Token Classification
GLiNER2
Safetensors
English
extractor
Text classification
Named Entity Recognition
Relation Extraction
Intent classification
Sentiment Analysis
Topic classification
Structured extraction
Json extraction
information-extraction
boundary-extraction
Instructions to use fastino/gliner2.5-base-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use fastino/gliner2.5-base-v1 with GLiNER2:
from gliner2 import GLiNER2 model = GLiNER2.from_pretrained("fastino/gliner2.5-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
Commit ·
d97b7fb
1
Parent(s): 72ac19b
update banner (#3)
Browse files- update banner (b4ada7e38aaf0a49ca55c4e9c9d7a0ac3e1fa5df)
Co-authored-by: Thao Nguyen <[email protected]>
README.md
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<img src="GLiNER-2.5-Base-HF-Banner.png" alt="Fastino Agent - Fine-tune GLiNER with a single prompt" width="100%"/>
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