|
|
--- |
|
|
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) |
