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--- |
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library_name: transformers |
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tags: |
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- ner |
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- biomedicine |
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license: mit |
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base_model: |
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- microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext |
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pipeline_tag: token-classification |
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--- |
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# AIObioEnts: All-in-one biomedical entities |
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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. |
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**For full details, see the [main GitHub repository](https://github.com/sirisacademic/AIObioEnts/)** |
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## Anatomical biomedical entities |
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We have followed the original AIONER training pipeline based on the BioRED dataset along with additional BioRED-compatible datasets for set of core entities (Gene, Disease, Chemical, Species, Variant, Cell line), which we have fine-tuned using a modified version of the latest release of the [AnatEM](https://nactem.ac.uk/anatomytagger/#AnatEM) corpus, and a subset of entities that are of interest to us: *cell*, *cell component*, *tissue*, *muti-tissue structure*, and *organ*, along with the newly-introduced *cancer*. 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) |
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**F1 scores** |
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The F1 scores on the test set of this modified dataset are shown below: |
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| | **BiomedBERT-base abstract+fulltext** | |
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| -------------------------- | :-----------------------------------: | |
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| **Cell** | 87.76 | |
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| **Cell component** | 81.74 | |
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| **Tissue** | 72.26 | |
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| **Cancer** | 89.29 | |
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| **Organ** | 84.18 | |
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| **Multi-tissue structure** | 72.65 | |
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| **Overall** | 84.22 | |
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## Usage |
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The model can be directly used from HuggingFace in a NER pipeline. However, we note that: |
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- The model was trained on sentence-level data, and it works best when the input is split |
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- Each sentence to tag must be surrounded by the flag corresponding to the entity type one wishes to identify, as in: `<entity_type>sentence</entity_type>`. In the case of this fine-tuned model, the entity type should be `'ALL'`. |
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- Since additional `'O'` labels are used in the AIONER scheme, the outputs should be postprocessed before aggregating the tags |
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We provide helper functions to tag individual texts in the [main repository](https://github.com/sirisacademic/AIObioEnts/) |
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````python |
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from tagging_fn import process_one_text |
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from transformers import pipeline |
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pipe = pipeline('ner', model='SIRIS-Lab/AIObioEnts-AnatEM-pubmedbert-full', aggregation_strategy='none', device=0) |
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process_one_text(text_to_tag, pipeline=pipe, entity_type='ALL') |
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```` |
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## References |
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[[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) |
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