Official source for spanish Language Models and resources made @ BSC-TEMU within the "Plan de las Tecnologías del Lenguaje" (Plan-TL).

Overview

Spanish Language Models 💃🏻

Corpora 📃

Corpora Number of documents Size (GB)
BNE 201,080,084 570GB

Models 🤖

Word embeddings 🔤

Word embeddings trained with FastText for 300d:

Evaluation

Dataset Metric RoBERTa-b RoBERTa-l BETO mBERT BERTIN
UD-POS F1 0.9907 0.9901 0.9900 0.9886 0.9904
Conll-NER F1 0.8851 0.8772 0.8759 0.8691 0.8627
Capitel-POS F1 0.9846 0.9851 0.9836 0.9839 0.9826
Capitel-NER F1 0.8959 0.8998 0.8771 0.8810 0.8741
STS Combined 0.8423 0.8420 0.8216 0.8249 0.7822
MLDoc Accuracy 0.9595 0.9600 0.9650 0.9560 0.9673
PAWS-X F1 0.9035 0.9000 0.8915 0.9020 0.8820
XNLI Accuracy 0.8016 WiP 0.8130 0.7876 WiP

Usage example ⚗️

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-bne')
model = AutoModelForMaskedLM.from_pretrained('BSC-TeMU/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('BSC-TeMU/roberta-large-bne')
model = AutoModelForMaskedLM.from_pretrained('BSC-TeMU/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])

Other Spanish Language Models 👩‍👧‍👦

We are developing domain-specific language models:

Cite 📣

@misc{gutierrezfandino2021spanish,
      title={Spanish Language Models}, 
      author={Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Marc Pàmies and Joan Llop-Palao and Joaquín Silveira-Ocampo and Casimiro Pio Carrino and Aitor Gonzalez-Agirre and Carme Armentano-Oller and Carlos Rodriguez-Penagos and Marta Villegas},
      year={2021},
      eprint={2107.07253},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Contact 📧

📋 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 Asier Gutiérrez-Fandiño ([email protected])

Owner
PlanTL-SANIDAD
PlanTL-SANIDAD
Recognition of 38 speech commands in russian. Based on Yandex Cup 2021 ML Challenge: ASR

Speech_38_ru_commands Recognition of 38 speech commands in russian. Based on Yandex Cup 2021 ML Challenge: ASR Программа умеет распознавать 38 ключевы

Andrey 9 May 05, 2022
Huggingface Transformers + Adapters = ❤️

adapter-transformers A friendly fork of HuggingFace's Transformers, adding Adapters to PyTorch language models adapter-transformers is an extension of

AdapterHub 1.2k Jan 09, 2023
Задания КЕГЭ по информатике 2021 на Python

КЕГЭ 2021 на Python В этом репозитории мои решения типовых заданий КЕГЭ по информатике в 2021 году, БЕСПЛАТНО! Задания Взяты с https://inf-ege.sdamgia

8 Oct 13, 2022
This repository contains the code for "Generating Datasets with Pretrained Language Models".

Datasets from Instructions (DINO 🦕 ) This repository contains the code for Generating Datasets with Pretrained Language Models. The paper introduces

Timo Schick 154 Jan 01, 2023
Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents

Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents [Project Page] [Paper] [Video] Wenlong Huang1, Pieter Abbee

Wenlong Huang 114 Dec 29, 2022
Applying "Load What You Need: Smaller Versions of Multilingual BERT" to LaBSE

smaller-LaBSE LaBSE(Language-agnostic BERT Sentence Embedding) is a very good method to get sentence embeddings across languages. But it is hard to fi

Jeong Ukjae 13 Sep 02, 2022
Pytorch NLP library based on FastAI

Quick NLP Quick NLP is a deep learning nlp library inspired by the fast.ai library It follows the same api as fastai and extends it allowing for quick

Agis pof 283 Nov 21, 2022
Learn meanings behind words is a key element in NLP. This project concentrates on the disambiguation of preposition senses. Therefore, we train a bert-transformer model and surpass the state-of-the-art.

