Text Classification
Transformers
Safetensors
GLiNER2
English
Text classification
Intent classification
Sentiment Analysis
Topic classification
Named Entity Recognition
decision-model
Instructions to use fastino/GLiNER2.5-Decide with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fastino/GLiNER2.5-Decide with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="fastino/GLiNER2.5-Decide")# pip install -U transformers accelerate # Load model directly from transformers import Gliner2ForSchemaExtraction model = Gliner2ForSchemaExtraction.from_pretrained("fastino/GLiNER2.5-Decide", device_map="auto") - GLiNER2
How to use fastino/GLiNER2.5-Decide with GLiNER2:
from gliner2 import AutoExtractor extractor = AutoExtractor.from_pretrained("fastino/GLiNER2.5-Decide") # 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
Install with the local extra; tag as text-classification (#8)
Browse files- Install with the local extra; tag as text-classification (b89c9c31cfe10de6d5b5775df8f75e28a2eeab38)
- Rebase onto main; keep only pipeline_tag: text-classification (affb6ae800f6e0c65ae93604a3b82757f863a13c)
- Merge main into PR; keep only pipeline_tag: text-classification (a9c7c187cfaecccaa82836871a00756ac30dc1d7)
Co-authored-by: Bhushan Sanjay Kinge <bkinge@users.noreply.huggingface.co>
README.md
CHANGED
|
@@ -3,7 +3,7 @@ library_name: transformers
|
|
| 3 |
license: apache-2.0
|
| 4 |
language:
|
| 5 |
- en
|
| 6 |
-
pipeline_tag:
|
| 7 |
tags:
|
| 8 |
- gliner2
|
| 9 |
- Text classification
|
|
|
|
| 3 |
license: apache-2.0
|
| 4 |
language:
|
| 5 |
- en
|
| 6 |
+
pipeline_tag: text-classification
|
| 7 |
tags:
|
| 8 |
- gliner2
|
| 9 |
- Text classification
|