File size: 20,600 Bytes
ad0be11 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 |
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "311e31e2",
"metadata": {},
"outputs": [],
"source": [
"# Import pandas for DataFrame manipulation\n",
"import pandas as pd\n",
"# Import numpy for numerical operations\n",
"import numpy as np\n",
"# Import torch for tensor operations and device handling\n",
"import torch\n",
"# Import MBART model and tokenizer from Hugging Face Transformers\n",
"from transformers import MBartForConditionalGeneration, MBart50TokenizerFast\n",
"# Import cosine similarity for comparing embeddings\n",
"from sklearn.metrics.pairwise import cosine_similarity\n",
"# Import tqdm to show progress bars for loops\n",
"from tqdm import tqdm\n",
"# Import regex utilities for tokenization and cleaning\n",
"import re"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3363ac62",
"metadata": {},
"outputs": [],
"source": [
"# --- Configuration ---\n",
"MODEL_NAME = \"your/model/name\"\n",
"SRC_LANG_CODE = \"src_lang_code\"\n",
"TGT_LANG_CODE = \"tgt_lang_code\"\n",
"CORPUS_FILE = \"your/corpus/here.csv\"\n",
"DICT_FILE = \"your/bilingual/dictionary/here.csv\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d5d67ae6",
"metadata": {},
"outputs": [],
"source": [
"# Hyperparameters for the Knowledge Score (KS_i)\n",
"# You would tune these based on empirical performance\n",
"ALPHA = 0.1\n",
"BETA = 0.3\n",
"GAMMA = 0.6\n",
"PERCENTILE_THRESHOLD = 70 # Filter threshold: keep pairs above this percentile"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f5fb7924",
"metadata": {},
"outputs": [],
"source": [
"def preprocess_text(text):\n",
" \"\"\"\n",
" Safely preprocesses text by handling NaN, non-string values,\n",
" and performing normalization steps.\n",
" \"\"\"\n",
" if not isinstance(text, str):\n",
" return \"\"\n",
" text = text.strip().lower()\n",
" text = re.sub(r\"\\s+\", \" \", text) # Collapse multiple spaces\n",
" text = re.sub(r\"[^a-zA-Z0-9\\s']\", \"\", text) # Remove unwanted symbols (keep alphanumerics and apostrophes)\n",
" return text\n",
"\n",
"\n",
"def load_data(corpus_file, dict_file):\n",
" \"\"\"Loads, cleans, and prepares the parallel corpus and bilingual dictionary.\"\"\"\n",
"\n",
" # --- Load the CSVs safely ---\n",
" try:\n",
" raw_corpus = pd.read_csv(corpus_file)\n",
" word_dictionary = pd.read_csv(dict_file)\n",
" except Exception as e:\n",
" raise ValueError(f\"Error loading files: {e}\")\n",
"\n",
" # --- Ensure expected columns exist ---\n",
" required_corpus_cols = {'English', 'Tagin'}\n",
" required_dict_cols = {'English', 'Tagin'}\n",
"\n",
" if not required_corpus_cols.issubset(raw_corpus.columns):\n",
" raise ValueError(f\"Corpus file must contain columns: {required_corpus_cols}\")\n",
" if not required_dict_cols.issubset(word_dictionary.columns):\n",
" raise ValueError(f\"Dictionary file must contain columns: {required_dict_cols}\")\n",
"\n",
" # --- Drop rows with all NaN values ---\n",
" raw_corpus = raw_corpus.dropna(how='all')\n",
"\n",
" # --- Fill NaN cells with empty strings ---\n",
" raw_corpus = raw_corpus.fillna(\"\")\n",
"\n",
" # --- Apply text preprocessing ---\n",
" raw_corpus[\"English\"] = raw_corpus[\"English\"].apply(preprocess_text)\n",
" raw_corpus[\"Tagin\"] = raw_corpus[\"Tagin\"].apply(preprocess_text)\n",
"\n",
" # --- Clean dictionary entries ---\n",
