Average Model Cost: $0.0000
Number of Runs: 13,373
Models by this creator
Impression section Generator For Radiology Reports 🏥 This model is is the result of participation of SINAI team in Task 1B: Radiology Report Summarization at the BioNLP workshop held on ACL 2023. The goal of this task is to foster development of automatic radiology report summarization systems and expanding their applicability by incorporating seven different modalities and anatomies in the provided data. We propose to automate the generation of radiology impressions with "sequence-to-sequence" learning that leverages the power of publicly available pre-trained models, both general domain and biomedical domain-specific. This repository provides access to our best-performing system that resulted from fine-tuning of Sci-Five base, which is T5 model trained for extra 200k steps to optimize it in the context of biomedical literature. Results The official evaluation results prove that adaptation of a general-domain system for biomedical literature is beneficial for the subsequent fine-tuning for radiology report summarization task. The Table below summarizes the official scores obtained by this model during the official evaluation. Team standings re available here. System description paper and citation The paper with the detailed description of the system is published in the Proceedings of the 22st Workshop on Biomedical Language Processing. BibTeX citation:
Disease mention recognizer for Spanish Social Media texts 🦠💬 This resource derives from the participation of the SINAI team in Mining Social Media Content for Disease Mention (SocialDisNER) shared task. This task focused on the recognition of disease mentions in tweets written in Spanish with the aim of using Twitter as a proxy to better understand societal perception of disease. This task brought the community effort to developing named entity recognition (NER) approaches to detect all kinds of disease mentions in social media text. Our approach is based on a model pre-trained on general-domain text. In order to leverage large scale additional Silver Standard data with automatically generated labels provided by task’s organisers we designed a two-stage fine-tuning framework. Results The model contained in this repository constitutes the fundament of the NER system presented by the SINAI team on SocialDisNER. Enhanced with data pysentimiento pre-processing and rule-based submission post-processing, it obtained encouraging results during the official evaluation, which are summarised in the table below. System description paper and citation The system description paper was be published at Social Media Mining for Health Application (#SMM4H) held on COLING22 in October 2022.