intent-crossencoder-miniLM2-L6-H768

intent-crossencoder-miniLM2-L6-H768

Cross-encoder reranker for enterprise intent detection (DLP / security). Fine-tuned from cross-encoder/nli-MiniLM2-L6-H768 on a synthetic intent-detection dataset.

Intended use

Binary classification: given a (user_input, intent_description) pair, predict whether the user input matches the intent. Designed as Stage-2 in a cascading firewall: Stage-1 (fast heuristic) → this model (reranker) → Stage-2 LLM (Qwen).

Input format

user_input [SEP] intent_description

Performance (held-out test set, threshold=0.3)

Training config

Inference snippet

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

model_id  = "aryasuneesh-quilr/intent-crossencoder-miniLM2-L6-H768"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model     = AutoModelForSequenceClassification.from_pretrained(model_id)
model.eval()

def score(user_input: str, intent_description: str) -> float:
    pair = f"{user_input} [SEP] {intent_description}"
    enc  = tokenizer(pair, return_tensors="pt", truncation=True, max_length=256)
    with torch.no_grad():
        logits = model(**enc).logits
    return torch.softmax(logits, dim=1)[0, 1].item()   # P(match)

# Example
s = score(
    "Our AWS_SECRET_ACCESS_KEY was found in a public repo",
    "Identify exposure of authentication credentials or API keys"
)
print(f"Match probability: {s:.4f}")   # use threshold 0.3727 for best F1

Files in this repo

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Generated 2026-02-23 08:35 UTC by ablation_reranker_training.py