language:
- es
license: apache-2.0
tags:
- spanish
- text-classification
- natural-language-understanding
- roberta-base
metrics:
- f1
model-index:
- name: Controversy-Prediction
results:
- task:
name: text-classification
type: text-classification
dataset:
name: meneame_controversy
type: text-classification
config: es-ES
split: test
metrics:
- name: F1
type: f1
value: 0.8472
widget:
- >-
Esposas, hijos, nueras y familiares de altos cargos del PP y de la cúpula
universitaria llenan la URJC -- Pedro González-Trevijano, rector de la
universidad desde 2002 a 2013, ahora magistrado del Tribunal Constitucional,
y su sucesor en el cargo, Fernando Suárez han tejido una red que ha dado
cobijo laboral a más de un centenar de familiares de vicerrectores, gerentes
o catedráticos en los cuatro campus con los que cuenta la universidad
localizados en Alcorcón, Móstoles, Fuenlabrada y Vicálvaro.
Spanish RoBERTa-base finetuned for Controversy Prediction
Table of Contents
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Model description
The Controversy Prediction model is a RoBERTa-base model trained of a dataset of news from the platform Menéame annotated with controversy tags in a community-based manner.
Intended uses and limitations
The Controversy Prediction model can be used for controversy prediction in news in Spanish.
How to use
Here is how to use this model:
from transformers import pipeline
from pprint import pprint
nlp = pipeline("text-classification", model="PlanTL-GOB-ES/Controversy-Prediction")
example = "Esposas, hijos, nueras y familiares de altos cargos del PP y de la cúpula universitaria llenan la URJC -- Pedro González-Trevijano, rector de la universidad desde 2002 a 2013, ahora magistrado del Tribunal Constitucional, y su sucesor en el cargo, Fernando Suárez han tejido una red que ha dado cobijo laboral a más de un centenar de familiares de vicerrectores, gerentes o catedráticos en los cuatro campus con los que cuenta la universidad localizados en Alcorcón, Móstoles, Fuenlabrada y Vicálvaro."
output = nlp(example)
pprint(output)
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 use a dataset of news from the Menéame platform, tagged with controversy labels in a community-based manner. The training set contains 18,270 news, from which 4,950 are controversial. The development set contains 1,058 news, from which 317 are controversial.
Training procedure
The model was trained with a batch size of 4 and a learning rate of 1e-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 the weighted F1 score.
Evaluation results
We evaluated the Controversy-Prediction model on the Menéame test set obtaining a weighted F1 score of 84.72. The test set contains 1,058 news, from which 317 are controversial.
Additional information
Author
Language Technologies Unit at the Barcelona Supercomputing Center ([email protected])
Contact information
For further information, send an email to [email protected]
Copyright
Copyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022)
Licensing information
Funding
This work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL.
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 of the models (SEDIA – State Secretariat for Digitalization and Artificial Intelligence) nor the creator (BSC – Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties of these models.
Los modelos publicados en este repositorio tienen una finalidad generalista y están a disposición de terceros. Estos modelos pueden tener sesgos y/u otro tipo de distorsiones indeseables.
Cuando terceros desplieguen o proporcionen sistemas y/o servicios a otras partes usando alguno de estos modelos (o utilizando sistemas basados en estos modelos) o se conviertan en usuarios de los modelos, deben tener en cuenta que es su responsabilidad mitigar los riesgos derivados de su uso y, en todo caso, cumplir con la normativa aplicable, incluyendo la normativa en materia de uso de inteligencia artificial.
En ningún caso el propietario de los modelos (SEDIA – Secretaría de Estado de Digitalización e Inteligencia Artificial) ni el creador (BSC – Barcelona Supercomputing Center) serán responsables de los resultados derivados del uso que hagan terceros de estos modelos.