detection.ipynb

{
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   "cell_type": "code",
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    {
     "name": "stderr",
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     "text": [
      "/Users/kovacs/projects/LettuceDetect/lettucedetect/models/inference.py:84: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  labels = torch.tensor(labels, device=self.device)\n",
      "Compiling the model with `torch.compile` and using a `torch.cpu` device is not supported. Falling back to non-compiled mode.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions: [{'start': 31, 'end': 71, 'confidence': 0.9891982674598694, 'text': ' The population of France is 69 million.'}]\n"
     ]
    }
   ],
   "source": [
    "from lettucedetect.models.inference import HallucinationDetector\n",
    "\n",
    "# For a transformer-based approach:\n",
    "detector = HallucinationDetector(\n",
    "    method=\"transformer\", model_path=\"KRLabsOrg/lettucedect-base-modernbert-en-v1\"\n",
    ")\n",
    "\n",
    "contexts = [\n",
    "    \"France is a country in Europe. The capital of France is Paris. The population of France is 67 million.\",\n",
    "]\n",
    "question = \"What is the capital of France? What is the population of France?\"\n",
    "answer = \"The capital of France is Paris. The population of France is 69 million.\"\n",
    "\n",
    "# Get span-level predictions indicating which parts of the answer are considered hallucinated.\n",
    "predictions = detector.predict(\n",
    "    context=contexts, question=question, answer=answer, output_format=\"spans\"\n",
    ")\n",
    "print(\"Predictions:\", predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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