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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[('Pupunta', 'VBAF'), ('ako', 'PRS'), ('kanina', 'RBW'), ('sa', 'CCT'), ('mall', 'NNP'), ('upang', 'CCB'), ('bumili', 'VBAF')]\n"
]
}
],
"source": [
"import joblib\n",
"import nltk\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",
"new_sentence = \"Pupunta ako kanina sa mall upang bumili\"\n",
"\n",
"# Tokenize the new sentence\n",
"tokens = nltk.word_tokenize(new_sentence)\n",
"\n",
"\n",
"tagged_tokens = []\n",
"\n",
"for token in tokens:\n",
" pos_tag = nltk.pos_tag([token])[0][1]\n",
" tagged_tokens.append((token, pos_tag))\n",
"\n",
"\n",
"# Extract features for each token in the new sentence\n",
"features = [word_features(tagged_tokens, i) for i in range(len(tagged_tokens))]\n",
"\n",
"# Use the trained CRF model to predict labels for the tokens\n",
"predicted_labels = crf_model.predict([features])[0]\n",
"\n",
"# Combine tokens with predicted labels\n",
"predicted_tokens_with_labels = list(zip(tokens, predicted_labels))\n",
"\n",
"print(predicted_tokens_with_labels)\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sentence is grammatically correct.\n",
"Probabilities: [0.00594444340094924, 0.9940555095672607]\n"
]
}
],
"source": [
"import torch\n",
"from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained(\"zklmorales/bert_finetuned\")\n",
"model = AutoModelForSequenceClassification.from_pretrained(\"zklmorales/bert_finetuned\")\n",
"\n",
"new_sentence = \"Pupunta ako kahapon sa siyudad upang bumili ang mga gamit ko\"\n",
"\n",
"# Tokenize the input text\n",
"inputs = tokenizer(new_sentence, return_tensors=\"pt\")\n",
"\n",
"# Forward pass through the model\n",
"with torch.no_grad():\n",
" outputs = model(**inputs)\n",
"\n",
"# Get the predicted class (label) from the model output\n",
"predicted_class = torch.argmax(outputs.logits, dim=1).item()\n",
"\n",
"# Get softmax probabilities\n",
"probabilities = torch.softmax(outputs.logits, dim=1).squeeze().tolist()\n",
"\n",
"# Print the prediction and probabilities\n",
"if predicted_class == 1:\n",
" print(\"Sentence is grammatically correct.\")\n",
"else:\n",
" print(\"Sentence is grammatically wrong.\")\n",
"\n",
"print(\"Probabilities:\", probabilities)\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Original sentence: Tumakbo ang mga bata mula sa pagsabog\n",
"Grammar correction candidates:\n",
"Patay ang mga bata mula sa pagsabog Probability: 0.9976784586906433\n",
"Alisin ang mga bata mula sa pagsabog Probability: 0.9921312928199768\n",
"Turuan ang mga bata mula sa pagsabog Probability: 0.9664002060890198\n",
"Hanapin ang mga bata mula sa pagsabog Probability: 0.9470312595367432\n",
"Sinusuportahan ang mga bata mula sa pagsabog Probability: 0.9317439198493958\n",
"['VBTS', 'DTC', 'DTCP', 'NNC', 'RBL', 'CCT', 'NNC']\n"
]
}
],
"source": [
"import torch\n",
"from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoModelForMaskedLM\n",
"\n",
"# Load pre-trained RoBERTa tokenizer and model for MLM\n",
"tokenizer_mlm = AutoTokenizer.from_pretrained(\"fine_tuned_model\")\n",
"model_mlm = AutoModelForMaskedLM.from_pretrained(\"fine_tuned_model\")\n",
"\n",
"# Load pre-trained BERT tokenizer and model for sequence classification\n",
"tokenizer_cls = AutoTokenizer.from_pretrained(\"zklmorales/bert_finetuned\")\n",
"model_cls = AutoModelForSequenceClassification.from_pretrained(\"zklmorales/bert_finetuned\")\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",
"new_sentence = \"Tumakbo ang mga bata mula sa pagsabog\"\n",
"\n",
"tokens = nltk.word_tokenize(new_sentence)\n",
"\n",
"tagged_tokens = []\n",
"\n",
"for token in tokens:\n",
" pos_tag = nltk.pos_tag([token])[0][1]\n",
" tagged_tokens.append((token, pos_tag))\n",
