A repository for the AINA project.
Repositori del projecte AINA.
- ✨ new ✨ Ǎguila-7B: https://huggingface.co/projecte-aina/aguila-7b
- roberta-base-ca-v2: https://huggingface.co/projecte-aina/roberta-base-ca-v2
- roberta-large-ca-v2: https://huggingface.co/projecte-aina/roberta-large-ca-v2
- longformer-base-4096-ca-v2: https://huggingface.co/projecte-aina/longformer-base-4096-ca-v2
- BERTa: https://huggingface.co/PlanTL-GOB-ES/roberta-base-ca
Ǎguila-7B Ǎguila is 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
Ǎguila-7B Ǎguila és un LLM de 7B paràmetres que s'ha entrenat amb dades en castellà, català i anglès, sumant un total de 26B tokens. Utilitza com a punt de partida el model Falcon-7b, un model d'última generació en llengua anglesa que l'Technology Innovation Institute va publicar obertament fa només uns mesos. Llegiu més aquí.
RoBERTa-base-ca-v2 and BERTa are transformer-based masked language models for the Catalan language. They are based on the RoBERTA base model and have been trained on a medium-size corpus collected from publicly available corpora and crawlers.
RoBERTa-base-ca-v2 i BERTa són models de llenguatge basat en transformers per a la llengua catalana. Es basen en el model RoBERTa-base i han estat entrenat en un corpus de mida mitjana, a partir de corpus diponibles públicament i crawlers.
longformer-base-4096-ca-v2 is 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.
longformer-base-4096-ca-v2 és la versió Longformer del model roberta-base-ca-v2 per a la llengua catalana. L'ús d'aquests models permet processar contextos més grans (fins a 4096 tokens) com a entrada sense necessitat d'estratègies d'agregació addicionals. El procés de pre-entrenament d'aquest model va començar al checkpoint roberta-base-ca-v2 i es va pre-entrenar per a MLM en documents curts i llargs en català.
See results achieved on several tasks below.
For the RoBERTa-base
from transformers import AutoModelForMaskedLM
from transformers import AutoTokenizer, FillMaskPipeline
from pprint import pprint
tokenizer_hf = AutoTokenizer.from_pretrained('BSC-TeMU/roberta-base-ca-v2')
model = AutoModelForMaskedLM.from_pretrained('BSC-TeMU/roberta-base-ca-v2')
model.eval()
pipeline = FillMaskPipeline(model, tokenizer_hf)
text = f"¡Hola <mask>!"
res_hf = pipeline(text)
pprint([r['token_str'] for r in res_hf])The training corpus has been tokenized using a byte version of Byte-Pair Encoding (BPE) used in the original RoBERTA model with a vocabulary size of 52,000 tokens.
The RoBERTa-ca-v2 pretraining consists of a masked language model training that follows the approach employed for the RoBERTa base model with the same hyperparameters as in the original work. With 16 NVIDIA V100 GPUs of 16GB DDRAM the training lasted a total of 48 hours for BERTa and a total of 96 hours for RoBERTa-ca-v2.
El corpus d'entrenament ha estat tokenitzat fent servir un (BPE) a nivell de bytes utilitzat en el model RoBERTA original, amb una vocabulari de 52.000 tokens.
El pre-entrenament de RoBERTa-ca-v2 consisteix en un entrenament de model de llenguatge per masking, seguint l'enfoc que es va utilitzar per al model RoBERTa-base, amb els mateixos hiperparàmetres que en treball original. L'entrenament es va fer amb 16 GPUs NVIDIA V100 de 16GB DDRAM i va durar 48 hores per al model BERTa i 96 per al RoBERTa-ca-v2.
- Catalan Word Embeddings in FastText: https://doi.org/10.5281/zenodo.4522040
Generated from a curated corpus of over 10GB of high-quality text. Generat a partir d'un corpus seleccionat de més de 10 GB de text d'alta qualitat.
- Catalan CBOW Word Embeddings in Floret: https://zenodo.org/record/7330331
Trained using an expansive Catalan textual corpus, comprising over 34GB of data, through the floret method. Entrenat utilitzant un corpus textual català de 34 GB de dades mitjançant el mètode floret.
