Token Classification
GLiNER2
Safetensors
extractor
information-extraction
named-entity-recognition
relation-extraction
event-extraction
text-classification
Instructions to use whr778/mmbert-base-combined with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use whr778/mmbert-base-combined with GLiNER2:
from gliner2 import GLiNER2 model = GLiNER2.from_pretrained("whr778/mmbert-base-combined") # 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
- Xet hash:
- 3d6b51ffa356965bbd491dac5d37fa23ca3939ebebe4d5aaf4a97365a863283c
- Size of remote file:
- 34.4 MB
- SHA256:
- 155c4a9cca71c00e3b5b1432d58a693e3e8beff7f8068e40ab4f0944d051a663
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.