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"cells": [
{
"cell_type": "code",
"id": "initial_id",
"metadata": {
"collapsed": true,
"ExecuteTime": {
"end_time": "2024-10-18T02:33:03.992606Z",
"start_time": "2024-10-18T02:33:03.198886Z"
}
},
"source": [
"from transformers import AutoTokenizer, AutoModelForTokenClassification\n",
"import torch\n",
"\n",
"# Load the saved model and tokenizer\n",
"model_path = \"./results/best_model\"\n",
"tokenizer = AutoTokenizer.from_pretrained(model_path)\n",
"model = AutoModelForTokenClassification.from_pretrained(model_path)\n",
"\n",
"service_mapping = {\n",
" \"hotel\": [\"hotel\", \"hotels\", \"khách sạn\", \"khach san\", \"ks\"],\n",
" \"flight\": [\"flight\", \"flights\", \"vé máy bay\", \"máy bay\",\"may bay\"],\n",
" \"car rental\": [\"car rental\", \"car rentals\", \"thuê xe\", \"xe\"],\n",
" \"ticket\": [\"ticket\", \"tickets\", \"vé\", \"vé tham quan\",\"ve\", \"ve tham quan\"],\n",
" \"tour\": [\"tour\", \"tours\", \"du lịch\",\"du lich\"]\n",
" }\n",
"\n",
"# Define id2label mapping\n",
"id2label = {0: \"O\", 1: \"B-SERVICE\", 2: \"I-SERVICE\", 3: \"B-LOCATION\", 4: \"I-LOCATION\"}\n",
"\n",
"def map_service(service):\n",
" service = service.lower()\n",
" for key, values in service_mapping.items():\n",
" if any(v in service for v in values):\n",
" return key\n",
" return None\n",
"\n",
"def predict(text):\n",
" # Tokenize the input text\n",
" inputs = tokenizer(text, return_tensors=\"pt\", truncation=True, padding=True)\n",
" with torch.no_grad():\n",
" outputs = model(**inputs)\n",
" predictions = torch.argmax(outputs.logits, dim=2)\n",
" predicted_labels = [id2label[p.item()] for p in predictions[0]]\n",
" word_ids = inputs.word_ids()\n",
" aligned_labels = []\n",
" current_word = None\n",
" for word_id, label in zip(word_ids, predicted_labels):\n",
" if word_id != current_word:\n",
" aligned_labels.append(label)\n",
" current_word = word_id\n",
" \n",
" # Extract entities\n",
" entities = {\"SERVICE\": [], \"LOCATION\": []}\n",
" current_entity = None\n",
" current_tokens = []\n",
" \n",
" words = text.split()\n",
" for word, label in zip(words, aligned_labels):\n",
" if label.startswith(\"B-\"):\n",
" if current_entity:\n",
" if current_entity == \"SERVICE\":\n",
" mapped_service = map_service(\" \".join(current_tokens))\n",
" if mapped_service:\n",
" entities[current_entity].append(mapped_service)\n",
" else:\n",
" entities[current_entity].append(\" \".join(current_tokens))\n",
" current_entity = label[2:]\n",
" current_tokens = [word]\n",
" elif label.startswith(\"I-\") and current_entity:\n",
" current_tokens.append(word)\n",
" else:\n",
" if current_entity:\n",
" if current_entity == \"SERVICE\":\n",
" mapped_service = map_service(\" \".join(current_tokens))\n",
" if mapped_service:\n",
" entities[current_entity].append(mapped_service)\n",
" else:\n",
" entities[current_entity].append(\" \".join(current_tokens))\n",
" current_entity = None\n",
" current_tokens = []\n",
" \n",
" if current_entity:\n",
" if current_entity == \"SERVICE\":\n",
" mapped_service = map_service(\" \".join(current_tokens))\n",
" if mapped_service:\n",
" entities[current_entity].append(mapped_service)\n",
" else:\n",
" entities[current_entity].append(\" \".join(current_tokens))\n",
" \n",
" if entities[\"SERVICE\"]:\n",
" entities[\"SERVICE\"] = [entities[\"SERVICE\"][0]]\n",
" \n",
" return entities\n",
"\n",
"# Test function\n",
"def test_ner(text):\n",
" print(f\"Input: {text}\")\n",
" result = predict(text)\n",
" print(\"Output:\", result)\n",
" return result"
],
"outputs": [],
"execution_count": 3
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-10-18T03:19:29.075358Z",
"start_time": "2024-10-18T03:19:28.999181Z"
}
},
"cell_type": "code",
"source": [
"test_texts = [\n",
" \"tour du lich gia re da lat\"\n",
"]\n",
"\n",
"for text in test_texts:\n",
" test_ner(text)\n",
" print()"
],
"id": "65a6a938b3590ad2",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Input: tour du lich gia re da lat\n",
"Output: {'SERVICE': ['tour'], 'LOCATION': ['da lat']}\n",
"\n"
]
}
],
"execution_count": 13
},
{
"metadata": {},
"cell_type": "code",
"outputs": [],
"execution_count": null,
"source": "",
"id": "826408096ee93b4"
}
],
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"file_extension": ".py",
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