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--- |
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title: README |
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sdk: static |
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pinned: true |
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--- |
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Welcome to the space of ChouBERT, a French language model for plant health text mining. |
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On l'appelle ChouBERT parce qu'il est fait pour surveiller les végétaux comme le chou, il gère bien les polysémies comme "chou" ou "chouchou" et il est chou. |
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We further pre-trained CamemBERT base model on French plant health bulletins and Tweets to build ChouBERT. |
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ChouBERT-n are pre-trained for n epochs with MLM. You may use these models if you want to reproduce our experiments. |
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ChouBERT-n-plant-health-ner are fine-tuned ChouBERT-n for Named Entity Recongnition (NER) in plant health domain. The NER paper: <https://hal.science/hal-04245168/>. |
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ChouBERT-n-plant-health-tweet-classifier are fine-tuned ChouBERT-n for distinguishing tweets about Plant Health observation from other tweets. We describe how we build ChouBRET in this paper: <https://hal.archives-ouvertes.fr/hal-03621123> |
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Our work shows that ChouBERT-16 and ChouBERT-32-based classifiers are the most generalizable for recognizing unseen hazards, especially polysemous terms. |
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We also upload the CamemBERT-based classifiers as the baseline. |
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Listen to a song of ChouBERT (generated with sumo): <https://suno.com/song/acb11b86-5433-4d97-8e68-e44aefc66a99> |
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### BibTeX entries |
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```bibtex |
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@inproceedings{jiang2022choubert, |
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title={ChouBERT: Pre-training French Language Model for Crowdsensing with Tweets in Phytosanitary Context}, |
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author={Jiang, Shufan and Angarita, Rafael and Cormier, St{\'e}phane and Orensanz, Julien and Rousseaux, Francis}, |
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booktitle={International Conference on Research Challenges in Information Science}, |
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pages={653--661}, |
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year={2022}, |
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organization={Springer} |
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} |
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@inproceedings{jiang2022ner, |
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title = {{Named Entity Recognition for Monitoring Plant Health Threats in Tweets: a ChouBERT Approach}}, |
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author = {Jiang, Shufan and Angarita, Rafael and Cormier, St{\'e}phane and Rousseaux, Francis}, |
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booktitle = {{2022 6th International Conference on Universal Village (UV)}}, |
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address = {Boston, United States}, |
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publisher = {{IEEE}}, |
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year = {2022}, |
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doi = {10.1109/UV56588.2022.10185492}, |
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} |
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``` |
