Zero-Shot Classification
GLiNER2
Safetensors
English
Russian
extractor
safety
pii
ai-security
zero-shot
text-classification
span-categorization
token-classification
guardrails
Instructions to use hivetrace/gliner-guard-biencoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use hivetrace/gliner-guard-biencoder with GLiNER2:
from gliner2 import GLiNER2 model = GLiNER2.from_pretrained("hivetrace/gliner-guard-biencoder") # 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
| { | |
| "cache_labels": false, | |
| "counting_layer": "count_lstm_v2", | |
| "cross_fuser_heads": 0, | |
| "cross_fuser_layers": 1, | |
| "encoder_mode": "bi", | |
| "max_len": null, | |
| "max_width": 12, | |
| "model_name": "bogdanminko/mmBERT-small", | |
| "model_type": "extractor", | |
| "schema_model_name": null, | |
| "schema_projection_dim": 256, | |
| "token_pooling": "first", | |
| "transformers_version": "5.3.0" | |
| } | |