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 eval.json from openbles/firewall-1: direct link, hf CLI and curl.
- Browser
- Download file 1.66 kB
-
https://huggingface.co/openbles/firewall-1/resolve/main/eval.json
- Command line
-
hf download hf://openbles/firewall-1/eval.json
-
curl -L -o eval.json https://huggingface.co/openbles/firewall-1/resolve/main/eval.json
1.66 kB
| { | |
| "run_at": "2026-10-09", | |
| "base": "fastino/GLiNER2.5-Decide", | |
| "adapter_sha256": "50a9158b3b3c12360f4ae75921c3048d1a257a5c7346c7684976a7e7fc49848c", | |
| "trained_on": [ | |
| "data/boundary_train.jsonl (840)", | |
| "data/deepset_train.jsonl (546)" | |
| ], | |
| "jev_labels_used": false, | |
| "base_untuned": { | |
| "boundary": { | |
| "n": 240, | |
| "injections": 120, | |
| "latency_p50_s": 0.032, | |
| "bar0.5": { | |
| "precision": 0.916, | |
| "recall": 1.0, | |
| "f1": 0.956, | |
| "false_flags": 0.092, | |
| "accuracy": 0.954 | |
| }, | |
| "bar0.9": { | |
| "precision": 0.991, | |
| "recall": 0.942, | |
| "f1": 0.966, | |
| "false_flags": 0.008, | |
| "accuracy": 0.967 | |
| } | |
| }, | |
| "deepset": { | |
| "n": 116, | |
| "injections": 60, | |
| "latency_p50_s": 0.032, | |
| "bar0.5": { | |
| "precision": 0.868, | |
| "recall": 0.55, | |
| "f1": 0.673, | |
| "false_flags": 0.089, | |
| "accuracy": 0.724 | |
| }, | |
| "bar0.9": { | |
| "precision": 1.0, | |
| "recall": 0.083, | |
| "f1": 0.154, | |
| "false_flags": 0.0, | |
| "accuracy": 0.526 | |
| } | |
| } | |
| }, | |
| "tuned": { | |
| "boundary": { | |
| "n": 240, | |
| "injections": 120, | |
| "latency_p50_s": 0.033, | |
| "bar0.5": { | |
| "precision": 1.0, | |
| "recall": 0.992, | |
| "f1": 0.996, | |
| "false_flags": 0.0, | |
| "accuracy": 0.996 | |
| }, | |
| "bar0.9": { | |
| "precision": 1.0, | |
| "recall": 0.992, | |
| "f1": 0.996, | |
| "false_flags": 0.0, | |
| "accuracy": 0.996 | |
| } | |
| }, | |
| "deepset": { | |
| "n": 116, | |
| "injections": 60, | |
| "latency_p50_s": 0.033, | |
| "bar0.5": { | |
| "precision": 0.965, | |
| "recall": 0.917, | |
| "f1": 0.94, | |
| "false_flags": 0.036, | |
| "accuracy": 0.94 | |
| }, | |
| "bar0.9": { | |
| "precision": 0.965, | |
| "recall": 0.917, | |
| "f1": 0.94, | |
| "false_flags": 0.036, | |
| "accuracy": 0.94 | |
| } | |
| } | |
| } | |
| } |