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"2630dc50e32d4472a16cfb4eb17e097b": {
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"model_module": "@jupyter-widgets/controls",
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"model_name": "DescriptionStyleModel",
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"model_module_version": "1.5.0",
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"state": {
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"_model_module": "@jupyter-widgets/controls",
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"_model_module_version": "1.5.0",
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"_model_name": "DescriptionStyleModel",
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"_view_count": null,
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"_view_module": "@jupyter-widgets/base",
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"_view_module_version": "1.2.0",
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"_view_name": "StyleView",
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"description_width": ""
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},
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"cells": [
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|||
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|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"# Process dataset"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "3U_WUpdO6TZu"
|
|||
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|
}
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|
},
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|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"!pip -q install simpletransformers"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
},
|
|||
|
|
"id": "S0Uf9Z51mR0z",
|
|||
|
|
"outputId": "de71467d-fc80-4ff4-9122-815bcdd6f54c"
|
|||
|
|
},
|
|||
|
|
"execution_count": 1,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m250.5/250.5 KB\u001b[0m \u001b[31m18.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
|||
|
|
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m105.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
|||
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|
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m81.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m452.9/452.9 KB\u001b[0m \u001b[31m47.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.6/43.6 KB\u001b[0m \u001b[31m5.6 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",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.8/5.8 MB\u001b[0m \u001b[31m93.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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|
|||
|
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"\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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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",
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|
"\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
|
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|
|
"\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",
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|
"\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",
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|
"\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,
|
|||
|
|
"referenced_widgets": [
|
|||
|
|
"694d7bfbd87e4f33a4daa40430cc94d2",
|
|||
|
|
"1fd07cd761e7434d85c1d8c49c3f45c1",
|
|||
|
|
"620871da669b40738224ff69d538187b",
|
|||
|
|
"b84e764ef62b437e87a781f16247fd03",
|
|||
|
|
"d77be9f7427d4ad2a1dfea71a81ae94b",
|
|||
|
|
"4d8ed96b126f428cb2f9d5c87a0a1734",
|
|||
|
|
"db1e8202e91f4ca89b208f427e667aad",
|
|||
|
|
"8b9a439c743f41b1a246f73b57ce7f7a",
|
|||
|
|
"ce3f97321e6344759fc1677ae1f6d000",
|
|||
|
