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||
]
|
||
},
|
||
{
|
||
"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",
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||
"620871da669b40738224ff69d538187b",
|
||
"b84e764ef62b437e87a781f16247fd03",
|
||
"d77be9f7427d4ad2a1dfea71a81ae94b",
|
||
"4d8ed96b126f428cb2f9d5c87a0a1734",
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"e165e4e93a2d47e1950a24a9cacc9c2c",
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"09e3bdf1dd704536a221c45cb83153fb",
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||
"80bd7745299741379eb6e217e59b25a4",
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"8fcbe8d444604329885793af296e6e20",
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||
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||
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||
"c308fb7e86574aeeb1d0a3f466e258f4",
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||
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||
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||
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||
"c73360ec684f4fc7ab4c6a7feedbe869",
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||
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||
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||
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||
"fddba07539ca410fa0a01a507a552163",
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||
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||
"921073a913b645c09c729ea53652692e",
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||
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||
"e71aedf9bd0242588fb1f5b8ad0cf959",
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"cbcfd38560aa42229b38646589a21443",
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||
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||
"31eb343b909e44edab6cc1d2459e48f7",
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||
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||
"c152415760654b2ea94f7f6d3e198b0f",
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
"c356e4700b234ebaa31f281b8433f450",
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||
"d4bc8a7cffd14ff6af8dc511a3e1286d",
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||
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||
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||
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||
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||
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||
"517afe6a17874decbd47de3ce72a9404",
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||
"5aa2fdba1c904950bc54b262ab555034",
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||
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||
"ed501e110f7c464d9da1907f466436fc",
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||
"596887ed71ed4e8d8f86d7734ed2fdbf",
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||
"457d3bb22439443996def7ddb5297b48",
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||
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||
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||
"8b35ab0a9e8e4fc09ab0957d38dff455",
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
"0dd0a4cae3c54cc8b3bd84ed00fc7271",
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||
"1a4f2b73114c4c5f89a689408382606d",
|
||
"2fd632ca734f4c14b9afbed0f84fa6c9",
|
||
"3c20fdd236754831a25e34165cdd7e83",
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||
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||
"67a557d13a5f4085a171b30df2d3d1fa",
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||
"a40161360f63496d986c238f4737d18d",
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||
"3adadf95aa2b476c8d7fbae28e7601d2",
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||
"7b14c35716e24020b8d958d2eec87a1d",
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||
"cdfa3d0b37a84ded886daf1b3fbb40c8",
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||
"b0cb60bba68743fd921d9bd89592bbc4",
|
||
"79d01d6cc0d2495da6500d47a74907de",
|
||
"5bf59986c355418d9927965555b5d5ee",
|
||
"54b37c9fbcf943b28662d0bce70f9597",
|
||
"b9f4a2396f64412aa5ef5a1b3f54b188",
|
||
"761b316c638b4dd6bb961806c4c976db",
|
||
"664f9ce06b7741c08de1a9606011ba21",
|
||
"5749137c204647e891ba9ba9de89f6a8",
|
||
"7d24fe9ca6c448c29c80973d69920604",
|
||
"fc3f1b4a874d4e2bb8ce6acc1a19434c",
|
||
"c5079621e5524474a1d9e3e82b0dcca8",
|
||
"b1055daab78d424294b58b1409fd6817",
|
||
"a6a46c330e7e4299b3e3780da4bd4b14",
|
||
"825ff8fbc7f3491cb99285387866b8ab",
|
||
"717e22a6ed844d83af0aea0c58bd1395",
|
||
"0f68e5e4378f4f6e98a05d3130bdb88a",
|
||
"c8e1d4c758214a75be5b9b09da28d048",
|
||
"c7cc3813cc4943859c74c2b7d793391f"
|
||
]
|
||
},
|
||
"id": "Ru6qXPQJwcD1",
|
||
"outputId": "2e31cc79-4035-42d5-e223-d484aae002be"
|
||
},
|
||
"execution_count": 7,
|
||
"outputs": [
|
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{
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"output_type": "display_data",
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}
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},
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"metadata": {}
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},
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{
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}
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"metadata": {}
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|
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{
|
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"output_type": "stream",
|
||
"name": "stderr",
|
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"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"
|
||
]
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},
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},
|
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{
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"output_type": "stream",
|
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"name": "stderr",
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|
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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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" warnings.warn(\n"
|
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},
|
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{
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"output_type": "display_data",
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}
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},
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"metadata": {}
|
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},
|
||
{
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"output_type": "display_data",
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"data": {
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}
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},
|
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"metadata": {}
|
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},
|
||
{
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"output_type": "display_data",
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"metadata": {}
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||
},
|
||
{
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|
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}
|
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"metadata": {}
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},
|
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{
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"version_minor": 0,
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"model_id": "55ce5188abeb4321915dc969b167bf75"
|
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}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
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"output_type": "display_data",
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"version_minor": 0,
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"model_id": "b1f76457fb7148198dea00c00413f0ce"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"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"
|
||
]
|
||
},
|
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"version_minor": 0,
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"model_id": "a40161360f63496d986c238f4737d18d"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
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"Running Evaluation: 0%| | 0/58 [00:00<?, ?it/s]"
|
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],
|
||
"application/vnd.jupyter.widget-view+json": {
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"version_major": 2,
|
||
"version_minor": 0,
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||
"model_id": "5749137c204647e891ba9ba9de89f6a8"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"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()"
|
||
],
|
||
"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/",
|
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"height": 98,
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"output_type": "display_data",
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}
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"metadata": {}
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{
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||
"output_type": "execute_result",
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||
"data": {
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||
"text/plain": [
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"['greeting', 'req_title']"
|
||
]
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||
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"metadata": {},
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]
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{
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||
"cell_type": "code",
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"source": [
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||
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"data": {
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{
|
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"output_type": "execute_result",
|
||
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"text/plain": [
|
||
"['req_instruction']"
|
||
]
|
||
},
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||
"metadata": {},
|
||
"execution_count": 11
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"nlu.predict('The chicken is golden brown now. What do I need to do?')"
|
||
],
|
||
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|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
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