AMUseBot/ai_talks/AMUseBotBackend/utils/roberta_nlu_training.ipynb
2023-06-05 21:23:33 +02:00

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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m177.4/177.4 KB\u001b[0m \u001b[31m20.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m132.0/132.0 KB\u001b[0m \u001b[31m16.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m213.0/213.0 KB\u001b[0m \u001b[31m25.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.7/62.7 KB\u001b[0m \u001b[31m8.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m140.6/140.6 KB\u001b[0m \u001b[31m18.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.5/84.5 KB\u001b[0m \u001b[31m12.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Building wheel for validators (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Building wheel for pathtools (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import os \n",
"import glob\n",
"import json\n",
"from simpletransformers.classification import (\n",
" MultiLabelClassificationModel, MultiLabelClassificationArgs\n",
")\n",
"import pandas as pd\n",
"from sklearn.model_selection import train_test_split"
],
"metadata": {
"id": "vDokRpXPdLlI"
},
"execution_count": 2,
"outputs": []
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "-DYdAH_PZ7kq",
"outputId": "c2f83ee0-0313-445c-f19d-36a104e93547"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Cloning into 'CookDial'...\n",
"remote: Enumerating objects: 596, done.\u001b[K\n",
"remote: Counting objects: 100% (596/596), done.\u001b[K\n",
"remote: Compressing objects: 100% (89/89), done.\u001b[K\n",
"remote: Total 596 (delta 507), reused 585 (delta 501), pack-reused 0\u001b[K\n",
"Receiving objects: 100% (596/596), 1.23 MiB | 23.30 MiB/s, done.\n",
"Resolving deltas: 100% (507/507), done.\n"
]
}
],
"source": [
"!git clone https://github.com/YiweiJiang2015/CookDial.git"
]
},
{
"cell_type": "code",
"source": [
"# 1st pass - get all possible intents, create translation dict\n",
"all_intents = set()\n",
"\n",
"for file in list(glob.glob('CookDial/data/dialog/*.json')): \n",
" with open(file, encoding='utf-8') as dial_file:\n",
" dial_data = json.load(dial_file)\n",
" for message in dial_data['messages']: \n",
" if message['bot'] == False:\n",
" intents = json.loads(message['annotations'])['intent']\n",
" for intent in [intent.strip() for intent in intents.split(';')]:\n",
" if intent != '':\n",
" all_intents.add(intent)\n",
"\n",
"intent2int = dict(zip(sorted(list(all_intents)), range(len(all_intents))))\n",
"intent2int"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "vXXaLkirc9Ql",
"outputId": "bfd5f631-2d2f-4da2-8061-0415e50472d0"
},
"execution_count": 4,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'affirm': 0,\n",
" 'confirm': 1,\n",
" 'goodbye': 2,\n",
" 'greeting': 3,\n",
" 'negate': 4,\n",
" 'other': 5,\n",
" 'req_amount': 6,\n",
" 'req_duration': 7,\n",
" 'req_ingredient': 8,\n",
" 'req_ingredient_list': 9,\n",
" 'req_ingredient_list_ends': 10,\n",
" 'req_ingredient_list_length': 11,\n",
" 'req_instruction': 12,\n",
" 'req_is_recipe_finished': 13,\n",
" 'req_is_recipe_ongoing': 14,\n",
" 'req_parallel_action': 15,\n",
" 'req_repeat': 16,\n",
" 'req_start': 17,\n",
" 'req_substitute': 18,\n",
" 'req_temperature': 19,\n",
" 'req_title': 20,\n",
" 'req_tool': 21,\n",
" 'req_use_all': 22,\n",
" 'thank': 23}"
]
},
"metadata": {},
"execution_count": 4
}
]
},
{
"cell_type": "code",
"source": [
"# 2nd pass - append utterance + intent multi-hot vectors to processed data\n",
"processed_data = []\n",
"\n",
"for file in list(glob.glob('CookDial/data/dialog/*.json')): \n",
" with open(file, encoding='utf-8') as dial_file:\n",
" dial_data = json.load(dial_file) \n",
" for message in dial_data['messages']:\n",
" if message['bot'] == False:\n",
" annotations = json.loads(message['annotations'])\n",
" intents = [intent.strip() for intent in annotations['intent'].split(';')]\n",
" intents.remove('')\n",
" intents_multi_hot = [0] * len(all_intents)\n",
" for intent in intents:\n",
" intents_multi_hot[intent2int[intent]] = 1\n",
" processed_data.append([message['utterance'], intents_multi_hot])\n",
"\n",
"processed_data[:5]"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "g0gS47sAmQ1y",
"outputId": "dff4c4eb-b8fa-41a4-aef6-37233a14a621"
},
"execution_count": 5,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[['Hi what are we making today?',\n",
" [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0]],\n",
" ['Yes. What are the first two ingredients?',\n",
" [1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],\n",
" ['Ok. I have them. What are the next ingredients?',\n",
" [0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],\n",
" ['Ok. I have them as well. What are the next?',\n",
" [0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],\n",
" ['Ok. what kind of milk should I use?',\n",
" [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]]"
]
},
"metadata": {},
"execution_count": 5
}
]
},
{
"cell_type": "code",
"source": [
"processed_data_df = pd.DataFrame(processed_data)\n",
"train_data, test_data = train_test_split(processed_data_df, test_size=0.1, random_state=42, shuffle=True)"
],
"metadata": {
"id": "yJ_Ht32Cv_Q2"
},
"execution_count": 6,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# Train NLU model"
],
"metadata": {
"id": "itzBiH4D6Wq6"
}
},
{
"cell_type": "code",
"source": [
"# Very simple model definition, optimal epoch number is something between 5 and 10\n",
"model_args = MultiLabelClassificationArgs(num_train_epochs=5, overwrite_output_dir=True)\n",
"model = MultiLabelClassificationModel(\n",
" 'roberta',\n",
" 'roberta-base',\n",
" num_labels=len(all_intents),\n",
" args=model_args,\n",