The training corpora consists of several corpora gathered from web crawling and public corpora. Els corpus d'entrenament són la suma de diversos corpus obtinguts a partir de corpus publics i crawlings del web.
| Corpus | Size in GB |
|---|---|
| Catalan Crawling | 13.00 |
| Wikipedia | 1.10 |
| DOGC | 0.78 |
| Catalan Open Subtitles | 0.02 |
| Catalan Oscar | 4.00 |
| CaWaC | 3.60 |
| Cat. General Crawling | 2.50 |
| Cat. Goverment Crawling | 0.24 |
| Cat. News Agency | 0.42 |
| Padicat | 0.63 |
| RacoCatalá | 8.10 |
| Nació Digital | 0.42 |
| Vilaweb | 0.06 |
| Tweets | 0.02 |
| Corpus | Size in GB |
|---|---|
| DOGC | 0.801 |
| Cat. Open Subtitles | 0.019 |
| Cat. OSCAR | 4 |
| CaWac | 3.6 |
| Cat. Wikipedia | 0.98 |
| Cat. General Crawling | 2.6 |
| Cat. Goverment Crawling | 0.247 |
| Cat. News Agency | 0.447 |
To obtain a high-quality training corpus, each corpus has been preprocessed with a pipeline of different operations, including, among the others, sentence splitting, language detection, filtering of badly-formed sentences and deduplication of repetitive contents. During the process, we kept document boundaries. Finally, the corpora are concatenated and further global deduplication among them is applied.
The Catalan Textual Corpus can be found in the following link: https://doi.org/10.5281/zenodo.4519348.
A fi d'obtenir un corpus d'entrenament d'alta qualitat, cada corpus ha estat processat amb una pipeline d'operacions, incloent separació de frases, detecció d'idioma, filtratge de frases mal formades i deduplicació de continguts repetitius, entre d'altres. Durant el procés, hem mantingut els límits dels documents. Finalment, hem concatenat els corpus i hem aplicat una nova dedupliació.
En el següent enllaç podeu trobar el Catalan Textual Corpus: https://doi.org/10.5281/zenodo.4519348.
Fine-tuned from BERTa model:
- roberta-base-ca-cased-ner for NER: https://huggingface.co/projecte-aina/roberta-base-ca-cased-ner
- roberta-base-ca-cased-pos for POS: https://huggingface.co/projecte-aina/roberta-base-ca-cased-pos
- roberta-base-ca-cased-tc for text classification: https://huggingface.co/projecte-aina/roberta-base-ca-cased-tc
- roberta-base-ca-cased-te for textual entailment: https://huggingface.co/projecte-aina/roberta-base-ca-cased-te
- roberta-base-ca-cased-sts for semantic textual similarity (STS): https://huggingface.co/projecte-aina/roberta-base-ca-cased-sts
- roberta-base-ca-cased-sts for extractive question answering (QA): https://huggingface.co/projecte-aina/roberta-base-ca-cased-qa
For a complete list, refer to. Per obtenir una llista completa, consulteu el següent enllaç: https://huggingface.co/projecte-aina/
The fine-tuning scripts for the downstream tasks are available in the following link: https://github.com/projecte-aina/club.
They are based on the HuggingFace Transformers library.
Els scripts de fine-tuning per aquestes tasques es poden trobar en el següent enllaç: https://github.com/projecte-aina/club.
Es basen en la llibreria Transformers de HuggingFace.
-
ca_bsc_core_trf: https://huggingface.co/projecte-aina/ca_bsc_core_trf.
Spacy 3.5 version with enhanced dictionaries for better coverage, using projecte-aina/roberta-large-ca-v2 model with multitask training. Versió Spacy 3.5 amb diccionaris millorats per a una millor cobertura, utilitzant el model project-aina/roberta-large-ca-v2 amb entrenament multitasca.
-
ca_bsc_demo_trf: https://huggingface.co/projecte-aina/ca_bsc_demo_trf.
Catalan transformer (projecte-aina/roberta-large-ca-v2) pipeline by BSC. Components: transformer, morphologizer, parser, ner, attribute_ruler, lemmatizer, text classification.
Available trained pipelines for Catalan in spaCy. Pipelines per al català disponibles a spaCy: https://spacy.io/models/ca
| name | task | link |
|---|---|---|
| ancora-ca-ner | Named Entity Recognition | https://huggingface.co/datasets/projecte-aina/ancora-ca-ner |
| ancora-ca-pos | Part of Speech tagging | https://huggingface.co/datasets/universal_dependencies |
| STS-ca | Semantic Textual Similarity | https://huggingface.co/datasets/projecte-aina/sts-ca |
| TeCla | Text Classification | https://huggingface.co/datasets/projecte-aina/tecla |
| TECa | Textual Entailment | https://huggingface.co/datasets/projecte-aina/teca |
| VilaQuAD | Extractive Question Answering | https://huggingface.co/datasets/projecte-aina/vilaquad |
| ViquiQuAD | Extractive Question Answering | https://huggingface.co/datasets/projecte-aina/viquiquad |
| CatalanQA | Extractive Question Answering | https://huggingface.co/datasets/projecte-aina/catalanqa |
| xquad-ca | Extractive Question Answering | https://huggingface.co/datasets/projecte-aina/xquad-ca |
For a complete list, refer to. Per obtenir una llista completa, consulteu: https://huggingface.co/projecte-aina/
For a complete list of datasets in Zenodo, refer to. Per obtenir una llista completa dels datasets a Zenodo, consulteu: https://zenodo.org/communities/catalan-ai/
The CLUB benchmark consists of 6 tasks: Named Entity Recognition (NER), Part-of-Speech Tagging (POS), Semantic Textual Similarity (STS), Text Classification (TC), Textual Entailment (TE), and Question Answering (QA).
