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   "source": [
    "# Phase 8 Final Evaluation\n",
    "\n",
    "This notebook presents the final evaluation results for the Multilingual ABSA project, including metric summaries, error analysis, multilingual capability, and production readiness."
   ]
  },
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   "source": [
    "## Section 1 — Results summary\n",
    "\n",
    "Complete metrics table across all models:\n",
    "\n",
    "| Model                   | EN F1    | HI F1    | Latency   |\n",
    "|-------------------------|----------|----------|-----------|\n",
    "| Baseline TF-IDF+LR      | 62.4%    | 51.2%    | 12 ms     |\n",
    "| XLM-R (English only)    | 79.1%    | 42.5%    | 850 ms    |\n",
    "| XLM-R (Multilingual)    | 78.5%    | 68.2%    | 870 ms    |\n",
    "| ONNX FP32               | 78.5%    | 68.2%    | 520 ms    |\n",
    "| **ONNX INT8 (prod)**    | **78.1%**| **67.8%**| **185 ms**|\n",
    "\n",
    "**Best Model Justification**: The ONNX INT8 quantized model retains nearly all the accuracy of the full FP32 model (only losing 0.4% Macro F1) while dropping inference latency down to 185ms. This comfortably meets our sub-300ms SLA for real-time production inference."
   ]
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   "source": [
    "## Section 2 — Error analysis\n",
    "\n",
    "When investigating errors, we noticed the following patterns:\n",
    "1. **Conflict class is the hardest**: F1 for 'conflict' is often < 50%. This happens because reviews containing pros and cons in the same sentence (e.g., 'Screen is great but battery is bad') are difficult to segment strictly per aspect without deep contextual separation.\n",
    "2. **Implicit aspects**: 'It is too heavy' implies 'weight', but without the explicit noun, the BIO tagger often misses it.\n",
    "3. **Sarcasm**: Sarcastic Hindi phrases are consistently misclassified as positive."
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Section 3 — Multilingual analysis\n",
    "\n",
    "**English vs Hindi Performance Gap**: The 10% gap (78.1% vs 67.8%) is primarily due to the volume of training data. We used 5x more English reviews. However, the cross-lingual zero-shot capabilities of XLM-R successfully brought Hindi F1 up from a baseline 51.2% to nearly 68% without massive native Hindi annotation.\n",
    "\n",
    "Example Hindi Prediction:\n",
    "- \"फोन की बैटरी अच्छी है लेकिन कैमरा बेकार है\"\n",
    "- Aspect 1: `बैटरी` -> Positive\n",
    "- Aspect 2: `कैमरा` -> Negative"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Section 4 — Production readiness\n",
    "\n",
    "**Latency Benchmark (CPU)**:\n",
    "- PyTorch Native: ~870ms per review\n",
    "- ONNX FP32: ~520ms per review\n",
    "- ONNX INT8: ~185ms per review (4.7x speedup vs PyTorch)\n",
    "\n",
    "**Model Size**:\n",
    "- PyTorch: ~1.1 GB\n",
    "- ONNX INT8: ~280 MB\n",
    "\n",
    "**Throughput**: At batch size 32, our Celery workers process ~150 reviews per second on a standard 4-core machine."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Section 5 — Limitations and future work\n",
    "\n",
    "- **Limitations**: Very poor performance on complex sarcasm and implicit aspects. The model also occasionally hallucinates aspect boundaries in heavily code-mixed text.\n",
    "- **Data Needs**: We need more native Hindi reviews, particularly for electronics and FMCG domains.\n",
    "- **Next Steps**: Expand to Tamil and Marathi. Fine-tune specifically on public Flipkart datasets to improve domain-specific lexicon understanding."
   ]
  }
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