Instructions to use mircq/GLINER-INT8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use mircq/GLINER-INT8 with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("mircq/GLINER-INT8") text = "Cristiano Ronaldo dos Santos Aveiro was born on 5 February 1985 in Funchal, Madeira, Portugal." labels = ["person", "date", "location"] entities = model.predict_entities(text, labels) for entity in entities: print(entity["text"], "=>", entity["label"]) - GLiNER2
How to use mircq/GLINER-INT8 with GLiNER2:
from gliner2 import AutoExtractor extractor = AutoExtractor.from_pretrained("mircq/GLINER-INT8") # 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
Download tokenizer.json from mircq/GLINER-INT8: direct link, hf CLI and curl.
- Browser
- Download file 16.3 MB
-
https://huggingface.co/mircq/GLINER-INT8/resolve/main/tokenizer.json
- Command line
-
hf download hf://mircq/GLINER-INT8/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/mircq/GLINER-INT8/resolve/main/tokenizer.json
16.3 MB
- Xet hash:
- 960e67e35ae1e2eee52b50a33eb73c5ba01310e6e02fa62d31ff7516b2beec14
- Size of remote file:
- 16.3 MB
- SHA256:
- a1c7ccb287623cccb7c03150953b6d2a09dd95122933393c9151c3a60095c97e
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