" word_dictionary[\"English\"] = word_dictionary[\"English\"].apply(preprocess_text)\n",
" word_dictionary[\"Tagin\"] = word_dictionary[\"Tagin\"].apply(preprocess_text)\n",
"\n",
" # --- Convert dictionary to mapping ---\n",
" word_dictionary = word_dictionary.set_index('English')['Tagin'].to_dict()\n",
"\n",
" # --- Remove empty rows after cleaning ---\n",
" raw_corpus = raw_corpus[\n",
" (raw_corpus[\"English\"].str.strip() != \"\") &\n",
" (raw_corpus[\"Tagin\"].str.strip() != \"\")\n",
" ].reset_index(drop=True)\n",
"\n",
" print(f\"Loaded {len(raw_corpus)} sentence pairs and {len(word_dictionary)} dictionary entries.\")\n",
" return raw_corpus, word_dictionary"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "772824d1",
"metadata": {},
"outputs": [],
"source": [
"load_data(CORPUS_FILE,DICT_FILE)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7322656f",
"metadata": {},
"outputs": [],
"source": [
"# Function for Step 2: Perplexity (PPL)\n",
"@torch.no_grad()\n",
"def calculate_perplexity(sentence, model, tokenizer, device):\n",
" \"\"\"Computes perplexity of a sentence using the given LM.\"\"\"\n",
" try:\n",
" # Tokenize and format for mBART-50 (e.g., [lang_code] X [eos])\n",
" # We'll treat this as a generation task from the source language to itself\n",
" # to get log probabilities for the language modeling loss.\n",
" input_ids = tokenizer(\n",
" sentence,\n",
" return_tensors=\"pt\",\n",
" max_length=512,\n",
" truncation=True\n",
" ).input_ids.to(device)\n",
" \n",
" # Set the source language\n",
" tokenizer.src_lang = SRC_LANG_CODE\n",
" \n",
" # The labels for perplexity are the input tokens themselves, shifted.\n",
" # This is essentially a language modeling task.\n",
" labels = input_ids.clone()\n",
" \n",
" # Use -100 to ignore the loss for special tokens (like the language code token)\n",
" labels[:, 0] = -100\n",
"\n",
" outputs = model(input_ids=input_ids, labels=labels)\n",
" neg_log_likelihood = outputs.loss\n",
" \n",
" # Perplexity is exp(average negative log-likelihood)\n",
" # The 'outputs.loss' from the Transformers library is already the average NLL per token.\n",
" ppl = torch.exp(neg_log_likelihood).item()\n",
" return ppl\n",
" except Exception as e:\n",
" print(f\"Error calculating PPL for: '{sentence}'. Error: {e}\")\n",
" return float('inf') # Return a very high PPL for errors/bad sentences\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "231f19a8",
"metadata": {},
"outputs": [],
"source": [
"def normalize_inverse_ppl(ppl_scores, epsilon=1e-6):\n",
" \"\"\"\n",
" Safely normalizes inverse perplexity (1/PPL_i) to [0, 1].\n",
" \n",
" Handles edge cases where PPL scores are constant, contain inf/nan, or are invalid.\n",
" \"\"\"\n",
" ppl_scores = np.array(ppl_scores, dtype=np.float64)\n",
"\n",
" # Replace infinities or NaNs with large finite numbers for stability\n",
" ppl_scores = np.nan_to_num(ppl_scores, nan=np.inf, posinf=np.inf, neginf=np.inf)\n",
"\n",
" # Compute inverse PPL (fluency measure)\n",
" inv_ppl = 1.0 / (ppl_scores + epsilon)\n",
"\n",
" # Remove any remaining NaNs/Infs from inverse scores\n",
" inv_ppl = np.nan_to_num(inv_ppl, nan=0.0, posinf=0.0, neginf=0.0)\n",
"\n",
" inv_min = np.min(inv_ppl)\n",
" inv_max = np.max(inv_ppl)\n",
"\n",
" # Handle zero-range case: all scores are the same\n",
" if np.isclose(inv_max, inv_min) or np.isnan(inv_max - inv_min):\n",
