SIRIS-Lab
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AIObioEnts-core-pubmedbert-full

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 ---
 library_name: transformers
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 ---
 library_name: transformers
+tags:
+- ner
+- biomedicine
+license: mit
+base_model:
+- microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext
+pipeline_tag: token-classification
 ---
+# AIObioEnts: All-in-one biomedical entities
+Biomedical named-entity recognition following the all-in-one NER (AIONER) scheme introduced by [Luo *et al.*](https://doi.org/10.1093/bioinformatics/btad310). This is a straightforward Hugging-Face-compatible implementation without using a decoding head for ease of integration with other pipelines.
+**For full details, see the [main GitHub repository](https://github.com/sirisacademic/AIObioEnts/)**
+## Core biomedical entities
+We have followed the original original AIONER training pipeline based on the BioRED dataset along with additional BioRED-compatible datasets:
+- Gene: GNormPlus, NLM-Gene, DrugProt
+- Disease: BC5CDR, NCBI Disease
+- Chemical: BC5CDR, NLM-Chem, DrugProt
+- Species: Species-800, Linnaeus
+- Variant: tmVar
+- Cell line: BioID
+using three pre-trained language models as a base. This model corresponds to the implementation based on [BiomedBERT-base pre-trained on both abstracts from PubMed and full-texts articles from PubMedCentral](https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext)
+**F1 scores**
+The F1 scores of the current implementation on the BioRED test set are shown below:
+|               | **BiomedBERT-base abstract+fulltext** |
+| ------------- | :-----------------------------------: |
+| **Cell line** |                 96.91                 |
+| **Chemical**  |                 92.02                 |
+| **Disease**   |                 88.64                 |
+| **Gene**      |                 94.41                 |
+| **Species**   |                 97.59                 |
+| **Variant**   |                 89.58                 |
+|  |  |  |  |
+| **Overall**   |               92.44              |
+## Usage
+The model can be directly used from HuggingFace in a NER pipeline. However, we note that:
+- The model was trained on sentence-level data, and it works best when the input is split
+- Each sentence to tag must be surrounded by the flag corresponding to the entity type one wishes to identify (any of the 6 individual entities or "ALL"), as in: `<entity_type>sentence</entity_type>`
+- Since additional `'O'` labels are used in the AIONER scheme, the outputs should be postprocessed before aggregating the tags
+We provide helper functions to tag individual texts in the [main repository](https://github.com/sirisacademic/AIObioEnts/)
+````python
+from tagging_fn import process_one_text
+from transformers import pipeline
+pipe = pipeline('ner', model='SIRIS-Lab/AIObioEnts-core-pubmedbert-full', aggregation_strategy='none', device=0)
+process_one_text(text_to_tag, pipeline=pipe, entity_type='ALL')
+````
+## References
+[[1] Ling Luo, Chih-Hsuan Wei, Po-Ting Lai, Robert Leaman, Qingyu Chen, and Zhiyong Lu. "AIONER: All-in-one scheme-based biomedical named entity recognition using deep learning." Bioinformatics, Volume 39, Issue 5, May 2023, btad310.](https://doi.org/10.1093/bioinformatics/btad310)