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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "jaosjY4rGRNH"
},
"source": [
"# Installing NeMo from source\n",
"\n",
"\n",
"You can run either this notebook locally (if you have all the dependencies and a GPU) or on Google Colab.\n",
"\n",
"Instructions for setting up Colab are as follows:\n",
"1. Open a new Python 3 notebook.\n",
"2. Import this notebook from GitHub (File -> Upload Notebook -> \"GITHUB\" tab -> copy/paste GitHub URL)\n",
"3. Connect to an instance with a GPU (Runtime -> Change runtime type -> select \"GPU\" for hardware accelerator)\n",
"4. Run the cell below to set up dependencies.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "goQzOSflEq27"
},
"outputs": [],
"source": [
"import os \n",
"BRANCH = 'r1.17.0'\n",
"!apt-get update && apt-get install -y libsndfile1 ffmpeg\n",
"!git clone https://github.com/NVIDIA/NeMo --branch $BRANCH\n",
"os.chdir('NeMo')\n",
"!./reinstall.sh\n",
"os.chdir('..')\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "GjQ_z_xQMDIb"
},
"source": [
"# Overview\n",
"\n",
"There are three tasks as part of this tutorial\n",
"\n",
"1. Intent and Slot Classification using Assistant Dataset and a BERT model\n",
"2. Intent Classification using Schema Guided Dialogue Dataset and a GPT2 model\n",
"3. Answer Extender using MS Marco NLGen Dataset and a BART model\n",
"\n",
"Feel free to skip to the task that interests you most after installing NeMo from source."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "AS-zwy8tEq2_"
},
"source": [
"# 1. Intent and Slot Classification using Assistant Dataset\n",
"\n",
"## 1.1 Task Description\n",
"\n",
"**Joint Intent and Slot classification** - is a task of classifying an Intent and detecting all relevant Slots (Entities)\n",
"for this Intent in a query.\n",
"For example, in the query: `What is the weather in Santa Clara tomorrow morning?`, we would like to classify the query\n",
"as a `weather` Intent, and detect `Santa Clara` as a `location` slot and `tomorrow morning` as a `date_time` slot.\n",
"Intents and Slots names are usually task specific and defined as labels in the training data.\n",
"This is a fundamental step that is executed in any task-driven Conversational Assistant.\n",
"\n",
"Our model enables to train and then detect both of these tasks together.\n",
"\n",
"Note: There is a similar model available at [Joint Intent Slot Classification Colab](https://colab.research.google.com/github/NVIDIA/NeMo/blob/stable/tutorials/nlp/Joint_Intent_and_Slot_Classification.ipynb). However, this model only support BERT style models while the model in this tutorial supports other types of models such as GPT2. "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "FJk_UAyeEq3B"
},
"source": [
"\n",
"## 1.2 Download Assistant dataset and convert to NeMo format\n",
"\n",
"This is a virtual assistant interaction data set that can be downloaded from here: https://github.com/xliuhw/NLU-Evaluation-Data.\n",
"There are about 10K training and 1K testing queries which cover 64 various Intents and 55 Slots. \n",
"\n",
"An example is:\n",
"\n",
"* utterance: what alarms have i set for tomorrow \n",
"* intent: alarm_query\n",
"* slots: date(tomorrow)\n",
"\n",
"\n",
"Note: While only the assistant dataset is used here, import_dataset.py is also compatible with ATIS and SNIPS"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "jjOVdGX2Eq3D"
},
"outputs": [],
"source": [
"# download and unzip the example dataset from github\n",
"!wget https://github.com/xliuhw/NLU-Evaluation-Data/archive/master.zip\n",
"!unzip master.zip\n",
"# convert the dataset to the NeMo format\n",