")\n",
"\n",
"# Very simple training (run with GPU runtime!)\n",
"model.train_model(train_data)\n",
"\n",
"# Very simple evaluation\n",
"result, model_outputs, wrong_predictions = model.eval_model(\n",
" test_data\n",
")\n",
"\n",
"# Evaluation returns LRAP score - \"The obtained score is always strictly greater than 0 and the best value is 1\"\n",
"# From: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.label_ranking_average_precision_score.html\n",
"print(result)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 627,
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},
"id": "Ru6qXPQJwcD1",
"outputId": "2e31cc79-4035-42d5-e223-d484aae002be"
},
"execution_count": 7,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading: 0%| | 0.00/481 [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "694d7bfbd87e4f33a4daa40430cc94d2"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading: 0%| | 0.00/501M [00:00<?, ?B/s]"
],
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"version_major": 2,
"version_minor": 0,
"model_id": "e165e4e93a2d47e1950a24a9cacc9c2c"
}
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{
"output_type": "stream",
"name": "stderr",
"text": [
"Some weights of the model checkpoint at roberta-base were not used when initializing RobertaForMultiLabelSequenceClassification: ['lm_head.dense.weight', 'lm_head.bias', 'lm_head.layer_norm.bias', 'lm_head.decoder.weight', 'lm_head.layer_norm.weight', 'lm_head.dense.bias']\n",
"- This IS expected if you are initializing RobertaForMultiLabelSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
"- This IS NOT expected if you are initializing RobertaForMultiLabelSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
"Some weights of RobertaForMultiLabelSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.out_proj.bias', 'classifier.out_proj.weight', 'classifier.dense.bias', 'classifier.dense.weight']\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
]
},
{
"output_type": "display_data",
"data": {
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"name": "stderr",
"text": [
"/usr/local/lib/python3.8/dist-packages/simpletransformers/classification/classification_model.py:612: UserWarning: Dataframe headers not specified. Falling back to using column 0 as text and column 1 as labels.\n",
" warnings.warn(\n"
]
},
{
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"data": {
"text/plain": [
"Epoch: 0%| | 0/5 [00:00<?, ?it/s]"
],
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"metadata": {}
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{
"output_type": "display_data",
"data": {
"text/plain": [
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],
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"data": {
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],
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"version_major": 2,
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"name": "stderr",
"text": [
"/usr/local/lib/python3.8/dist-packages/simpletransformers/classification/classification_model.py:1454: UserWarning: Dataframe headers not specified. Falling back to using column 0 as text and column 1 as labels.\n",
" warnings.warn(\n"
]
},
{
"output_type": "display_data",
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" 0%| | 0/461 [00:00<?, ?it/s]"
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"data": {
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"Running Evaluation: 0%| | 0/58 [00:00<?, ?it/s]"
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"version_major": 2,
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"name": "stdout",
"text": [
"{'LRAP': 0.932345722168039, 'eval_loss': 0.044855168964392664}\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# Create and test NLU class"
],
"metadata": {
"id": "QVmLzOC76ZBb"
}
},
{
"cell_type": "code",
"source": [
"# Only this class and the trained model directory (\"checkpoint\" folders inside not needed) have to be copied into the chatbot project\n",
"class NLU:\n",
" def __init__(self):\n",
" self.int2intent = {0: 'affirm', 1: 'confirm', 2: 'goodbye', 3: 'greeting',\n",
" 4: 'negate', 5: 'other', 6: 'req_amount', 7: 'req_duration',\n",
" 8: 'req_ingredient', 9: 'req_ingredient_list', 10: 'req_ingredient_list_ends',\n",
" 11: 'req_ingredient_list_length', 12: 'req_instruction',\n",
" 13: 'req_is_recipe_finished', 14: 'req_is_recipe_ongoing',\n",
" 15: 'req_parallel_action', 16: 'req_repeat', 17: 'req_start', \n",
" 18: 'req_substitute', 19: 'req_temperature', 20: 'req_title',\n",
" 21: 'req_tool', 22: 'req_use_all', 23: 'thank'}\n",
"\n",
" # 2nd argument is the directory containing the trained model\n",
" self.model = MultiLabelClassificationModel('roberta', 'outputs', num_labels=len(self.int2intent))\n",
" \n",
" def predict(self, utterance):\n",
" predictions_vector, raw_outputs = self.model.predict([utterance])\n",
" predictions_vector = predictions_vector[0]\n",
" predicted_intents = []\n",
" for i in range(len(predictions_vector)):\n",
" if predictions_vector[i] == 1:\n",
" predicted_intents.append(self.int2intent[i])\n",
" return predicted_intents"
],
"metadata": {
"id": "RI1F-5QkxDGB"
},
"execution_count": 8,
"outputs": []
},
{
"cell_type": "code",
"source": [
"nlu = NLU()"
],
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"id": "haUIjgQMxFEQ"
},
"execution_count": 9,
"outputs": []
},
{
"cell_type": "code",
"source": [
"nlu.predict('Hi, what do we cook today? Recommend me something.')"
],
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" 0%| | 0/1 [00:00<?, ?it/s]"
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"nlu.predict('Continue, please.')"
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]
}