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"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "e4ca0fb0",
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"from PIL import Image\n",
"from tqdm.auto import tqdm\n",
"from transformers import AutoModelForCausalLM, AutoProcessor"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a961375e",
"metadata": {},
"outputs": [],
"source": [
"# Load dataset\n",
"from get_cdli_dataset import get_dataset, IMG_CACHE\n",
"\n",
"dataset = get_dataset()\n",
"test_dataset = dataset[\"test\"]\n",
"\n",
"print(test_dataset)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e226c45c",
"metadata": {},
"outputs": [],
"source": [
"# Load the model\n",
"\n",
"# model_path = \"PaddlePaddle/PaddleOCR-VL\" # base\n",
"# model_path = \"./outputs/sft\"\n",
"model_path = \"../\"\n",
"\n",
"model = AutoModelForCausalLM.from_pretrained(\n",
" model_path, trust_remote_code=True, torch_dtype=torch.bfloat16\n",
").to(\"cuda\").eval()\n",
"processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "97b9a2cb",
"metadata": {},
"outputs": [],
"source": [
"import pyxdameraulevenshtein as dl\n",
"\n",
"def compute_ter(expected_ids: list[int], predicted_ids: list[int]) -> float:\n",
" \"\"\"\n",
" Compute Token Error Rate (TER) between ground truth and completion tokens.\n",
" TER = (substitutions + deletions + insertions) / len(ground_truth)\n",
"\n",
" TER is better than CER for cuneiform OCR as:\n",
" - Multi-character Unicode signs count as 1 token instead of multiple chars\n",
" - Special tokens like @obverse/@reverse count as 1 token\n",
" \"\"\"\n",
"\n",
" if len(expected_ids) == 0:\n",
" return 0.0 if len(predicted_ids) == 0 else 1.0\n",
"\n",
" # Calculate edit distance on token sequences\n",
" distance = dl.damerau_levenshtein_distance(expected_ids, predicted_ids)\n",
"\n",
" # TER is the edit distance normalized by the truth token count\n",
" ter = distance / max(1, len(expected_ids))\n",
"\n",
" return ter"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "859c4fc2",
"metadata": {},
"outputs": [],
"source": [
"# Run inference on all test examples\n",
"results = []\n",
"total_ter = 0.0\n",
"\n",
"pbar = tqdm(test_dataset, desc=\"Evaluating on test set\")\n",
"\n",
"for idx, example in enumerate(pbar):\n",
" expected = example[\"unicode\"]\n",
" expected_ids = processor.tokenizer.encode(expected, add_special_tokens = False)\n",
"\n",
" # Load image\n",
" with Image.open(IMG_CACHE / f\"P{str(example['id']).rjust(6, '0')}.jpg\").convert(\n",
" \"RGB\"\n",
" ) as image:\n",
" # Prepare input\n",
" messages = [\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": [\n",
" {\"type\": \"image\", \"image\": image},\n",
" {\"type\": \"text\", \"text\": \"OCR:\"},\n",
" ],\n",
" },\n",
" ]\n",
"\n",
" inputs = processor.apply_chat_template(\n",
" messages, \n",
" tokenize=True, \n",
" add_generation_prompt=True, \t\n",
" return_dict=True,\n",
" return_tensors=\"pt\"\n",
" ).to(\"cuda\")\n",
"\n",
" # Generate prediction\n",
" with torch.no_grad():\n",
" output_ids = model.generate(\n",
" **inputs,\n",
" use_cache=True,\n",
" max_new_tokens=int(len(expected_ids) * 1.2),\n",
" repetition_penalty=1.03,\n",
" )\n",
"\n",
" predicted_ids = output_ids[0][inputs[\"input_ids\"].shape[1] :][:-1].tolist()\n",
"\n",
" # Compute TER for this example\n",
" ter = compute_ter(expected_ids, predicted_ids)\n",
" total_ter += ter\n",
"\n",