" return np.zeros_like(inv_ppl)\n",
"\n",
" # Normal min–max scaling\n",
" inv_ppl_norm = (inv_ppl - inv_min) / (inv_max - inv_min)\n",
" inv_ppl_norm = np.clip(inv_ppl_norm, 0.0, 1.0)\n",
"\n",
" return inv_ppl_norm\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ad791177",
"metadata": {},
"outputs": [],
"source": [
"# Function for Step 3: Semantic Similarity (Sim)\n",
"\n",
"def calculate_semantic_similarity(s_i, t_i, model, tokenizer, device):\n",
" \"\"\"\n",
" Computes Cosine Similarity between source and target sentence embeddings \n",
" and normalizes the result to the range [0, 1].\n",
" \"\"\"\n",
" try:\n",
" def get_embedding(sentence, lang_code):\n",
" tokenizer.src_lang = lang_code\n",
" inputs = tokenizer(\n",
" sentence,\n",
" return_tensors=\"pt\",\n",
" max_length=512,\n",
" truncation=True,\n",
" padding=True\n",
" ).to(device)\n",
" \n",
" with torch.no_grad():\n",
" encoder_output = model.model.encoder(**inputs).last_hidden_state\n",
" \n",
" mean_embedding = encoder_output[:, 1:-1, :].mean(dim=1).squeeze() \n",
" \n",
" return mean_embedding.cpu().detach().numpy().reshape(1, -1)\n",
"\n",
" emb_s = get_embedding(s_i, SRC_LANG_CODE) \n",
" emb_t = get_embedding(t_i, TGT_LANG_CODE)\n",
"\n",
" sim_raw = cosine_similarity(emb_s, emb_t)[0][0]\n",
" \n",
" sim_normalized = (sim_raw + 1) / 2\n",
" sim_normalized = max(0.0, min(1.0, sim_normalized))\n",
" \n",
" return sim_normalized\n",
" \n",
" except Exception as e:\n",
" # print(f\"Error calculating Sim for: '{s_i}' and '{t_i}'. Error: {e}\")\n",
" return 0.0"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "87eebd8b",
"metadata": {},
"outputs": [],
"source": [
"# Function for Step 4: Lexical Match (Lex) # header describing the block\n",
"# blank line preserved for readability\n",
"# Define a function that computes a lexical match score based on a bilingual dictionary\n",
"def calculate_lexical_match(s_i, t_i, word_dictionary):\n",
" # Docstring start: describe purpose and formula for lex score\n",
" \"\"\"\n",
" Computes a dictionary-based lexical match score prioritizing phrase matches.\n",
" Score = (Count of source words covered by successfully translated phrases) / (Total words in source sentence)\n",
" \"\"\" # docstring end\n",
" # Helper: normalize text to ease phrase matching (lowercase, token boundaries)\n",
" def normalize_text(text):\n",
" # Simple tokenization: lowercase, remove non-word characters, and join back for easy phrase matching\n",
" return \" \" + \" \".join(re.findall(r'\\b\\w+\\b', text.lower())) + \" \" # pad with spaces for boundary-safe matching\n",
" # Normalize the source sentence for phrase lookups\n",
" s_normalized = normalize_text(s_i)\n",
" # Token set of the target sentence for quick membership tests\n",
" t_tokens = set(re.findall(r'\\b\\w+\\b', t_i.lower()))\n",
" # blank line preserved for readability\n",
" # Sort dictionary keys by length (descending) to prioritize phrase matches over single words\n",
" tagin_phrases = sorted(word_dictionary.keys(), key=len, reverse=True)\n",
" # blank line preserved for readability\n",
" # Extract source word tokens and compute total count\n",
" source_words = re.findall(r'\\b\\w+\\b', s_i.lower())\n",
" total_source_words = len(source_words)\n",
" # Initialize covered word counter\n",
" covered_word_count = 0\n",