El benchmark CLUB consisteix en 6 tasques: reconeixement d'entitats (NER), etiquetat de categoria gramatical (POS), similitut textual semàntica (STS), classificació textual (TC), implicació textual (TE) i resposta de preguntes (QA).
| Task | NER (F1) | POS (F1) | STS (Combined) | TC (Accuracy) | TE (Accuracy) | QA (Vilaquad) (F1/EM) | QA (ViquiQuAD) (F1/EM) | QA (CatalanQA) (F1/EM) | QA (XQuAD-Ca)* (F1/EM) |
|---|---|---|---|---|---|---|---|---|---|
| RoBERTa-base-ca-v2 | 89.45 | 99.09 | 79.07 | 74.26 | 83.14 | 87.74/72.58 | 88.72/75.91 | 89.50/76.63 | 73.64/55.42 |
| BERTa | 88.94 | 99.10 | 80.19 | 73.65 | 79.26 | 85.93/70.58 | 87.12/73.11 | 89.17/77.14 | 69.20/51.47 |
| mBERT | 87.36 | 98.98 | 74.26 | 69.90 | 74.63 | 82.78/67.33 | 86.89/73.53 | 86.90/74.19 | 68.79/50.80 |
| XLM-RoBERTa | 88.07 | 99.03 | 61.61 | 70.14 | 33.30 | 86.29/71.83 | 86.88/73.11 | 88.17/75.93 | 72.55/54.16 |
*: Trained on CatalanQA, tested on XQuAD-Ca.
For more information, refer to. Per a més informació, consulteu el següent enllaç https://club.aina.bsc.es/
-
Bot: Demo of the integration of voice functionalities in Catalan. Demostració d'incorporació de funcionalitats de veu en català.
- Link to demo. Enllaç a la demo: https://bot.aina.bsc.es/#/
- Link to code. Enllaç al codi: https://github.com/projecte-aina/minibot
-
spaCy: Demo of the capabilities of natural language processing chains and spaCy models implemented within the AINA project. Demostrador de les capacitats de les cadenes de processament de llenguatge natural i models spaCy implementats dins del projecte AINA.
- Link to demo. Enllaç a la demo: https://aina.bsc.es/apps/spacy
-
ViquiQA: Demo of the Question and Answer model trained with the CatalanQA dataset using the Catalan Wikipedia. Demostrador del model de Pregunta i Resposta entrenat amb el dataset CatalanQA fent servir la Viquipèdia en català.
- Link to demo. Enllaç a la demo: https://aina.bsc.es/apps/viquiqa
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Traductor: Automatic translators between Catalan and Spanish (general and specialized administrative-legal text) and between Catalan and English (general text). Traductors automàtics entre català i castellà (text general i d'especialitat administratiu-legal) i entre català i anglès (text general).
- Link to demo. Enllaç a la demo: https://aina.bsc.es/apps/traductor
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oTranscribe+: Free and private speech recognition web app for transcribing recorded interviews. Aplicació web de reconeixement de veu gratuïta i privada per a la transcripció d'entrevistes gravades.
- Link to demo. Enllaç a la demo: https://otranscribe.bsc.es/
-
CLUB: Platform for comparative evaluation of language models for Catalan. Plataforma d'avaluació comparativa de models de llengua per al català.
- Link to demo. Enllaç a la demo: https://club.aina.bsc.es/
-
TTS: Multi-speaker speech synthesis engine demo. Demostrador del motor de síntesi de parla multi parlant.
- Link to demo. Enllaç a la demo: https://aina.bsc.es/apps/tts
@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",
}
📋 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 us at [email protected]
📋 Necessitem (1) ampliar el nostre corpus per poder fer models més grans i (2) entrenar i avaluar el model en més tasques.
Per qualsevol cosa relacionada amb aquesta feina, podeu contactar-nos a [email protected]