" pbar.set_postfix_str(f\"AVG TER={total_ter / (idx+1):.3f}\")\n",
"\n",
" prediction = processor.decode(\n",
" predicted_ids,\n",
" skip_special_tokens=False,\n",
" ).strip()\n",
"\n",
" # Store results\n",
" results.append(\n",
" {\n",
" \"id\": example[\"id\"],\n",
" \"expected\": expected,\n",
" \"prediction\": prediction,\n",
" \"ter\": ter,\n",
" }\n",
" )\n",
" tqdm.write(f\"\\033[94m\\nID: {example['id']} | TER: {ter:.4f}\\033[0m\")\n",
" tqdm.write(f\"\\033[92mExpected:\\033[0m\\n{expected}\")\n",
" tqdm.write(f\"\\033[91mPredicted:\\033[0m\\n{prediction}\")\n",
"\n",
"# Compute averages\n",
"average_ter = total_ter / len(test_dataset)\n",
"print(f\"\\n{'='*60}\")\n",
"print(f\"Average Token Error Rate (TER): {average_ter:.4f} ({average_ter*100:.2f}%)\")\n",
"print(f\"{'='*60}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3c6a8e02",
"metadata": {},
"outputs": [],
"source": [
"# Show examples: best and worst predictions (sorted by TER)\n",
"sorted_results = sorted(results, key=lambda x: x[\"ter\"])\n",
"\n",
"print(\"=\"*60)\n",
"print(\"BEST PREDICTIONS (Lowest TER)\")\n",
"print(\"=\"*60)\n",
"for i in range(min(10, len(sorted_results))):\n",
" r = sorted_results[i]\n",
" print(f\"\\nExample {i+1} - ID: {r['id']} - TER: {r['ter']:.4f}\")\n",
" print(f\"Expected:\\n{r['expected']}\")\n",
" print(f\"Predicted:\\n{r['prediction']}\")\n",
" print(\"-\"*60)\n",
"\n",
"print(\"\\n\" + \"=\"*60)\n",
"print(\"WORST PREDICTIONS (Highest TER)\")\n",
"print(\"=\"*60)\n",
"for i in range(min(10, len(sorted_results))):\n",
" r = sorted_results[-(i+1)]\n",
" print(f\"\\nExample {i+1} - ID: {r['id']} - TER: {r['ter']:.4f}\")\n",
" print(f\"Expected:\\n{r['expected']}\")\n",
" print(f\"Predicted:\\n{r['prediction']}\")\n",
" print(\"-\"*60)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d5ceae30",
"metadata": {},
"outputs": [],
"source": [
"# TER and CER distribution statistics\n",
"import numpy as np\n",
"\n",
"ter_values = [r[\"ter\"] for r in results]\n",
"\n",
"print(\"=\"*60)\n",
"print(\"TER (TOKEN ERROR RATE) DISTRIBUTION STATISTICS\")\n",
"print(\"=\"*60)\n",
"print(f\"Mean TER: {np.mean(ter_values):.4f} ({np.mean(ter_values)*100:.2f}%)\")\n",
"print(f\"Median TER: {np.median(ter_values):.4f} ({np.median(ter_values)*100:.2f}%)\")\n",
"print(f\"Std Dev: {np.std(ter_values):.4f}\")\n",
"print(f\"Min TER: {np.min(ter_values):.4f} ({np.min(ter_values)*100:.2f}%)\")\n",
"print(f\"Max TER: {np.max(ter_values):.4f} ({np.max(ter_values)*100:.2f}%)\")\n",
"print(f\"\\nPercentiles:\")\n",
"print(f\" 25th: {np.percentile(ter_values, 25):.4f}\")\n",
"print(f\" 50th: {np.percentile(ter_values, 50):.4f}\")\n",
"print(f\" 75th: {np.percentile(ter_values, 75):.4f}\")\n",
"print(f\" 90th: {np.percentile(ter_values, 90):.4f}\")\n",
"print(f\" 95th: {np.percentile(ter_values, 95):.4f}\")\n",
"print(f\" 98th: {np.percentile(ter_values, 98):.4f}\")\n",
"\n",
"# Count perfect predictions\n",
"perfect_predictions = sum(1 for ter in ter_values if ter == 0.0)\n",
"print(f\"\\nPerfect predictions (TER=0%): {perfect_predictions}/{len(ter_values)} ({perfect_predictions/len(ter_values)*100:.2f}%)\")\n",
"\n",
"# Count predictions with TER < 0.5 (less than 50% error)\n",
"good_predictions = sum(1 for ter in ter_values if ter < 0.5)\n",
"print(f\"Good predictions (TER<50%): {good_predictions}/{len(ter_values)} ({good_predictions/len(ter_values)*100:.2f}%)\")"
]
}
],
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