"!python NeMo/scripts/dataset_processing/nlp/intent_and_slot/import_datasets.py --dataset_name=assistant --source_data_dir=./NLU-Evaluation-Data-master --target_data_dir=./assistant"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5n81deZsEq3G"
},
"source": [
"## 1.3 Training and/or Testing the model\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "eoYc_8jhEq3G"
},
"outputs": [],
"source": [
"# model.dataset.data_dir: folder to load data from\n",
"# model.dataset.dialogues_example_dir: folder that stores predictions for each sample\n",
"!(python NeMo/examples/nlp/dialogue/dialogue.py \\\n",
" do_training=True \\\n",
" model.dataset.data_dir='./assistant' \\\n",
" model.dataset.dialogues_example_dir='./assistant_bert_examples' \\\n",
" model.dataset.task='assistant' \\\n",
" model.language_model.pretrained_model_name='bert-base-uncased' \\\n",
" exp_manager.create_wandb_logger=False)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "GaPmHjayEbg8"
},
"source": [
"**Results after 3 epochs**\n",
"\n",
"Intent report: \n",
"```\n",
" label precision recall f1 support \n",
" alarm_query (label_id: 0) 100.00 94.44 97.14 18\n",
" alarm_remove (label_id: 1) 100.00 90.91 95.24 11\n",
" alarm_set (label_id: 2) 94.12 94.12 94.12 17\n",
" audio_volume_down (label_id: 3) 75.00 42.86 54.55 7\n",
" audio_volume_mute (label_id: 4) 100.00 92.86 96.30 14\n",
" audio_volume_up (label_id: 5) 72.22 100.00 83.87 13\n",
" calendar_query (label_id: 6) 87.50 77.78 82.35 18\n",
" calendar_remove (label_id: 7) 94.44 100.00 97.14 17\n",
" calendar_set (label_id: 8) 94.44 94.44 94.44 18\n",
" cooking_recipe (label_id: 9) 85.71 70.59 77.42 17\n",
" datetime_convert (label_id: 10) 88.89 100.00 94.12 8\n",
" datetime_query (label_id: 11) 89.47 100.00 94.44 17\n",
" email_addcontact (label_id: 12) 80.00 100.00 88.89 8\n",
" email_query (label_id: 13) 100.00 83.33 90.91 18\n",
" email_querycontact (label_id: 14) 78.95 88.24 83.33 17\n",
" email_sendemail (label_id: 15) 94.44 94.44 94.44 18\n",
" general_affirm (label_id: 16) 100.00 100.00 100.00 17\n",
" general_commandstop (label_id: 17) 100.00 100.00 100.00 18\n",
" general_confirm (label_id: 18) 100.00 100.00 100.00 17\n",
" general_dontcare (label_id: 19) 100.00 100.00 100.00 18\n",
" general_explain (label_id: 20) 100.00 100.00 100.00 17\n",
" general_joke (label_id: 21) 91.67 100.00 95.65 11\n",
" general_negate (label_id: 22) 100.00 100.00 100.00 18\n",
" general_praise (label_id: 23) 100.00 100.00 100.00 17\n",
" general_quirky (label_id: 24) 60.00 50.00 54.55 18\n",
" general_repeat (label_id: 25) 100.00 100.00 100.00 17\n",
" iot_cleaning (label_id: 26) 100.00 100.00 100.00 15\n",
" iot_coffee (label_id: 27) 85.71 100.00 92.31 18\n",
" iot_hue_lightchange (label_id: 28) 100.00 94.12 96.97 17\n",
" iot_hue_lightdim (label_id: 29) 100.00 100.00 100.00 12\n",
" iot_hue_lightoff (label_id: 30) 100.00 100.00 100.00 17\n",
" iot_hue_lighton (label_id: 31) 100.00 50.00 66.67 4\n",
" iot_hue_lightup (label_id: 32) 84.62 91.67 88.00 12\n",
" iot_wemo_off (label_id: 33) 100.00 100.00 100.00 9\n",
" iot_wemo_on (label_id: 34) 100.00 85.71 92.31 7\n",
" lists_createoradd (label_id: 35) 90.00 100.00 94.74 18\n",
" lists_query (label_id: 36) 100.00 94.12 96.97 17\n",
" lists_remove (label_id: 37) 88.89 88.89 88.89 18\n",
" music_likeness (label_id: 38) 100.00 93.75 96.77 16\n",
" music_query (label_id: 39) 100.00 100.00 100.00 17\n",
" music_settings (label_id: 40) 77.78 100.00 87.50 7\n",
" news_query (label_id: 41) 72.73 88.89 80.00 18\n",
" play_audiobook (label_id: 42) 100.00 100.00 100.00 17\n",
" play_game (label_id: 43) 93.75 83.33 88.24 18\n",
" play_music (label_id: 44) 85.00 100.00 91.89 17\n",
" play_podcasts (label_id: 45) 100.00 88.89 94.12 18\n",
" play_radio (label_id: 46) 84.21 94.12 88.89 17\n",
" qa_currency (label_id: 47) 85.00 94.44 89.47 18\n",
