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Found 14 papers, 3 papers with code
BERTrade: Using Contextual Embeddings to Parse Old French
The successes of contextual word embeddings learned by training large-scale language models, while remarkable, have mostly occurred for languages where significant amounts of raw texts are available and where annotated data in downstream tasks have a relatively regular spelling.
Le projet FREEM : ressources, outils et enjeux pour l’étude du français d’Ancien Régime (The F RE EM project: Resources, tools and challenges for the study of Ancien Régime French)
En dépit de leur qualité certaine, les ressources et outils disponibles pour l’analyse du français d’Ancien Régime ne sont plus à même de répondre aux enjeux de la recherche en linguistique et en littérature pour cette période.
From FreEM to D’AlemBERT: a Large Corpus and a Language Model for Early Modern French
anguage models for historical states of language are becoming increasingly important to allow the optimal digitisation and analysis of old textual sources.
A Data-driven Approach to Named Entity Recognition for Early Modern French
However, instead of developing a specialised architecture to tackle the particularities of this state of language, we opt for a data-driven approach by developing a new corpus with fine-grained entity annotation, covering three centuries of literature corresponding to the early modern period; we try to annotate as much data as possible producing a corpus that is many times bigger than the most popular NER evaluation corpora for both Contemporary English and French.
Tokenizer Choice For LLM Training: Negligible or Crucial?
The recent success of LLMs has been predominantly driven by curating the training dataset composition, scaling of model architectures and dataset sizes and advancements in pretraining objectives, leaving tokenizer influence as a blind spot.
Semi-automatic staging area for high-quality structured data extraction from scientific literature
For manual operations, the interface (SuperCon2 interface) is developed to increase efficiency during manual correction by providing a smart interface and an enhanced PDF document viewer.
The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset
As language models grow ever larger, the need for large-scale high-quality text datasets has never been more pressing, especially in multilingual settings.
Perplexed by Quality: A Perplexity-based Method for Adult and Harmful Content Detection in Multilingual Heterogeneous Web Data
As demand for large corpora increases with the size of current state-of-the-art language models, using web data as the main part of the pre-training corpus for these models has become a ubiquitous practice.
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions.
Automatic extraction of materials and properties from superconductors scientific literature
Using Grobid-superconductors, we built SuperCon2, a database of 40324 materials and properties records from 37700 papers.
Ranked #1 on
NER
on SuperMat
From FreEM to D'AlemBERT: a Large Corpus and a Language Model for Early Modern French
Because these historical states are at the same time more complex to process and more scarce in the corpora available, specific efforts are necessary to train natural language processing (NLP) tools adapted to the data.
Documenting Geographically and Contextually Diverse Data Sources: The BigScience Catalogue of Language Data and Resources
In recent years, large-scale data collection efforts have prioritized the amount of data collected in order to improve the modeling capabilities of large language models.
Towards a Cleaner Document-Oriented Multilingual Crawled Corpus
The need for raw large raw corpora has dramatically increased in recent years with the introduction of transfer learning and semi-supervised learning methods to Natural Language Processing.
Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets
With the success of large-scale pre-training and multilingual modeling in Natural Language Processing (NLP), recent years have seen a proliferation of large, web-mined text datasets covering hundreds of languages.
