Update README.md

README.md

@@ -5,4 +5,151 @@ language:
 base_model:
 - dmis-lab/biobert-base-cased-v1.1
 pipeline_tag: token-classification
----
+---
+[![Paper](https://img.shields.io/badge/Paper-View%20on%20bioRxiv-orange?logo=biorxiv&logoColor=white)](https://www.biorxiv.org/content/10.1101/2025.08.29.671515v1)
+[![GitHub](https://img.shields.io/badge/GitHub-omicsNLP%2FmicrobELP-blue?logo=github)](https://github.com/omicsNLP/microbELP)
+[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://github.com/omicsNLP/microbELP/blob/main/LICENSE)
+
+# 🦠 MicrobELP — Microbiome Entity Recognition
+
+MicrobELP is a deep learning model for Microbiome Entity Recognition, identifying microbial entities (bacteria, archaea, fungi) in biomedical and scientific text.
+It is part of the [microbELP](https://github.com/omicsNLP/microbELP) toolkit and has been optimised for GPU inference.
+
+This model enables automated extraction of microbiome names from unstructured text, facilitating microbiome-related text mining and literature curation.
+
+---
+
+## 🚀 Quick Start (Hugging Face)
+
+You can directly load and run the model with the Hugging Face `transformers` pipeline:
+
+```python
+from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
+
+tokenizer = AutoTokenizer.from_pretrained("omicsNLP/microbELP_NER")
+model = AutoModelForTokenClassification.from_pretrained("omicsNLP/microbELP_NER")
+
+nlp = pipeline("ner", model=model, tokenizer=tokenizer)
+
+example = "The first microbiome I learned about is called Helicobacter pylori."
+ner_results = nlp(example)
+
+print(ner_results)
+```
+
+Output:
+
+```
+[
+ {'entity': 'LABEL_0', 'score': 0.9954, 'index': 1, 'word': 'the', 'start': 0, 'end': 3},
+ ...
+ {'entity': 'LABEL_1', 'score': 0.9889, 'index': 11, 'word': 'he', 'start': 47, 'end': 49},
+ {'entity': 'LABEL_2', 'score': 0.9710, 'index': 16, 'word': 'p', 'start': 60, 'end': 61},
+ ...
+]
+```
+
+where:
+- LABEL_0 → Outside (O)
+- LABEL_1 → Begin-microbiome (B-microbiome)
+- LABEL_2 → Inside-microbiome (I-microbiome)
+
+---
+
+## 🧩 Integration with the microbELP Python Package
+
+If you prefer a high-level interface with automatic aggregation, postprocessing, and text-location mapping, you can use the `microbELP` package directly.
+
+Installation:
+```bash
+git clone https://github.com/omicsNLP/microbELP.git
+pip install ./microbELP
+```
+
+It is recommended to install in an isolated environment due to dependencies.
+
+Example Usage (GPU model)
+
+```python
+from microbELP import microbiome_DL_ner
+
+input_text = "The first microbiome I learned about is called Helicobacter pylori."
+print(microbiome_DL_ner(input_text))
+```
+
+Output:
+
+```python
+[{'Entity': 'Helicobacter pylori', 'locations': {'offset': 47, 'length': 19}}]
+```
+
+You can also process a list of texts for batch inference:
+
+```python
+input_list = [
+    "The first microbiome I learned about is called Helicobacter pylori.",
+    "Then I learned about Eubacterium rectale."
+]
+print(microbiome_DL_ner(input_list))
+```
+
+Output:
+
+```python
+[
+  [{'Entity': 'Helicobacter pylori', 'locations': {'offset': 47, 'length': 19}}],
+  [{'Entity': 'Eubacterium rectale', 'locations': {'offset': 21, 'length': 19}}]
+]
+```
+Each element in the output corresponds to one input text, containing recognised microbiome entities and their text locations.
+
+---
+
+## 📘 Model Details
+
+| Property          | Description                            |
+| ----------------- | -------------------------------------- |
+| **Task**          | Named Entity Recognition (NER)         |
+| **Domain**        | Microbiome / Biomedical Text Mining    |
+| **Entity Type**   | `microbiome`                           |
+| **Model Type**    | Transformer-based token classification |
+| **Framework**     | Hugging Face 🤗 Transformers           |
+| **Optimised for** | GPU inference                          |
+
+
+---
+
+## 📚 Citation
+
+If you find this repository useful, please consider giving a star ⭐ and citation 📝:
+
+```bibtex
+@article {Patel2025.08.29.671515,
+	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.},
+	title = {Microbial Named Entity Recognition and Normalisation for AI-assisted Literature Review and Meta-Analysis},
+	elocation-id = {2025.08.29.671515},
+	year = {2025},
+	doi = {10.1101/2025.08.29.671515},
+	publisher = {Cold Spring Harbor Laboratory},
+	URL = {https://www.biorxiv.org/content/early/2025/08/30/2025.08.29.671515},
+	eprint = {https://www.biorxiv.org/content/early/2025/08/30/2025.08.29.671515.full.pdf},
+	journal = {bioRxiv}
+}
+```
+
+---
+
+## 🔗 Resources
+
+| Property          | Description                            |
+| ----------------- | -------------------------------------- |
+| **GitHub Project**|<img src="https://img.shields.io/github/stars/omicsNLP/microbELP.svg?logo=github&label=Stars" style="vertical-align:middle;"/>|
+| **Paper**         |[![DOI:10.1101/2021.01.08.425887](http://img.shields.io/badge/DOI-10.1101/2025.08.29.671515-BE2536.svg)](https://doi.org/10.1101/2025.08.29.671515)|
+| **Data**          |[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.17305411.svg)](https://doi.org/10.5281/zenodo.17305411)|
+| **Codiet**        |[![CoDiet](https://img.shields.io/badge/used_by:_%F0%9F%8D%8E_CoDiet-5AA764)](https://www.codiet.eu)|
+
+---
+
+## ⚙️ License
+
+This model and code are released under the MIT License.