diff --git a/README.md b/README.md
index beb433b1d323b5ee871b0ab2ed8711071c2fa771..f5a42c4517fcb36c94bae94099b64cacadcfcd02 100644
--- a/README.md
+++ b/README.md
@@ -14,18 +14,13 @@ tags:
 
 # Prompt Injection Detection (encoder, multi-label)
 
-Encoder classifier that detects which prompt-injection attack categories (out of
-9) appear in an input. Fine-tuned from
-**[`jhu-clsp/mmBERT-base`](https://huggingface.co/jhu-clsp/mmBERT-base)**.
-Replaces the 2B Qwen decoder LoRA with a single-forward-pass encoder for
-lower-latency runtime-security use in LLM-Guard's `PromptInjection` scanner.
+Multi-label classifier over 9 prompt-injection attack categories,
+fine-tuned from **[`jhu-clsp/mmBERT-base`](https://huggingface.co/jhu-clsp/mmBERT-base)**. Single
+forward pass; `is_valid` = any attack above threshold (0.5).
 
 - **Base model**: [`jhu-clsp/mmBERT-base`](https://huggingface.co/jhu-clsp/mmBERT-base)
+- **Trained with**: max_seq_length=3072, epochs=6, lr=3e-05
 - **Labels (9)**: DirectInjection, Jailbreak, Adversarial, Extraction, Encoding, Manipulation, Smuggling, Indirect, MultiTurn
-- **Output**: per-category sigmoid; `is_valid` = any attack above threshold
-  (0.5).
-- **Multilingual / long context**: inherited from the base encoder; trained with
-  inputs up to the base model's positional limit.
 
 ## Usage
 
@@ -42,39 +37,19 @@ enc = tokenizer(text, truncation=True, max_length=3072, return_tensors="pt")
 with torch.no_grad():
     probs = model(**enc).logits.sigmoid()[0]      # per-category sigmoid
 
-threshold = 0.5
+# Decision thresholds fitted on a held-out split, stored in config (default 0.5).
 id2label = model.config.id2label                  # {0: "DirectInjection", 1: "Jailbreak", ...}
-present = {id2label[i]: round(float(p), 3) for i, p in enumerate(probs) if p >= threshold}
+cat_thr = getattr(model.config, "category_thresholds", None) or {}
+iv_thr = getattr(model.config, "is_valid_threshold", 0.5)
 
