Token Classification
GLiNER
PyTorch
English
entity recognition
named-entity-recognition
zero-shot
zero-shot-ner
zero shot
biomedical-nlp
leukemia
hematology
cancer
clinical-medicine
disease
gene
protein
treatment
Instructions to use OpenMed/OpenMed-ZeroShot-NER-BloodCancer-Multi-209M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use OpenMed/OpenMed-ZeroShot-NER-BloodCancer-Multi-209M with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("OpenMed/OpenMed-ZeroShot-NER-BloodCancer-Multi-209M") - Notebooks
- Google Colab
- Kaggle
feat: Upload fine-tuned medical NER model OpenMed-ZeroShot-NER-BloodCancer-Multi-209M
Browse files
README.md
CHANGED
|
@@ -98,7 +98,7 @@ The CLL (Chronic Lymphocytic Leukemia) corpus is a domain-specific biomedical NE
|
|
| 98 |
### Installation
|
| 99 |
|
| 100 |
```bash
|
| 101 |
-
pip install gliner
|
| 102 |
```
|
| 103 |
|
| 104 |
### Usage
|
|
@@ -111,7 +111,7 @@ from transformers import pipeline
|
|
| 111 |
model_name = "OpenMed/OpenMed-ZeroShot-NER-BloodCancer-Multi-209M"
|
| 112 |
|
| 113 |
from gliner import GLiNER
|
| 114 |
-
model = GLiNER.from_pretrained(
|
| 115 |
|
| 116 |
# Example usage with default entity types
|
| 117 |
text = "The patient presented with chronic lymphocytic leukemia symptoms."
|
|
|
|
| 98 |
### Installation
|
| 99 |
|
| 100 |
```bash
|
| 101 |
+
pip install -q "gliner[tokenizers]"
|
| 102 |
```
|
| 103 |
|
| 104 |
### Usage
|
|
|
|
| 111 |
model_name = "OpenMed/OpenMed-ZeroShot-NER-BloodCancer-Multi-209M"
|
| 112 |
|
| 113 |
from gliner import GLiNER
|
| 114 |
+
model = GLiNER.from_pretrained(model_name)
|
| 115 |
|
| 116 |
# Example usage with default entity types
|
| 117 |
text = "The patient presented with chronic lymphocytic leukemia symptoms."
|