|
"5aeace3378f8478f955e68008942ef8e",
|
|||
|
|
"75877749fad44f8289e0d9dbec9f56ea",
|
|||
|
|
"e165e4e93a2d47e1950a24a9cacc9c2c",
|
|||
|
|
"e8878d5aee0b4dc7a2d746098f1f59b1",
|
|||
|
|
"084461e6ab544a6583c65f8525a000fb",
|
|||
|
|
"09e3bdf1dd704536a221c45cb83153fb",
|
|||
|
|
"80bd7745299741379eb6e217e59b25a4",
|
|||
|
|
"8fcbe8d444604329885793af296e6e20",
|
|||
|
|
"23420c2b8abf4856b7271d973fca60dc",
|
|||
|
|
"3eee21d29d46400fa138b4f7abda88b6",
|
|||
|
|
"a315824e6c82410aa6a18728ee47c90c",
|
|||
|
|
"187aa680575b481391fc5659c09713d0",
|
|||
|
|
"6e1391b14f9b4d1796b136f108263d2a",
|
|||
|
|
"652cb1eeb61841e0b9369aa1d431997d",
|
|||
|
|
"52381344c6dd4c4fa7968f8883e49181",
|
|||
|
|
"d72ba453917349a99e55c3683f0c2ec2",
|
|||
|
|
"afe596a4072040fb9f8207c93b19cf5d",
|
|||
|
|
"8cb75dde2d2b45fa970abd5d990d2163",
|
|||
|
|
"109065eef9564ea5a878149a278b11bf",
|
|||
|
|
"a308679f644f401ba5a1f747e350eced",
|
|||
|
|
"37f2ce644b8348338d3b523f215401dc",
|
|||
|
|
"c308fb7e86574aeeb1d0a3f466e258f4",
|
|||
|
|
"dbf8829907284c8a8183b017c2aae8e3",
|
|||
|
|
"ba1d21be483447c2b4153ae47b5e8ef7",
|
|||
|
|
"6a97d164c0c2422e9088fc8593395849",
|
|||
|
|
"c73360ec684f4fc7ab4c6a7feedbe869",
|
|||
|
|
"de2ced951f0c42b9a6825ec1eaf929d1",
|
|||
|
|
"ffc643f758bc4de88afe065299d4e088",
|
|||
|
|
"6bc3f90d7e5e4e88ba3d311a8a567c47",
|
|||
|
|
"fddba07539ca410fa0a01a507a552163",
|
|||
|
|
"66b5a099a2014851888bbdeeae52f321",
|
|||
|
|
"921073a913b645c09c729ea53652692e",
|
|||
|
|
"8c7eb08b18404d17a03de30a3ccbfbdb",
|
|||
|
|
"e71aedf9bd0242588fb1f5b8ad0cf959",
|
|||
|
|
"35c34efd9ed74315aa59cb65089ad273",
|
|||
|
|
"cbcfd38560aa42229b38646589a21443",
|
|||
|
|
"2988c068984e4b339dec5babd2bc428a",
|
|||
|
|
"bdbdaabdb6f446e0bf16a93c6a5661c8",
|
|||
|
|
"618fee944a154711829a04e1086dcd32",
|
|||
|
|
"31eb343b909e44edab6cc1d2459e48f7",
|
|||
|
|
"dcb3b61dd4024b8e9bf79fbb2d30e3e6",
|
|||
|
|
"c152415760654b2ea94f7f6d3e198b0f",
|
|||
|
|
"01d120e68e6848f1ae13894efed8c0cd",
|
|||
|
|
"7623b401609c44c9baa0fffd565150c3",
|
|||
|
|
"54b99e52fe6747d2a6bd680e03500f95",
|
|||
|
|
"658dbaa9b27749ffa888b63d4218ba80",
|
|||
|
|
"95e8017e96c4499d9faf11436ce71581",
|
|||
|
|
"a322b6988052406bb8d62d511ec78d58",
|
|||
|
|
"1ea5dd4200a24965a42bc5785cfdcdef",
|
|||
|
|
"43d27ee537c54d6c876d1a6c9fafa7f2",
|
|||
|
|
"bb500c6318a64d1c87897d757a7e3525",
|
|||
|
|
"2a8b1e7791ea4ae3ada6d1b30e08fa36",
|
|||
|
|
"85cd6a13086a4710af1a7b2314677764",
|
|||
|
|
"d56b160bb1024fcc8c809f39473c2dd9",
|
|||
|
|
"52805e8b234b4fc192f6699f0717ebc2",
|
|||
|
|
"c356e4700b234ebaa31f281b8433f450",
|
|||
|
|
"d4bc8a7cffd14ff6af8dc511a3e1286d",
|
|||
|
|
"9cb9941639294bc487b94539741c951e",
|
|||
|
|
"c915e102526f43bfbd8bbf6a3b4874ea",
|
|||
|
|
"3929a2b624dd4c4b91dcbcadd3447a26",
|
|||
|
|
"c478faf64a7743dab416dda9751afbcf",
|
|||
|
|
"13b4e4d45ae4424cb701b1dcb642c072",
|
|||
|
|
"a00fc08b2866432db04b36f8dea396d8",
|
|||
|
|
"612648114ecd42ae845991d0dc8a2f67",
|
|||
|
|
"5a35d02849a24439baa29ce67f4757ac",
|
|||
|
|
"262d009d7d734579830af0df2c8a1c1f",
|
|||
|
|
"209f48a1eef94af19724c0be26836726",
|
|||
|
|
"3337ee5a59e04e89bc91b9de496dd903",
|
|||
|
|
"8567db17d900457b8e86d9403f0d2cc5",
|
|||
|
|
"1346b9eddb434601b9874cd97dfe7196",
|
|||
|
|
"517afe6a17874decbd47de3ce72a9404",
|
|||
|
|
"5aa2fdba1c904950bc54b262ab555034",
|
|||
|
|
"33d451cac4544bf19944d7baff4ea2af",
|
|||
|
|
"ed501e110f7c464d9da1907f466436fc",
|
|||
|
|
"596887ed71ed4e8d8f86d7734ed2fdbf",
|
|||
|
|
"457d3bb22439443996def7ddb5297b48",
|
|||
|
|
"378911c7a7c748aab5791d9e8b9dcab2",
|
|||
|
|
"3cf76806441945a6951232fa2f904e51",
|
|||
|
|
"8b35ab0a9e8e4fc09ab0957d38dff455",
|
|||
|
|
"b6aeb792e88547f696ce89811ec4c0c4",
|
|||
|
|
"703d9c03faff4184b99b44e52f8a7530",
|
|||
|
|
"c87c7d421cc246ec92b051dd47db68bb",
|
|||
|
|
"d44a2e32cd3246668c319b8c9fb38723",
|
|||
|
|
"60f9c4d6d962480e99fda68ad3338fbe",
|
|||
|
|
"1dfed9b17fec435fa99ca991bdf9d4bc",
