---
license: mit
datasets:
- anthonyyazdaniml/gliner-biomed-pre-training
language:
- en
- multilingual
base_model:
- urchade/gliner_multi-v2.1
pipeline_tag: token-classification
tags:
- gliner
---

# About
This is an initial test for multilingual biomedical NER model using GLiNER. The model uses multilingual GLiNER model (`urchade/gliner_multi-v2.1`) as the base model and fine-tuned with biomedical NER dataset.

## Installation
This model uses GLiNER python library. You can install the library:
```
pip install gliner
```

## Usage
To use the model, you can load the model and tokenizer using `GLiNER.from_pretrained()` method. Then, you can use the `predict_entities()` method to extract entities from the input text.

```python
from gliner import GLiNER
model = GLiNER.from_pretrained("fjoeda/gliner-biomed-multi-test", load_tokenizer=True)

text = "<your input text here>"
labels = ["entity #1", "entity#2"]

entities = model.predict_entities(text, labels, threshold=0.5)

for entity in entities:
    print(entity["text"], "=>", entity["label"])
```

### Sample on English Text
Here is an example of how to use the model to extract entities from English text:

```python
text = """
Male patient, 68 years old, admitted from July 1, 2026 to July 4, 2026
due to recurrent urination symptoms, weak flow, and pelvic pain
caused by Benign Prostatic Hyperplasia (BPH).
Transurethral resection of the prostate (TURP) was performed without complications.
Patient was prescribed continued medical therapy with tamsulosin 0.4 mg qd,
"""

labels = ["Disease", "Procedure", "Medication", "Symptom", "Anatomy", "Medical Device"]

entities = model.predict_entities(text, labels, threshold=0.5)

for entity in entities:
    print(entity["text"], "=>", entity["label"])
```

output:
```
recurrent urination symptoms => Symptom
weak flow => Symptom
pelvic pain => Symptom
Benign Prostatic Hyperplasia (BPH) => Disease
Transurethral resection of the prostate (TURP) => Procedure
tamsulosin 0.4 mg qd => Medication
```


### Sample on Indonesian Text
Since GLiNER multilingual model can recognize entities from non-English text without having explicitly trained on the language, here is an example of how to use the model to extract entities from Indonesian text:

```python
text = """
Pasien pria usia 68 tahun, dirawat dari 1 Juli 2026 hingga 4 Juli 2026
karena gejala urinasi berulang, aliran lemah, dan nyeri panggul
yang disebabkan oleh Benign Prostatic Hyperplasia (BPH).
evaluasi klinis, USG, uroflowmetry, dan cystoscopy, dilakukan TURP tanpa komplikasi
serta diberikan terapi medis lanjutan dengan tamsulosin 0.4 mg qd,
antibiotik profilaksis, analgesik, dan lanjutan pengobatan hipertensi
"""

labels = ["Disease", "Procedure", "Medication", "Symptom", "Anatomy", "Medical Device"]

entities = model.predict_entities(text, labels, threshold=0.5)

for entity in entities:
    print(entity["text"], "=>", entity["label"])
```

output:
```
urinasi berulang => Symptom
aliran lemah => Symptom
nyeri panggul => Symptom
Benign Prostatic Hyperplasia (BPH) => Disease
evaluasi klinis => Procedure
USG => Procedure
uroflowmetry => Procedure
cystoscopy => Procedure
TURP => Procedure
tamsulosin 0.4 mg qd => Medication
antibiotik profilaksis => Medication
analgesik => Medication
```