chizhikchi
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spanish-SM-disease-finder

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Mariia commited on Sep 12, 2022

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 # 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)](https://temu.bsc.es/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](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne). In order to leverage large scale additional [Silver Standard data](https://zenodo.org/record/6803567/preview/SocialDisNER_LargeScale_additionaldata.zip#tree_item0) with automatically generated labels provided by task’s organisers we designed a two-stage fine-tuning framework. The figure below illustrated the fine-tuning process:
-<img src="https://huggingface.co/chizhikchi/spanish-SM-disease-finder/blob/main/SocialDisNER.png" alt="Two-step fine-tuning" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
 # 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`](https://github.com/pysentimiento/pysentimiento) pre-processing and rule-based submission post-processing, it obtained encouraging results during the official evaluation, which are summarised in the table below.

 # 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)](https://temu.bsc.es/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](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne). In order to leverage large scale additional [Silver Standard data](https://zenodo.org/record/6803567/preview/SocialDisNER_LargeScale_additionaldata.zip#tree_item0) 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`](https://github.com/pysentimiento/pysentimiento) pre-processing and rule-based submission post-processing, it obtained encouraging results during the official evaluation, which are summarised in the table below.