GLiNER2
Safetensors
English
extractor
Text classification
Named Entity Recognition
Relation Extraction
Intent classification
Sentiment Analysis
Topic classification
Structured extraction
Json extraction
Instructions to use fastino/gliner2-base-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use fastino/gliner2-base-v1 with GLiNER2:
from gliner2 import GLiNER2 model = GLiNER2.from_pretrained("fastino/gliner2-base-v1") # 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
Congratulation on being one of the top 100 trending models of the day
#1
by ariG23498 - opened
Hey team!
I am Aritra from Hugging Face. It is great to see your model trending among the top 100 models.
I would like to point out that you could register your library as a custom library to Hugging Face, and that could open up a lot of goodies at the model card.
To know more, here is the official guide: https://huggingface.co/docs/hub/en/models-adding-libraries
Let me know if you need any help from my side.
I am closing this issue as it was resolved! π
ariG23498 changed discussion status to closed