Update README.md

README.md

@@ -113,8 +113,8 @@ languages:
 - zh
 - zh-Latn
 - zu
-licenses:
-- odc-by-1.0
+license:
+- odc-by
 multilinguality:
 - multilingual
 size_categories:
@@ -362,7 +362,27 @@ BERTIN Project is releasing this dataset under the same terms AllenAI released m
 
 ### Citation Information
 
+To cite this dataset:
 ```bibtex
+@article{BERTIN,
+	author = {Javier De la Rosa y Eduardo G. Ponferrada y Manu Romero y Paulo Villegas y Pablo González de Prado Salas y María Grandury},
+	title = {{BERTIN}: Efficient Pre-Training of a Spanish Language Model using Perplexity Sampling},
+	journal = {Procesamiento del Lenguaje Natural},
+	volume = {68},
+	number = {0},
+	year = {2022},
+	keywords = {},
+	abstract = {The pre-training of large language models usually requires massive amounts of resources, both in terms of computation and data. Frequently used web sources such as Common Crawl might contain enough noise to make this pretraining sub-optimal. In this work, we experiment with different sampling methods from the Spanish version of mC4, and present a novel data-centric technique which we name perplexity sampling that enables the pre-training of language models in roughly half the amount of steps and using one fifth of the data. The resulting models are comparable to the current state-of-the-art, and even achieve better results for certain tasks. Our work is proof of the versatility of Transformers, and paves the way for small teams to train their models on a limited budget.},
+	issn = {1989-7553},
+	url = {http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6403},
+	pages = {13--23}
+}
+```
+
+If you use this dataset, we would love to hear about it! Reach out on twitter, GitHub, Discord, or shoot us an email.
+
+To cite the original `mc4` dataset:
+```
 @article{2019t5,
     author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu},
     title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},