New State-of-the-Art in Preposition Sense Disambiguation Supervisor: Prof. Dr. Alexander Mehler Alexander Henlein Institutions: Goethe University TTLa

Dirk Neuhäuser 4 Apr 06, 2022
🛸 Use pretrained transformers like BERT, XLNet and GPT-2 in spaCy

spacy-transformers: Use pretrained transformers like BERT, XLNet and GPT-2 in spaCy This package provides spaCy components and architectures to use tr

Explosion 1.2k Jan 08, 2023
Code for the paper "BERT Loses Patience: Fast and Robust Inference with Early Exit".

Patience-based Early Exit Code for the paper "BERT Loses Patience: Fast and Robust Inference with Early Exit". NEWS: We now have a better and tidier i

Kevin Canwen Xu 54 Jan 04, 2023
基于pytorch+bert的中文事件抽取

pytorch_bert_event_extraction 基于pytorch+bert的中文事件抽取,主要思想是QA(问答)。 要预先下载好chinese-roberta-wwm-ext模型,并在运行时指定模型的位置。

西西嘛呦 31 Nov 30, 2022
Simple Annotated implementation of GPT-NeoX in PyTorch

Simple Annotated implementation of GPT-NeoX in PyTorch This is a simpler implementation of GPT-NeoX in PyTorch. We have taken out several optimization

labml.ai 101 Dec 03, 2022
내부 작업용 django + vue(vuetify) boilerplate. 짠 하면 돌아감.

Pocket Galaxy 아주 간단한 개인용, 혹은 내부용 툴을 만들어야하는데 이왕이면 웹이 편하죠? 그럴때를 위해 만들어둔 django와 vue(vuetify)로 이뤄진 boilerplate 입니다. 각 폴더에 있는 설명서대로 실행을 시키면 일단 당장 뭔가가 돌아갑니

Jamie J. Seol 16 Dec 03, 2021
PeCo: Perceptual Codebook for BERT Pre-training of Vision Transformers

PeCo: Perceptual Codebook for BERT Pre-training of Vision Transformers

Microsoft 105 Jan 08, 2022
All the code I wrote for Overwatch-related projects that I still own the rights to.

overwatch_shit.zip This is (eventually) going to contain all the software I wrote during my five-year imprisonment stay playing Overwatch. I'll be add

zkxjzmswkwl 2 Dec 31, 2021
pkuseg多领域中文分词工具; The pkuseg toolkit for multi-domain Chinese word segmentation

pkuseg:一个多领域中文分词工具包 (English Version) pkuseg 是基于论文[Luo et. al, 2019]的工具包。其简单易用,支持细分领域分词,有效提升了分词准确度。 目录 主要亮点 编译和安装 各类分词工具包的性能对比 使用方式 论文引用 作者 常见问题及解答 主要

LancoPKU 6k Dec 29, 2022
Extract city and country mentions from Text like GeoText without regex, but FlashText, a Aho-Corasick implementation.

flashgeotext ⚡ 🌍 Extract and count countries and cities (+their synonyms) from text, like GeoText on steroids using FlashText, a Aho-Corasick impleme

Ben 57 Dec 16, 2022
TextAttack 🐙 is a Python framework for adversarial attacks, data augmentation, and model training in NLP

TextAttack 🐙 Generating adversarial examples for NLP models [TextAttack Documentation on ReadTheDocs] About • Setup • Usage • Design About TextAttack

QData 2.2k Jan 03, 2023
A library that integrates huggingface transformers with the world of fastai, giving fastai devs everything they need to train, evaluate, and deploy transformer specific models.

blurr A library that integrates huggingface transformers with version 2 of the fastai framework Install You can now pip install blurr via pip install

ohmeow 253 Dec 31, 2022
Trained T5 and T5-large model for creating keywords from text

text to keywords Trained T5-base and T5-large model for creating keywords from text. Supported languages: ru Pretraining Large version | Pretraining B

Danil 61 Nov 24, 2022