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README.md

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+# Model Card for BioNExt
+
+BioNExt, is an end-to-end Biomedical Relation Extraction and Classifcation system. The work utilized three modules, a Tagger (Named Entity Recognition), Linker (Entity Linking) and an Extractor (Relation Extraction and Classification). 
+
+This repositories contains two models: 
+
+1. Tagger: Named Entity Recognition module, which performs 6 class biomedical NER:  **Genes, Diseases, Chemicals, Variants (mutations), Species, and Cell Lines**. 
+2. Extractor: Performs Relation Extraction and classification. The classes for the relation Extraction are:  **Positive Correlation, Negative Correlation, Association, Binding, Drug Interaction, Cotreatment, Comparison, and Conversion.**
+
+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. 
+
+## Model Details
+
+### Model Description
+
+- **Developed by:** IEETA
+- **Model type:** BERT Base
+- **Language(s) (NLP):** English
+- **License:** MIT
+- **Finetuned from model:** BioLinkBERT-Large 
+
+### Model Sources
+
+- **Repository:** [IEETA BioNExt GitHub](https://github.com/ieeta-pt/BioNExt)
+- **Paper:** Towards Discovery: An End-to-End System for Uncovering Novel Biomedical Relations [Awaiting Publication]
+
+**Authors:**
+- Tiago Almeida ([ORCID: 0000-0002-4258-3350](https://orcid.org/0000-0002-4258-3350))
+- Richard A A Jonker ([ORCID: 0000-0002-3806-6940](https://orcid.org/0000-0002-3806-6940))
+- Rui Antunes ([ORCID: 0000-0003-3533-8872](https://orcid.org/0000-0003-3533-8872))
+- João R Almeida ([ORCID: 0000-0003-0729-2264](https://orcid.org/0000-0003-0729-2264))
+- Sérgio Matos ([ORCID: 0000-0003-1941-3983](https://orcid.org/0000-0003-1941-3983))
+
+
+## Uses
+
+Note we do not take any liability for the use of the model in any professional/medical domain. The model is intended for academic purposes only. 
+
+## How to Get Started with the Model
+
+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)
+
+## Training Details
+
+### Training Data
+
+The training data utilized was the BioRED corpus, wihtin the scope of the BioCreative-VIII challenge.
+
+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
+
+
+### Results
+
+As evaluated as an end to end system, our results are as follows:
+- **Tagger**: 43.10
+- **Linker**: 32.46
+- **Extractor**: 24.59
+
+## Citation
+
+**BibTeX:**
+
+[Awaiting Publication]