-# Same schema the original Qwen scanner emitted: is_valid = any attack fired.
-result = {"is_valid": bool(present), "category": {k: True for k in present}}
+present = {lab: round(float(probs[i]), 3)
+           for i, lab in id2label.items()
+           if probs[i] >= cat_thr.get(lab, 0.5)}
+is_valid = bool(float(probs.max()) >= iv_thr)     # the binary attack gate
+
+# Same schema the original Qwen scanner emitted.
+result = {"is_valid": is_valid, "category": {k: True for k in present}}
 print(result)   # e.g. {"is_valid": True, "category": {"DirectInjection": True}}
 ```
 
-## Test-set metrics (n=500)
-
-| Metric | Value |
-|--------|-------|
-| is_valid (attack-detection) accuracy | 0.864 |
-| category-set (exact) accuracy | 0.626 |
-| micro-F1 | 0.742 |
-| macro-F1 | 0.733 |
-| latency mean (ms/example) | 1.7679505981504917 |
-| latency p95 (ms/example) | 1.7809227108955383 |
-| device | cuda:0 |
-
-### Per-category F1
-
-| Category | F1 | Description |
-|----------|----|-------------|
-| `Adversarial` | 0.794 | Carefully crafted inputs that exploit model quirks or training artifacts to elicit unintended behavior without an obvious override. |
-| `DirectInjection` | 0.908 | Explicit instruction overrides that tell the model to ignore prior context (e.g. "ignore all previous instructions and …"). |
-| `Encoding` | 0.712 | Obfuscated payloads using base64 / ROT13 / leetspeak / homoglyphs / zero-width chars / shell pipes to bypass keyword filters. |
-| `Extraction` | 0.748 | Attempts to leak the system prompt, hidden instructions, or memorized training data (e.g. "print everything between <<system>> tags"). |
-| `Indirect` | 0.673 | Injection delivered through untrusted retrieved content (RAG passages, scraped pages, file contents) rather than the user's direct turn. |
-| `Jailbreak` | 0.577 | Persona / role swaps and constraint bypasses aimed at disabling safety alignment (e.g. DAN, "you are now an unrestricted assistant"). |
-| `Manipulation` | 0.693 | Social-engineering framings (urgency, authority, sympathy, false context) that pressure the model into compliance. |
-| `MultiTurn` | 0.653 | Crescendo / drip-feed attacks that build up across multiple turns to gradually erode guardrails. |
-| `Smuggling` | 0.843 | Hidden control tokens, chat-template markers, or special sequences injected to confuse the parser (e.g. fake `<|im_end|>` / role tags). |
-
-*Evaluated on `test_dataset_injection.csv`. Generated 2026-06-03 10:15 UTC.*
+> Test-set metrics are added by `eval_and_push_card.py` after evaluation.
diff --git a/checkpoint-1020/model.safetensors b/checkpoint-1020/model.safetensors
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diff --git a/checkpoint-1020/training_args.bin b/checkpoint-1020/training_args.bin
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diff --git a/checkpoint-1360/config.json b/checkpoint-1360/config.json
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-  "mlp_bias": false,
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-  "model_type": "modernbert",
-  "norm_bias": false,
-  "norm_eps": 1e-05,
-  "num_attention_heads": 12,
-  "num_hidden_layers": 22,
-  "pad_token_id": 0,
-  "position_embedding_type": "sans_pos",
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diff --git a/checkpoint-1360/model.safetensors b/checkpoint-1360/model.safetensors
deleted file mode 100644
index 81c17243186bb9f41fd364d759a9e62d191ebeb5..0000000000000000000000000000000000000000
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deleted file mode 100644
index 9905abce02fbf535bca153ab66bcadd42b3e482e..0000000000000000000000000000000000000000
--- a/checkpoint-1360/optimizer.pt
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deleted file mode 100644
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-version https://git-lfs.github.com/spec/v1
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diff --git a/checkpoint-1360/special_tokens_map.json b/checkpoint-1360/special_tokens_map.json
deleted file mode 100644
index be4ad795f49075ac5e56d8108ef63be314a1e967..0000000000000000000000000000000000000000
--- a/checkpoint-1360/special_tokens_map.json
+++ /dev/null
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-{
-  "additional_special_tokens": [
-    "<start_of_turn>",
-    "<end_of_turn>"
-  ],
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diff --git a/checkpoint-1360/tokenizer.json b/checkpoint-1360/tokenizer.json
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-{
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diff --git a/checkpoint-1360/trainer_state.json b/checkpoint-1360/trainer_state.json
deleted file mode 100644
index 5619fddde587e3dc71235e12f0aac1aceb084dac..0000000000000000000000000000000000000000
--- a/checkpoint-1360/trainer_state.json
+++ /dev/null
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-{
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-{
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diff --git a/checkpoint-846/training_args.bin b/checkpoint-846/training_args.bin
deleted file mode 100644
index c915cd25319e8b981fe1b97d83a5880c04bcaefa..0000000000000000000000000000000000000000
--- a/checkpoint-846/training_args.bin
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--- a/config.json
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@@ -6,6 +6,17 @@
   "attention_dropout": 0.0,
   "base_model": "jhu-clsp/mmBERT-base",
   "bos_token_id": 2,
+  "category_thresholds": {
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@@ -35,6 +46,7 @@
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+  "is_valid_threshold": 0.15,
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@@ -67,7 +79,7 @@
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@@ -79,12 +91,12 @@
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       "MultiTurn"
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     "task": "prompt-injection-detection",
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diff --git a/eval_metrics.json b/eval_metrics.json
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--- a/eval_metrics.json
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new file mode 100644
index 0000000000000000000000000000000000000000..bc892a8b512e8df7423b118ccccdb0371dcdbe8c
--- /dev/null
+++ b/thresholds.json
@@ -0,0 +1,14 @@
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+}
\ No newline at end of file
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@@ -14,8 +14,8 @@
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 }
\ No newline at end of file