Token Classification
GLiNER2
Safetensors
GLiNER
English
extractor
named-entity-recognition
ner
pii
anonymisation
privacy
Eval Results (legacy)
Instructions to use OvermindLab/nerpa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use OvermindLab/nerpa with GLiNER2:
from gliner2 import AutoExtractor extractor = AutoExtractor.from_pretrained("OvermindLab/nerpa") # Extract entities text = "Apple CEO Tim Cook announced iPhone 15 in Cupertino yesterday." result = extractor.extract_entities(text, ["company", "person", "product", "location"]) print(result) - GLiNER
How to use OvermindLab/nerpa with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("OvermindLab/nerpa") 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"]) - Notebooks
- Google Colab
- Kaggle
Download config.json from OvermindLab/nerpa: direct link, hf CLI and curl.
- Browser
- Download file 254 Bytes
-
https://huggingface.co/OvermindLab/nerpa/resolve/main/config.json
- Command line
-
hf download hf://OvermindLab/nerpa/config.json
-
curl -L -o config.json https://huggingface.co/OvermindLab/nerpa/resolve/main/config.json
254 Bytes
| { | |
| "_attn_implementation_autoset": true, | |
| "counting_layer": "count_lstm", | |
| "max_width": 8, | |
| "model_name": "microsoft/deberta-v3-large", | |
| "model_type": "extractor", | |
| "token_pooling": "first", | |
| "transformers_version": "4.57.3", | |
| "use_moe": false | |
| } | |