training procedure The model was trained with a batch size of 16 and a learning rate of 5e-5 for 5 epochs. We then selected the best checkpoint using the downstream task metric in the corresponding development set and then evaluated it on the test set. ## Evaluation ### Variable and metrics This model was finetuned maximizing F1 score. ### Evaluation results We evaluated the _roberta-base-ca-v2-cased-ner_ on the AnCora-Ca-NER test set against standard multilingual and monolingual baselines: | Model | AnCora-Ca-NER (F1)| | ------------|:-------------| | roberta-base-ca-v2-cased-ner | 89.29 | | roberta-base-ca-cased-ner | **89.76** | | mBERT | 86.87 | | XLM-RoBERTa | 86.31 | For more details, check the fine-tuning and evaluation scripts in the official [GitHub repository](https://github.com/projecte-aina/club). ## Additional information ### Author Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es) ### Contact information For further information, send an email to aina@bsc.es ### Copyright Copyright (c) 2022 Text Mining Unit at Barcelona Supercomputing Center ### Licensing information [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0) ### Funding This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina). ### Citation Information If you use any of these resources (datasets or models) in your work, please cite our latest paper: ```bibtex @inproceedings{armengol-estape-etal-2021-multilingual, title = "Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? {A} Comprehensive Assessment for {C}atalan", author = "Armengol-Estap{\'e}, Jordi and Carrino, Casimiro Pio and Rodriguez-Penagos, Carlos and de Gibert Bonet, Ona and Armentano-Oller, Carme and Gonzalez-Agirre, Aitor and Melero, Maite and Villegas, Marta", booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021", month = aug, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.findings-acl.437", doi = "10.18653/v1/2021.findings-acl.437", pages = "4933--4946", } ``` ### Disclaimer
Click to expand 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.