Text Classification
GLiNER2
GLiNER
English
German
prompt-injection
ai-agents
security
guardrails
mcp
lora
openbles
Instructions to use openbles/firewall-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use openbles/firewall-1 with GLiNER2:
from gliner2 import AutoExtractor extractor = AutoExtractor.from_pretrained("openbles/firewall-1") # 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 openbles/firewall-1 with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("openbles/firewall-1") 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 lora_config.json from openbles/firewall-1: direct link, hf CLI and curl.
- Browser
- Download file 218 Bytes
-
https://huggingface.co/openbles/firewall-1/resolve/main/lora_config.json
- Command line
-
hf download hf://openbles/firewall-1/lora_config.json
-
curl -L -o lora_config.json https://huggingface.co/openbles/firewall-1/resolve/main/lora_config.json
218 Bytes
| { | |
| "use_lora": true, | |
| "lora_r": 16, | |
| "lora_alpha": 32.0, | |
| "lora_dropout": 0.0, | |
| "lora_target_modules": [ | |
| "encoder", | |
| "span_rep", | |
| "classifier", | |
| "count_embed", | |
| "count_pred" | |
| ], | |
| "merged": true | |
| } |