The resulting architecture consists of 6 layers, 768 dimensional embeddings and 12 attention heads. This adds up to a total of 82M parameters, which is considerably less than the 125M of standard RoBERTa-base models. This makes the model lighter and faster than the original, at the cost of slightly lower performance.
We encourage users of this model to check out the projecte-aina/roberta-base-ca-v2 model card to learn more details about the teacher model.
Intended uses and limitations
This model is ready-to-use only for masked language modeling (MLM) to perform the Fill-Mask task. However, it is intended to be fine-tuned on non-generative downstream tasks such as Question Answering, Text Classification or Named Entity Recognition.
How to use
Usage example where the model is passed to a fill-mask pipeline to predict the masked word (<mask>) from a given text.
python
1from pprint import pprint
2from transformers import pipeline
3pipe = pipeline("fill-mask", model="projecte-aina/distilroberta-base-ca-v2")4text ="El <mask> és el meu dia preferit de la setmana."5pprint(pipe(text))
[{'score': 0.2531125545501709,
'sequence': ' El dilluns és el meu dia preferit de la setmana.',
'token': 2885,
'token_str': ' dilluns'},
{'score': 0.13626143336296082,
'sequence': ' El divendres és el meu dia preferit de la setmana.',
'token': 2539,
'token_str': ' divendres'},
{'score': 0.11026635020971298,
'sequence': ' El dijous és el meu dia preferit de la setmana.',
'token': 2868,
'token_str': ' dijous'},
{'score': 0.10040736198425293,
'sequence': ' El dissabte és el meu dia preferit de la setmana.',
'token': 2480,
'token_str': ' dissabte'},
{'score': 0.09762872755527496,
'sequence': ' El diumenge és el meu dia preferit de la setmana.',
'token': 2587,
'token_str': ' diumenge'}]
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
The training corpus consists of several corpora gathered from web crawling and public corpora, as shown in the table below:
Corpus
Size (GB)
Catalan Crawling
13.00
RacoCatalá
8.10
Catalan Oscar
4.00
CaWaC
3.60
Cat. General Crawling
2.50
Wikipedia
1.10
DOGC
0.78
Padicat
0.63
ACN
0.42
Nació Digital
0.42
Cat. Government Crawling
0.24
Vilaweb
0.06
Catalan Open Subtitles
0.02
Tweets
0.02
Training procedure
This model has been trained using a technique known as Knowledge Distillation, which is used to shrink networks to a reasonable size while minimizing the loss in performance.
It basically consists in distilling a large language model (the teacher) into a more lightweight, energy-efficient, and production-friendly model (the student).
So, in a “teacher-student learning” setup, a relatively small student model is trained to mimic the behavior of a larger teacher model. As a result, the student has lower inference time and the ability to run in commodity hardware.
There is no publication for this specific model, but you can cite the paper where the teacher model was presented:
bibtex
1@inproceedings{armengol-estape-etal-2021-multilingual,
2 title = "Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? {A} Comprehensive Assessment for {C}atalan",
3 author = "Armengol-Estap{\'e}, Jordi and
4 Carrino, Casimiro Pio and
5 Rodriguez-Penagos, Carlos and
6 de Gibert Bonet, Ona and
7 Armentano-Oller, Carme and
8 Gonzalez-Agirre, Aitor and
9 Melero, Maite and
10 Villegas, Marta",
11 booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
12 month = aug,
13 year = "2021",
14 address = "Online",
15 publisher = "Association for Computational Linguistics",
16 url = "https://aclanthology.org/2021.findings-acl.437",
17 doi = "10.18653/v1/2021.findings-acl.437",
18 pages = "4933--4946",
19}
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) be liable for any results arising from the use made by third parties of these models.