|
|||
|
|
"b3eb3d4141a34504995d3759a41b3d29",
|
|||
|
|
"88bd3b36bdb04847b7d5282ba620510b",
|
|||
|
|
"c8fc8ab7d55e4718a3acea17aa37857f",
|
|||
|
|
"5064b361a71c4be29c4862a1a4c47378",
|
|||
|
|
"2c1b299e71874916829e1e043ddf3c1c",
|
|||
|
|
"55ce5188abeb4321915dc969b167bf75",
|
|||
|
|
"7d37faa109f643a69c7d1feaee525905",
|
|||
|
|
"716e1d0285474c5e9749774df0f611dd",
|
|||
|
|
"70b8d9006e944deea30410b9f9dc717f",
|
|||
|
|
"19d4f2ef89cc4ef68f8b5f397c855b53",
|
|||
|
|
"e0e84118789940189951a941c5c395a9",
|
|||
|
|
"d5c09b96d2ff44d49e1eb874c7a7af27",
|
|||
|
|
"2fb5100d5e1d439292d3eee2e53a6e35",
|
|||
|
|
"2fd9974744834e0eb6cc9b423d3ae735",
|
|||
|
|
"8de4005d69d34b2eb1bb4a3416a06f11",
|
|||
|
|
"33190fe607074346bc9c7bde56972182",
|
|||
|
|
"b1f76457fb7148198dea00c00413f0ce",
|
|||
|
|
"58f3511e743240c7989c4647c6ea0b75",
|
|||
|
|
"8ca46bdbb56c4532a6875a30066b2b98",
|
|||
|
|
"4392df5c458d426e9ce9ccff9c716862",
|
|||
|
|
"cc1540722a554c6986970b59575704ca",
|
|||
|
|
"0dd0a4cae3c54cc8b3bd84ed00fc7271",
|
|||
|
|
"1a4f2b73114c4c5f89a689408382606d",
|
|||
|
|
"2fd632ca734f4c14b9afbed0f84fa6c9",
|
|||
|
|
"3c20fdd236754831a25e34165cdd7e83",
|
|||
|
|
"8a72eddabde64d9d9ac1e0c00dda2d45",
|
|||
|
|
"67a557d13a5f4085a171b30df2d3d1fa",
|
|||
|
|
"a40161360f63496d986c238f4737d18d",
|
|||
|
|
"3adadf95aa2b476c8d7fbae28e7601d2",
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"text": [
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"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",
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|||
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|
"- 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",
|
|||
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|
"- 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",
|
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|
"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",
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"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
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"/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",
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"text": [
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"/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",
|
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|
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|
"output_type": "stream",
|
|||
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|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"{'LRAP': 0.932345722168039, 'eval_loss': 0.044855168964392664}\n"
|
|||
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|
]
|
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|
}
|
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]
|
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|
{
|
|||
|
|
"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()"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "haUIjgQMxFEQ"
|
|||
|
|
},
|
|||
|
|
"execution_count": 9,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"nlu.predict('Hi, what do we cook today? Recommend me something.')"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
|
"height": 98,
|
|||
|
|
"referenced_widgets": [
|
|||
|
|
"ca75853b847d4c26ba9081195457e546",
|
|||
|
|
"ad8888a4bf44462c8466c5421c38b76d",
|
|||
|
|
"61cd5772e5d7480c9ae11d583a1e5f96",
|
|||
|
|
"f7203a68fc7945cd916c61a6d8667ba6",
|
|||
|
|
"c1626c6c0b6c49be9b95e384bf5346eb",
|
|||
|
|
"55d25ed4b2ec4297837d1c2c5e141aa1",
|
|||
|
|
"1a66f9166d704867bc92b457d5ab9050",
|
|||
|
|
"7be6544b702242f485be17cccadc5034",
|
|||
|
|
"06cfc310c9e94e8ab2098ddd953ef85b",
|
|||
|
|
"20a94b2e13144f4ab38657f545001df6",
|
|||
|
|
"f5534224d9dd48eeab4151ba3110b018",
|
|||
|
|
"26837e0c63ad4468839adbf3c5ccfe28",
|
|||
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