" # If the source sentence is empty, return 0.0 immediately\n",
" if total_source_words == 0:\n",
" return 0.0\n",
" # Track indices of covered words if needed (not used further but kept for clarity)\n",
" covered_indices = set()\n",
" # Iterate over dictionary phrases (longest-first) to find matches in the source\n",
" for phrase in tagin_phrases:\n",
" # Skip empty dictionary entries\n",
" if not phrase:\n",
" continue\n",
" # Normalize the phrase for safe matching\n",
" norm_phrase = normalize_text(phrase)\n",
" # If the normalized phrase exists in the normalized source text, proceed\n",
" if norm_phrase in s_normalized:\n",
" # Get expected translation from the dictionary (lowercased)\n",
" expected_translation = word_dictionary[phrase].lower()\n",
" # Tokenize the expected translation into words\n",
" translation_words = re.findall(r'\\b\\w+\\b', expected_translation)\n",
" # Check whether all translated words appear in the target sentence tokens\n",
" is_translation_present = all(word in t_tokens for word in translation_words)\n",
" # If the translation words are present in the target, count the phrase as covered\n",
" if is_translation_present:\n",
" # Search for possibly multiple occurrences of the phrase in the source\n",
" start = 0\n",
" while True:\n",
" # Find next occurrence starting from 'start' index\n",
" start_index = s_normalized.find(norm_phrase, start)\n",
" # If no more occurrences, break the loop\n",
" if start_index == -1:\n",
" break\n",
" # Count how many words are in the matched phrase\n",
" phrase_word_count = len(re.findall(r'\\b\\w+\\b', phrase))\n",
" # Add the phrase's word count to the covered total\n",
" covered_word_count += phrase_word_count\n",
" # Advance the search start position past the current match\n",
" start = start_index + len(norm_phrase)\n",
" # end while loop for occurrences\n",
" # After checking all phrases, compute lex score as covered words / total source words (capped at 1.0)\n",
" lex_score = min(1.0, covered_word_count / total_source_words)\n",
" # Return lexical match score between 0 and 1\n",
" return lex_score"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "46f310e4",
"metadata": {},
"outputs": [],
"source": [
"# Main Algorithm Implementation\n",
"def knowledge_based_filtering(raw_corpus, word_dictionary, alpha, beta, gamma, percentile_threshold):\n",
" # 1. Load Model and Tokenizer (Step 1 of the algorithm's loop)\n",
" device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
" print(f\"Loading mBART-tgj-base model to {device}...\")\n",
" model = MBartForConditionalGeneration.from_pretrained(MODEL_NAME).to(device)\n",
" tokenizer = MBart50TokenizerFast.from_pretrained(MODEL_NAME)\n",
" \n",
" # Ensure source/target language codes are in the tokenizer vocabulary\n",
" if SRC_LANG_CODE not in tokenizer.vocab or TGT_LANG_CODE not in tokenizer.vocab:\n",
" print(f\"Warning: Language codes {SRC_LANG_CODE} or {TGT_LANG_CODE} not found in base mBART-tgj-base vocab.\")\n",
" print(\"Using placeholder language codes. Results may not be accurate.\")\n",
"\n",
" results = []\n",
"\n",
" # 2. Iterate through the corpus (Lines 1-6)\n",
" print(\"Processing corpus to calculate scores...\")\n",
" for index, row in tqdm(raw_corpus.iterrows(), total=len(raw_corpus), desc=\"Calculating KS\"):\n",
" s_i = row['English']\n",
" t_i = row['Tagin']\n",
"\n",
" # Line 2: Compute PPL_i (Lower is better)\n",
