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Model: Luimas/claim-extractor-detective-qwen3b
Source: Original Platform

scripts/evaluate.py

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+#!/usr/bin/env python
+# Quick self-contained eval: runs the GGUF (grammar-constrained) on built-in test passages and
+# reports JSON-validity, sarcasm handling, and contradiction detection.  pip install llama-cpp-python
+import glob, json, os
+from llama_cpp import Llama, LlamaGrammar
+HERE = os.path.dirname(os.path.abspath(__file__)); ROOT = os.path.dirname(HERE)
+GGUF = (glob.glob(os.path.join(ROOT, "*.gguf")) + glob.glob(os.path.join(HERE, "*.gguf")))[0]
+PROMPT = open(os.path.join(ROOT, "prompt.txt"), encoding="utf-8").read()
+GRAMMAR = LlamaGrammar.from_string(open(os.path.join(ROOT, "claim.gbnf"), encoding="utf-8").read())
+CASES = [
+    ("The mayor said crime fell last year. Hours later the police chief said it actually rose.", "contradiction"),
+    ("What an incredible update, my phone now dies by noon. The engineers really outdid themselves.", "sarcasm"),
+    ("On 2024-03-12 the central bank cut rates to 4.5%, citing easing inflation.", "fact"),
+]
+llm = Llama(model_path=GGUF, n_ctx=4096, n_gpu_layers=-1, verbose=False)
+ok = con = sar = nclaims = 0
+for text, tag in CASES:
+    o = llm.create_chat_completion(messages=[{"role": "user", "content": PROMPT + text}],
+                                   grammar=GRAMMAR, temperature=0.0, max_tokens=768)
+    try:
+        d = json.loads(o["choices"][0]["message"]["content"]); ok += 1; nclaims += len(d.get("claims", []))
+        if tag == "contradiction" and d.get("contradictions"): con += 1
+        if tag == "sarcasm" and any(c.get("stance") == "ironic" for c in d.get("claims", [])): sar += 1
+    except Exception:
+        pass
+print(f"valid JSON: {ok}/{len(CASES)} | avg claims: {nclaims/max(1,ok):.1f} | contradiction: {con}/1 | sarcasm: {sar}/1")