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README.md
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For a full description on how to utilize our end-to-end pipeline we point you towards our [GitHub](https://github.com/ieeta-pt/BioNExt) repository.
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## Model Details
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### Model Description
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- **Developed by:** IEETA
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- **Model type:** BERT Base
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Please refer to our GitHub repository for more information on our end-to-end inference pipeline: [IEETA BioNExt GitHub](https://github.com/ieeta-pt/BioNExt)
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## Training Details
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The training data utilized was the BioRED corpus, wihtin the scope of the BioCreative-VIII challenge.
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Ling Luo, Po-Ting Lai, Chih-Hsuan Wei, Cecilia N Arighi, Zhiyong Lu, BioRED: a rich biomedical relation extraction dataset, Briefings in Bioinformatics, Volume 23, Issue 5, September 2022, bbac282, https://doi.org/10.1093/bib/bbac282
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As evaluated as an end to end system, our results are as follows:
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- **Tagger**: 43.10
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For a full description on how to utilize our end-to-end pipeline we point you towards our [GitHub](https://github.com/ieeta-pt/BioNExt) repository.
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- **Developed by:** IEETA
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- **Model type:** BERT Base
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Please refer to our GitHub repository for more information on our end-to-end inference pipeline: [IEETA BioNExt GitHub](https://github.com/ieeta-pt/BioNExt)
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## Training Data
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The training data utilized was the BioRED corpus, wihtin the scope of the BioCreative-VIII challenge.
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Ling Luo, Po-Ting Lai, Chih-Hsuan Wei, Cecilia N Arighi, Zhiyong Lu, BioRED: a rich biomedical relation extraction dataset, Briefings in Bioinformatics, Volume 23, Issue 5, September 2022, bbac282, https://doi.org/10.1093/bib/bbac282
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## Results
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As evaluated as an end to end system, our results are as follows:
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- **Tagger**: 43.10
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