" pp= calculate_perplexity(s_i, model, tokenizer, device)\n",
" PPL_i= normalize_inverse_ppl(pp, epsilon=1e-6)\n",
" # PPL_i = normalize_inverse_ppl(row[\"Perplexity\"])\n",
" \n",
" # Line 3: Compute Sim_i (Higher is better)\n",
" Sim_i = calculate_semantic_similarity(s_i, t_i, model, tokenizer, device)\n",
" \n",
" # Line 4: Check Lex_i (Higher is better)\n",
" Lex_i = calculate_lexical_match(s_i, t_i, word_dictionary)\n",
" \n",
" # Line 5: Derive Knowledge Score (KS_i)\n",
" # Note: We use 1/PPL_i because PPL_i is an inverse quality metric (lower PPL is higher quality)\n",
" # while Sim and Lex are direct quality metrics (higher is better).\n",
" # We add a small epsilon to avoid division by zero, though a PPL of 0 is practically impossible.\n",
" # PPL_i_inv = 1.0 / (PPL_i + 1e-6)\n",
" # -----IMPORTANT------\n",
" \n",
" KS_i = alpha * PPL_i + beta * Sim_i + gamma * Lex_i\n",
" \n",
" results.append({\n",
" 'src_lang': s_i,\n",
" 'tgt_lang': t_i,\n",
" 'PPL_i': PPL_i,\n",
" 'Sim_i': Sim_i,\n",
" 'Lex_i': Lex_i,\n",
" 'PPL_i': PPL_i,\n",
" 'KS_i': KS_i\n",
" })\n",
"\n",
" # Convert results to DataFrame for filtering\n",
" scored_corpus = pd.DataFrame(results)\n",
"\n",
" # 3. Determine Threshold and Filter (Lines 7-9)\n",
" # Line 7: Find the 80th percentile of Knowledge Scores\n",
" tau_K = np.percentile(scored_corpus['KS_i'], percentile_threshold)\n",
" print(f\"\\n50th Percentile Knowledge Score (τ_K): {tau_K:.4f}\")\n",
" \n",
" # Line 8: Filter the corpus\n",
" D_filtered = scored_corpus[scored_corpus['KS_i'] >= tau_K].copy()\n",
" \n",
" # Final cleanup of columns and return\n",
" D_filtered = D_filtered[['src_lang', 'tgt_lang', 'KS_i']]\n",
" print(f\"Raw corpus size: {len(raw_corpus)}\")\n",
" print(f\"Filtered corpus size (KS_i >= τ_K): {len(D_filtered)}\")\n",
" \n",
" return D_filtered"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2b2ce69d",
"metadata": {},
"outputs": [],
"source": [
"# --- Execution --- # script entry and high-level execution steps\n",
"# blank line preserved for readability\n",
"# Guard to ensure code only runs when executed as a script, not on import\n",
"if __name__ == '__main__':\n",
" # 1. Load data # load and preprocess corpus and dictionary files\n",
" raw_corpus, word_dictionary = load_data(CORPUS_FILE, DICT_FILE)\n",
" # blank line preserved for readability\n",
" # 2. Run the filtering algorithm # compute KS_i and filter by percentile\n",
" filtered_corpus = knowledge_based_filtering(\n",
" raw_corpus, # pass the preprocessed corpus DataFrame\n",
" word_dictionary, # pass the dictionary mapping\n",
" ALPHA, BETA, GAMMA, # weighting hyperparameters for KS_i\n",
" PERCENTILE_THRESHOLD # percentile cutoff for filtering\n",
" )\n",
" # blank line preserved for readability\n",
" # Save the filtered corpus to CSV for downstream use\n",
" filtered_corpus.to_csv(\"tgj_corpus_filtered_70th.csv\", index=False)\n",
" # blank line preserved for readability\n",
" # Notify user of completion and where results were saved\n",
" print(\"\\nFiltering complete. Results saved to tgj_corpus_filtered_70th.csv\")\n",
" # Show a short preview of the filtered corpus\n",
" print(\"\\nFiltered Corpus Head:\")\n",
" print(filtered_corpus) # print DataFrame to stdout"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "ptorch",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|