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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import joblib\n",
"import nltk\n",
"import csv\n",
"\n",
"# Load the saved CRF model\n",
"crf_model = joblib.load(r'D:\\Thesis\\POS Tag Automation\\crf_model.pkl')\n",
"\n",
"def word_features(sent, i):\n",
" word = sent[i][0]\n",
" pos = sent[i][1]\n",
" \n",
" # first word\n",
" if i == 0:\n",
" prevword = '<START>'\n",
" prevpos = '<START>'\n",
" else:\n",
" prevword = sent[i-1][0]\n",
" prevpos = sent[i-1][1]\n",
" \n",
" # first or second word\n",
" if i == 0 or i == 1:\n",
" prev2word = '<START>'\n",
" prev2pos = '<START>'\n",
" else:\n",
" prev2word = sent[i-2][0]\n",
" prev2pos = sent[i-2][1]\n",
" \n",
" # last word\n",
" if i == len(sent) - 1:\n",
" nextword = '<END>'\n",
" nextpos = '<END>'\n",
" else:\n",
" nextword = sent[i+1][0]\n",
" nextpos = sent[i+1][1]\n",
" \n",
" # suffixes and prefixes\n",
" pref_1, pref_2, pref_3, pref_4 = word[:1], word[:2], word[:3], word[:4]\n",
" suff_1, suff_2, suff_3, suff_4 = word[-1:], word[-2:], word[-3:], word[-4:]\n",
" \n",
" return {'word':word, \n",
" 'prevword': prevword,\n",
" 'prevpos': prevpos, \n",
" 'nextword': nextword, \n",
" 'nextpos': nextpos, \n",
" 'suff_1': suff_1, \n",
" 'suff_2': suff_2, \n",
" 'suff_3': suff_3, \n",
" 'suff_4': suff_4, \n",
" 'pref_1': pref_1, \n",
" 'pref_2': pref_2, \n",
" 'pref_3': pref_3, \n",
" 'pref_4': pref_4,\n",
" 'prev2word': prev2word,\n",
" 'prev2pos': prev2pos \n",
" }\n",
"\n",
"# Function to process a sentence and output tokens with their POS tags\n",
"def process_sentence(sentence, label):\n",
" tokens = nltk.word_tokenize(sentence)\n",
" tagged_tokens = [(token, nltk.pos_tag([token])[0][1]) for token in tokens]\n",
" features = [word_features(tagged_tokens, i) for i in range(len(tagged_tokens))]\n",
" predicted_labels = crf_model.predict([features])[0]\n",
" predicted_tokens_with_labels = list(zip(tokens, predicted_labels))\n",
" input_tokens = [token[0] for token in predicted_tokens_with_labels]\n",
" pos_tags = [token[1] for token in predicted_tokens_with_labels]\n",
" return input_tokens, pos_tags, [label] * len(input_tokens)\n",
"\n",
"# Input CSV file path\n",
"input_csv_file = \"D:\\Thesis\\Datasets\\preprocessed_dataset.csv\"\n",
"# Output CSV file path\n",
"output_csv_file = \"testing_bert_finetune.csv\"\n",
"\n",
"# Open input CSV file for reading\n",
"with open(input_csv_file, 'r', newline='', encoding='utf-8') as csv_input_file:\n",
" reader = csv.reader(csv_input_file)\n",
" # Open output CSV file for writing\n",
" with open(output_csv_file, 'w', newline='', encoding='utf-8') as csv_output_file:\n",
" writer = csv.writer(csv_output_file)\n",
" # Write header to output CSV file\n",
" writer.writerow(['sentence', 'pos_tag', 'label'])\n",
" # Skip header row in input CSV file\n",
" next(reader)\n",
" # Process each row in input CSV file\n",
" for row in reader:\n",
" sentence = row[0]\n",
" label = row[1]\n",
" # Process the sentence to obtain tokens with POS tags and labels\n",
" tokens, pos_tags, labels = process_sentence(sentence, label)\n",
" # Write [CLS] token\n",
" writer.writerow(['[CLS]', '[CLS]', '1'])\n",
" # Write each token with its POS tag and label to the output CSV file\n",
" for token, pos_tag, label in zip(tokens, pos_tags, labels):\n",
" writer.writerow([token, '[POS_' + pos_tag + ']', label])\n",
" # Write [SEP] token at the end of the sentence\n",
" writer.writerow(['[SEP]', '[SEP]', '1'])\n"
]
}
],
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