Search Results for author:
Found 11 papers, 5 papers with code
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.
BigBIO: A Framework for Data-Centric Biomedical Natural Language Processing
Training and evaluating language models increasingly requires the construction of meta-datasets --diverse collections of curated data with clear provenance.
ANTILLES: An Open French Linguistically Enriched Part-of-Speech Corpus
Part-of-speech (POS) tagging is a classical natural language processing (NLP) task.
Ranked #1 on
Part-Of-Speech Tagging
on ANTILLES
FrenchMedMCQA: A French Multiple-Choice Question Answering Dataset for Medical domain
This paper introduces FrenchMedMCQA, the first publicly available Multiple-Choice Question Answering (MCQA) dataset in French for medical domain.
DrBenchmark: A Large Language Understanding Evaluation Benchmark for French Biomedical Domain
This limitation hampers the evaluation of the latest French biomedical models, as they are either assessed on a minimal number of tasks with non-standardized protocols or evaluated using general downstream tasks.
Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations
To close the gap, we organized the BioCreative LitCovid track to call for a community effort to tackle automated topic annotation for COVID-19 literature.
Correction automatique d’examens écrits par approche neuronale profonde et attention croisée bidirectionnelle (Deep Neural Networks and Bidirectional Cross-Attention for Automatic Answer Grading)
Nous proposons trois systèmes de classification reposant sur des caractéristiques extraites de plongements de mots contextuels issus d’un modèle BERT (CamemBERT).
DrBERT: A Robust Pre-trained Model in French for Biomedical and Clinical domains
In recent years, pre-trained language models (PLMs) achieve the best performance on a wide range of natural language processing (NLP) tasks.
A Zero-shot and Few-shot Study of Instruction-Finetuned Large Language Models Applied to Clinical and Biomedical Tasks
We evaluate four state-of-the-art instruction-tuned large language models (LLMs) -- ChatGPT, Flan-T5 UL2, Tk-Instruct, and Alpaca -- on a set of 13 real-world clinical and biomedical natural language processing (NLP) tasks in English, such as named-entity recognition (NER), question-answering (QA), relation extraction (RE), etc.
BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains
This marks the first large-scale multilingual evaluation of LLMs in the medical domain.
How Important Is Tokenization in French Medical Masked Language Models?
Subword tokenization has become the prevailing standard in the field of natural language processing (NLP) over recent years, primarily due to the widespread utilization of pre-trained language models.
