GLiNER2
Safetensors
Token Classification
Zero-Shot Classification
Text Classification
relation extraction
Structured extraction
Instructions to use bhaskars113/113-gliner-multi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use bhaskars113/113-gliner-multi with GLiNER2:
from gliner2 import GLiNER2 model = GLiNER2.from_pretrained("bhaskars113/113-gliner-multi") # Extract entities text = "Apple CEO Tim Cook announced iPhone 15 in Cupertino yesterday." result = extractor.extract_entities(text, ["company", "person", "product", "location"]) print(result) - Notebooks
- Google Colab
- Kaggle
| import torch | |
| import subprocess | |
| import sys | |
| class EndpointHandler: | |
| def __init__(self, path=""): | |
| # Ensure gliner is installed first | |
| try: | |
| import gliner | |
| except ImportError: | |
| subprocess.check_call([sys.executable, "-m", "pip", "install", "gliner==0.2.8"]) | |
| import gliner | |
| from gliner import GLiNER | |
| self.model = GLiNER.from_pretrained(path) | |
| self.model = self.model.to("cuda") | |
| self.model.eval() | |
| def __call__(self, data): | |
| if isinstance(data, dict) and "inputs" in data: | |
| data = data["inputs"] | |
| text = data.get("text", "") | |
| labels = data.get("labels", []) | |
| if not text or not labels: | |
| return {"error": "Please provide 'text' and 'labels'"} | |
| entities = self.model.predict_entities(text, labels) | |
| return {"entities": entities} | |