How to use OvermindLab/nerpa with GLiNER2:
from gliner2 import GLiNER2 model = GLiNER2.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)
How to use OvermindLab/nerpa with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("OvermindLab/nerpa")
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{ "_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 }