Text Classification
Transformers
Safetensors
modernbert
prompt-injection
jailbreak
security
multi-label
llm-guard
encoder
text-embeddings-inference
Instructions to use Accuknoxtechnologies/PromptInjection-Encoder-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Accuknoxtechnologies/PromptInjection-Encoder-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Accuknoxtechnologies/PromptInjection-Encoder-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Accuknoxtechnologies/PromptInjection-Encoder-v1") model = AutoModelForSequenceClassification.from_pretrained("Accuknoxtechnologies/PromptInjection-Encoder-v1") - Notebooks
- Google Colab
- Kaggle
add eval metrics json
Browse files- eval_metrics.json +16 -16
eval_metrics.json
CHANGED
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@@ -2,26 +2,26 @@
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"n": 500,
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"threshold": 0.5,
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"max_seq_length": 3072,
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"is_valid_accuracy": 0.
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"category_set_accuracy": 0.
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"micro_f1": 0.
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"macro_f1": 0.
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"per_category_f1": {
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"DirectInjection": 0.
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"Jailbreak": 0.
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"Adversarial": 0.
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"Extraction": 0.
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"Encoding": 0.
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"Manipulation": 0.
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"Smuggling": 0.
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"Indirect": 0.
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"MultiTurn": 0.
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},
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"latency_ms_per_example": {
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"mean": 1.
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"p95": 1.
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"device": "cuda:0"
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},
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"base_model": "jhu-clsp/mmBERT-base",
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"epochs":
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}
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"n": 500,
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"threshold": 0.5,
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"max_seq_length": 3072,
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+
"is_valid_accuracy": 0.864,
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+
"category_set_accuracy": 0.626,
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+
"micro_f1": 0.7423664122137404,
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| 8 |
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"macro_f1": 0.7334963397298655,
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| 9 |
"per_category_f1": {
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| 10 |
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"DirectInjection": 0.9076923076923077,
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| 11 |
+
"Jailbreak": 0.5773195876288659,
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| 12 |
+
"Adversarial": 0.7941176470588235,
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| 13 |
+
"Extraction": 0.7478260869565218,
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| 14 |
+
"Encoding": 0.7123287671232876,
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| 15 |
+
"Manipulation": 0.693069306930693,
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| 16 |
+
"Smuggling": 0.8429752066115702,
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| 17 |
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"Indirect": 0.6730769230769231,
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| 18 |
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"MultiTurn": 0.6530612244897959
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},
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"latency_ms_per_example": {
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| 21 |
+
"mean": 1.7679505981504917,
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| 22 |
+
"p95": 1.7809227108955383,
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| 23 |
"device": "cuda:0"
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| 24 |
},
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"base_model": "jhu-clsp/mmBERT-base",
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+
"epochs": 10
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}
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