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
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 model, a RoBERTa 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:
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)
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 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
Catalan BERTa-v2 (roberta-base-ca-v2) finetuned for Part-of-speech-tagging (POS) Table of Contents Table of Contents Click to expand Model description
The roberta-base-ca-v2-cased-pos is a Part-of-speech-tagging (POS) model for the Catalan language fine-tuned from the roberta-base-ca-v2 model, a RoBERTa 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-pos model can be used to Part-of-speech-tagging (POS) a 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:
from transformers import pipeline from pprint import pprint
nlp = pipeline("token-classification", model="projecte-aina/roberta-base-ca-v2-cased-pos") example = "Em dic Lluïsa i visc a Santa Maria del Camí."
pos_results = nlp(example) pprint(pos_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 Training data
We used the POS dataset in Catalan from the Universal Dependencies Treebank we refer to Ancora-ca-pos 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-pos on the Ancora-ca-ner test set against standard multilingual and monolingual baselines: Model Ancora-ca-pos (F1) roberta-base-ca-v2-cased-pos 98.96 roberta-base-ca-cased-pos 98.96 mBERT 98.83 XLM-RoBERTa 98.89
For more details, check the fine-tuning and evaluation scripts in the official GitHub repository. Additional information Author
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.
Additional information
Author
Text Mining Unit (TeMU) at the Barcelona Supercomputing Center ([email protected])
Contact information
For further information, send an email to [email protected]
Copyright
Copyright (c) 2022 Text Mining Unit at Barcelona Supercomputing Center
Licensing information
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
If you use any of these resources (datasets or models) in your work, please cite our latest paper:
@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.