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--- |
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license: mit |
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language: |
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- en |
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base_model: |
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- dmis-lab/biobert-base-cased-v1.1 |
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pipeline_tag: token-classification |
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--- |
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[](https://www.biorxiv.org/content/10.1101/2025.08.29.671515v1) |
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[](https://github.com/omicsNLP/microbELP) |
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[](https://github.com/omicsNLP/microbELP/blob/main/LICENSE) |
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# 🦠 MicrobELP — Microbiome Entity Recognition and Normalisation |
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MicrobELP is a deep learning model for Microbiome Entity Recognition and Normalisation, identifying microbial entities (bacteria, archaea, fungi) in biomedical and scientific text. |
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It is part of the [microbELP](https://github.com/omicsNLP/microbELP) toolkit and has been optimised for CPU and GPU inference. |
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This model enables automated extraction of microbiome names from unstructured text, facilitating microbiome-related text mining and literature curation. |
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We also provide a Named Entity Normalisation model on Hugging Face: |
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[](https://huggingface.co/omicsNLP/microbELP_NEN) |
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--- |
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## 🚀 Quick Start (Hugging Face) |
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You can directly load and run the model with the Hugging Face `transformers` pipeline: |
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```python |
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from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline |
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tokenizer = AutoTokenizer.from_pretrained("omicsNLP/microbELP_NER") |
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model = AutoModelForTokenClassification.from_pretrained("omicsNLP/microbELP_NER") |
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nlp = pipeline("ner", model=model, tokenizer=tokenizer) |
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example = "The first microbiome I learned about is called Helicobacter pylori." |
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ner_results = nlp(example) |
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print(ner_results) |
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``` |
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Output: |
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``` |
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[ |
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{'entity': 'LABEL_0', 'score': 0.9954, 'index': 1, 'word': 'the', 'start': 0, 'end': 3}, |
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... |
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{'entity': 'LABEL_1', 'score': 0.9889, 'index': 11, 'word': 'he', 'start': 47, 'end': 49}, |
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{'entity': 'LABEL_2', 'score': 0.9710, 'index': 16, 'word': 'p', 'start': 60, 'end': 61}, |
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... |
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] |
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``` |
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where: |
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- LABEL_0 → Outside (O) |
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- LABEL_1 → Begin-microbiome (B-microbiome) |
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- LABEL_2 → Inside-microbiome (I-microbiome) |
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--- |
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## 🧩 Integration with the microbELP Python Package |
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If you prefer a high-level interface with automatic aggregation, postprocessing, and text-location mapping, you can use the `microbELP` package directly. |
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Installation: |
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```bash |
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git clone https://github.com/omicsNLP/microbELP.git |
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pip install ./microbELP |
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``` |
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It is recommended to install in an isolated environment due to dependencies. |
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Example Usage |
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```python |
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from microbELP import microbiome_DL_ner |
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input_text = "The first microbiome I learned about is called Helicobacter pylori." |
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print(microbiome_DL_ner(input_text)) |
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``` |
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Output: |
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```python |
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[{'Entity': 'Helicobacter pylori', 'locations': {'offset': 47, 'length': 19}}] |
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``` |
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You can also process a list of texts for batch inference: |
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```python |
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input_list = [ |
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"The first microbiome I learned about is called Helicobacter pylori.", |
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"Then I learned about Eubacterium rectale." |
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] |
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print(microbiome_DL_ner(input_list)) |
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``` |
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Output: |
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```python |
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[ |
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[{'Entity': 'Helicobacter pylori', 'locations': {'offset': 47, 'length': 19}}], |
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[{'Entity': 'Eubacterium rectale', 'locations': {'offset': 21, 'length': 19}}] |
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] |
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``` |
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Each element in the output corresponds to one input text, containing recognised microbiome entities and their text locations. |
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There is one optional parameter to this function called `cpu` <type 'bool'>, the default value is False, i.e. runs on a GPU if any are available. If you want to force the usage of the CPU, you will need to use `microbiome_DL_ner(input_list, cpu = True)`. |
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--- |
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## 📘 Model Details |
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Find below some more information about this model. |
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| Property | Description | |
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| ----------------- | -------------------------------------- | |
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| **Task** | Named Entity Recognition (NER) | |
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| **Domain** | Microbiome / Biomedical Text Mining | |
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| **Entity Type** | `microbiome` | |
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| **Model Type** | Transformer-based token classification | |
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| **Framework** | Hugging Face 🤗 Transformers | |
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| **Optimised for** | GPU inference | |
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--- |
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## 📚 Citation |
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If you find this repository useful, please consider giving a like ❤️ and a citation 📝: |
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```bibtex |
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@article {Patel2025.08.29.671515, |
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author = {Patel, Dhylan and Lain, Antoine D. and Vijayaraghavan, Avish and Mirzaei, Nazanin Faghih and Mweetwa, Monica N. and Wang, Meiqi and Beck, Tim and Posma, Joram M.}, |
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title = {Microbial Named Entity Recognition and Normalisation for AI-assisted Literature Review and Meta-Analysis}, |
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elocation-id = {2025.08.29.671515}, |
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year = {2025}, |
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doi = {10.1101/2025.08.29.671515}, |
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publisher = {Cold Spring Harbor Laboratory}, |
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URL = {https://www.biorxiv.org/content/early/2025/08/30/2025.08.29.671515}, |
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eprint = {https://www.biorxiv.org/content/early/2025/08/30/2025.08.29.671515.full.pdf}, |
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journal = {bioRxiv} |
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} |
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``` |
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--- |
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## 🔗 Resources |
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Find below some more resources associated with this model. |
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| Property | Description | |
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| ----------------- | -------------------------------------- | |
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| **GitHub Project**|<img src="https://img.shields.io/github/stars/omicsNLP/microbELP.svg?logo=github&label=Stars" style="vertical-align:middle;"/>| |
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| **Paper** |[](https://doi.org/10.1101/2025.08.29.671515)| |
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| **Data** |[](https://doi.org/10.5281/zenodo.17305411)| |
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| **Codiet** |[](https://www.codiet.eu)| |
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--- |
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## ⚙️ License |
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This model and code are released under the MIT License. |
