Official source for spanish Language Models and resources made @ BSC-TEMU within the "Plan de las Tecnologías del Lenguaje" (Plan-TL).
A repository part of the MarIA project.
Corpora | Number of documents | Number of tokens | Size (GB) |
---|---|---|---|
BNE | 201,080,084 | 135,733,450,668 | 570GB |
✨ new ✨ Ǎguila-7B: https://huggingface.co/projecte-aina/aguila-7b
A 7B parameters LLM that has been trained on a mixture of Spanish, Catalan and English data, adding up to a total of 26B tokens. It uses the Falcon-7b model as a starting point, a state-of-the-art English language model that was openly released just a few months ago by the Technology Innovation Institute. Read more here
RoBERTa-base BNE: https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne
RoBERTa-large BNE: https://huggingface.co/PlanTL-GOB-ES/roberta-large-bne
Transformer-based masked language models for the Spanish language. They are based on the RoBERTa large model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the National Library of Spain (Biblioteca Nacional de España) from 2009 to 2019.
longformer-base-4096-bne-es: https://huggingface.co/PlanTL-GOB-ES/longformer-base-4096-bne-es
The Longformer version of the roberta-base-ca-v2 masked language model for the Catalan language. The use of these models allows us to process larger contexts (up to 4096 tokens) as input without the need of additional aggregation strategies. The pretraining process of this model started from the roberta-base-ca-v2 checkpoint and was pretrained for MLM on both short and long documents in Catalan.
GPT2-base BNE: https://huggingface.co/PlanTL-GOB-ES/gpt2-base-bne
GPT2-large BNE: https://huggingface.co/PlanTL-GOB-ES/gpt2-large-bne
Transformer-based model for the Spanish language. They are based on the GPT-2 model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the National Library of Spain (Biblioteca Nacional de España) from 2009 to 2019.
See results achieved on several tasks below. Vegeu els resultats obtinguts en diverses tasques més avall.
For the RoBERTa-base
from transformers import AutoModelForMaskedLM
from transformers import AutoTokenizer, FillMaskPipeline
from pprint import pprint
tokenizer_hf = AutoTokenizer.from_pretrained('PlanTL-GOB-ES/roberta-base-bne')
model = AutoModelForMaskedLM.from_pretrained('PlanTL-GOB-ES/roberta-base-bne')
model.eval()
pipeline = FillMaskPipeline(model, tokenizer_hf)
text = f"¡Hola <mask>!"
res_hf = pipeline(text)
pprint([r['token_str'] for r in res_hf])
For the RoBERTa-large
from transformers import AutoModelForMaskedLM
from transformers import AutoTokenizer, FillMaskPipeline
from pprint import pprint
tokenizer_hf = AutoTokenizer.from_pretrained('PlanTL-GOB-ES/roberta-large-bne')
model = AutoModelForMaskedLM.from_pretrained('PlanTL-GOB-ES/roberta-large-bne')
model.eval()
pipeline = FillMaskPipeline(model, tokenizer_hf)
text = f"¡Hola <mask>!"
res_hf = pipeline(text)
pprint([r['token_str'] for r in res_hf])
For a complete list, refer to https://huggingface.co/PlanTL-GOB-ES
Domain-specific language models:
For a complete list, refer to https://huggingface.co/PlanTL-GOB-ES
The EvalES benchmark consists of 10 tasks: Named Entity Recognition and Classification (CoNLL-NERC and CAPITEL-NERC), Part-of-Speech Tagging (UD-POS and CAPITEL-POS ), Text Classification (MLDoc), Paraphrase Identification (PAWS-X), Semantic Textual Similarity (STS), Question Answering (SQAC), Textual Entailment (XNLI) and Massive.
Dataset | Metric | RoBERTa-b | RoBERTa-l | BETO* | mBERT | BERTIN** | Electricidad*** |
---|---|---|---|---|---|---|---|
MLDoc | F1 | 0.9664 | 0.9702 | 0.9714🔥 | 0.9617 | 0.9668 | 0.9565 |
CoNLL-NERC | F1 | 0.8851🔥 | 0.8823 | 0.8759 | 0.8691 | 0.8835 | 0.7954 |
CAPITEL-NERC | F1 | 0.8960 | 0.9051🔥 | 0.8772 | 0.8810 | 0.8856 | 0.8035 |
PAWS-X | F1 | 0.9020 | 0.9150🔥 | 0.8930 | 0.9000 | 0.8965 | 0.9045 |
UD-POS | F1 | 0.9907🔥 | 0.9904 | 0.9900 | 0.9886 | 0.9898 | 0.9818 |
CAPITEL-POS | F1 | 0.9846 | 0.9856🔥 | 0.9836 | 0.9839 | 0.9847 | 0.9816 |
SQAC | F1 | 0.7923 | 0.8202🔥 | 0.7923 | 0.7562 | 0.7678 | 0.7383 |
STS | Combined | 0.8533🔥 | 0.8411 | 0.8159 | 0.8164 | 0.7945 | 0.8063 |
XNLI | Accuracy | 0.8016 | 0.8263🔥 | 0.8130 | 0.7876 | 0.7890 | 0.7878 |
Massive | Accuracy | 0.8605 | 0.8722 | 0.8732🔥 | 0.8504 | 0.8500 | 0.8517 |
* A model based on BERT architecture.
** A model based on RoBERTa architecture.
*** A model based on Electra architecture.
For more information, refer to https://benchmark.plantl.bsc.es/
@article{gutierrezfandino2022,
author = {Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Marc Pàmies and Joan Llop-Palao and Joaquin Silveira-Ocampo and Casimiro Pio Carrino and Carme Armentano-Oller and Carlos Rodriguez-Penagos and Aitor Gonzalez-Agirre and Marta Villegas},
title = {MarIA: Spanish Language Models},
journal = {Procesamiento del Lenguaje Natural},
volume = {68},
number = {0},
year = {2022},
issn = {1989-7553},
url = {http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6405},
pages = {39--60}
}
📋 We are interested in (1) extending our corpora to make larger models (2) train/evaluate the model in other tasks.
For questions regarding this work, contact [email protected]
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.