"\n",
"# Extract features for each token in the new sentence\n",
"features = [word_features(tagged_tokens, i) for i in range(len(tagged_tokens))]\n",
"\n",
"# Use the trained CRF model to predict labels for the tokens\n",
"predicted_labels = crf_model.predict([features])[0]\n",
"\n",
"# Combine tokens with predicted labels\n",
"predicted_tokens_with_labels = list(zip(tokens, predicted_labels))\n",
"\n",
"print(\"Original sentence:\", new_sentence)\n",
"\n",
"grammar_correction_candidates = []\n",
"\n",
"# Iterate over each word and mask it, then predict the masked word\n",
"for i, (token, predicted_label) in enumerate(zip(tokens, predicted_labels)):\n",
" # Check if the predicted label is a verb\n",
" if predicted_label.startswith('VB'):\n",
" # Mask the word\n",
" masked_words = tokens.copy()\n",
" masked_words[i] = tokenizer_mlm.mask_token\n",
" masked_sentence = \" \".join(masked_words)\n",
"\n",
" # Tokenize the masked sentence\n",
" tokens_mlm = tokenizer_mlm(masked_sentence, return_tensors=\"pt\")\n",
"\n",
" # Get the position of the masked token\n",
" masked_index = torch.where(tokens_mlm[\"input_ids\"] == tokenizer_mlm.mask_token_id)[1][0]\n",
"\n",
" # Get the logits for the masked token\n",
" with torch.no_grad():\n",
" outputs = model_mlm(**tokens_mlm)\n",
" predictions_mlm = outputs.logits\n",
"\n",
" # Get the top predicted words for the masked token\n",
" top_predictions_mlm = torch.topk(predictions_mlm[0, masked_index], k=5)\n",
" candidates_mlm = [tokenizer_mlm.decode(idx.item()) for idx in top_predictions_mlm.indices]\n",
"\n",
" # Reconstruct the sentence with each candidate\n",
" for candidate_mlm in candidates_mlm:\n",
" replaced_words = masked_words.copy()\n",
" replaced_words[i] = candidate_mlm\n",
" corrected_sentence = \" \".join(replaced_words).split() # Split and join to remove extra spaces\n",
" corrected_sentence = \" \".join(corrected_sentence) # Join words without extra spaces\n",
" \n",
" # Tokenize the corrected sentence for sequence classification\n",
" inputs_cls = tokenizer_cls(corrected_sentence, return_tensors=\"pt\")\n",
"\n",
" # Forward pass through the model for sequence classification\n",
" with torch.no_grad():\n",
" outputs_cls = model_cls(**inputs_cls)\n",
"\n",
" # Get softmax probabilities\n",
" probabilities = torch.softmax(outputs_cls.logits, dim=1).squeeze().tolist()\n",
" \n",
" # Get the most probable class\n",
" predicted_class = torch.argmax(outputs_cls.logits, dim=1).item()\n",
"\n",
" # Append the corrected sentence along with its probability and class\n",
" grammar_correction_candidates.append((corrected_sentence, probabilities[predicted_class]))\n",
"\n",
"# Sort the grammar correction candidates by their probabilities in descending order\n",
"grammar_correction_candidates.sort(key=lambda x: x[1], reverse=True)\n",
"\n",
"# Print the top 5 most probable grammar correction candidates\n",
"print(\"Grammar correction candidates:\")\n",
"for candidate, probability in grammar_correction_candidates:\n",
" print(candidate, \"Probability:\", probability)\n",
"print(predicted_labels)\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Nagising\n",
"67\n"
]
}
],
"source": [
"from fuzzywuzzy import fuzz\n",
"\n",
"original_word = \"Gigisingin\"\n",
"suggestions = [\"Tatakbo\", \"Nagising\", \"Hihiga\", \"Kakain\"]\n",
"\n",
"threshold = 60\n",
"\n",
"for suggestion in suggestions:\n",
" similarity_score = fuzz.ratio(original_word, suggestion)\n",
" if similarity_score >= threshold:\n",
" print(suggestion)\n",
" print(fuzz.ratio(original_word, suggestion))\n"
]
}
],
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"display_name": "Python 3",
"language": "python",
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