" qa_definition (label_id: 48) 89.47 100.00 94.44 17\n",
" qa_factoid (label_id: 49) 64.00 88.89 74.42 18\n",
" qa_maths (label_id: 50) 84.62 84.62 84.62 13\n",
" qa_stock (label_id: 51) 87.50 77.78 82.35 18\n",
" recommendation_events (label_id: 52) 87.50 82.35 84.85 17\n",
" recommendation_locations (label_id: 53) 83.33 83.33 83.33 18\n",
" recommendation_movies (label_id: 54) 100.00 60.00 75.00 10\n",
" social_post (label_id: 55) 100.00 94.12 96.97 17\n",
" social_query (label_id: 56) 100.00 82.35 90.32 17\n",
" takeaway_order (label_id: 57) 92.31 70.59 80.00 17\n",
" takeaway_query (label_id: 58) 93.75 83.33 88.24 18\n",
" transport_query (label_id: 59) 81.25 76.47 78.79 17\n",
" transport_taxi (label_id: 60) 100.00 100.00 100.00 16\n",
" transport_ticket (label_id: 61) 85.00 94.44 89.47 18\n",
" transport_traffic (label_id: 62) 93.75 88.24 90.91 17\n",
" weather_query (label_id: 63) 89.47 100.00 94.44 17\n",
" -------------------\n",
" micro avg 91.16 91.16 91.16 996\n",
" macro avg 91.66 90.44 90.48 996\n",
" weighted avg 91.72 91.16 91.04 996\n",
"```\n",
"Slot report: \n",
"```\n",
" label precision recall f1 support \n",
" alarm_type (label_id: 0) 0.00 0.00 0.00 2\n",
" app_name (label_id: 1) 0.00 0.00 0.00 1\n",
" artist_name (label_id: 2) 17.39 80.00 28.57 5\n",
" audiobook_author (label_id: 3) 0.00 0.00 0.00 0\n",
" audiobook_name (label_id: 4) 64.52 74.07 68.97 27\n",
" business_name (label_id: 5) 81.48 84.62 83.02 52\n",
" business_type (label_id: 6) 80.00 80.00 80.00 20\n",
" change_amount (label_id: 7) 57.14 66.67 61.54 6\n",
" coffee_type (label_id: 8) 100.00 33.33 50.00 3\n",
" color_type (label_id: 9) 75.00 92.31 82.76 13\n",
" cooking_type (label_id: 10) 0.00 0.00 0.00 1\n",
" currency_name (label_id: 11) 100.00 96.43 98.18 28\n",
" date (label_id: 12) 87.88 87.22 87.55 133\n",
" definition_word (label_id: 13) 85.00 85.00 85.00 20\n",
" device_type (label_id: 14) 84.75 76.92 80.65 65\n",
" drink_type (label_id: 15) 0.00 0.00 0.00 0\n",
" email_address (label_id: 16) 64.29 100.00 78.26 9\n",
" email_folder (label_id: 17) 100.00 50.00 66.67 2\n",
" event_name (label_id: 18) 80.00 75.00 77.42 64\n",
" food_type (label_id: 19) 84.38 77.14 80.60 35\n",
" game_name (label_id: 20) 93.55 78.38 85.29 37\n",
" game_type (label_id: 21) 0.00 0.00 0.00 0\n",
" general_frequency (label_id: 22) 0.00 0.00 0.00 9\n",
" house_place (label_id: 23) 80.95 91.89 86.08 37\n",
" ingredient (label_id: 24) 0.00 0.00 0.00 1\n",
" joke_type (label_id: 25) 100.00 100.00 100.00 5\n",
" list_name (label_id: 26) 89.29 69.44 78.12 36\n",
" meal_type (label_id: 27) 0.00 0.00 0.00 3\n",
" media_type (label_id: 28) 78.95 83.33 81.08 36\n",
" movie_name (label_id: 29) 0.00 0.00 0.00 1\n",
" movie_type (label_id: 30) 0.00 0.00 0.00 0\n",
" music_album (label_id: 31) 0.00 0.00 0.00 0\n",
" music_descriptor (label_id: 32) 0.00 0.00 0.00 2\n",
" music_genre (label_id: 33) 81.82 90.00 85.71 10\n",
" news_topic (label_id: 34) 80.00 30.77 44.44 13\n",
" order_type (label_id: 35) 100.00 42.11 59.26 19\n",
" person (label_id: 36) 70.79 100.00 82.89 63\n",
" personal_info (label_id: 37) 76.19 94.12 84.21 17\n",
" place_name (label_id: 38) 82.86 84.47 83.65 103\n",
" player_setting (label_id: 39) 75.00 42.86 54.55 7\n",
" playlist_name (label_id: 40) 0.00 0.00 0.00 3\n",
" podcast_descriptor (label_id: 41) 92.31 54.55 68.57 22\n",
" podcast_name (label_id: 42) 66.67 16.67 26.67 12\n",
" radio_name (label_id: 43) 94.87 94.87 94.87 39\n",
" relation (label_id: 44) 90.91 90.91 90.91 11\n",
" song_name (label_id: 45) 100.00 6.67 12.50 15\n",
" time (label_id: 46) 77.57 84.69 80.98 98\n",
" time_zone (label_id: 47) 44.44 100.00 61.54 4\n",
" timeofday (label_id: 48) 86.96 80.00 83.33 25\n",
" transport_agency (label_id: 49) 80.00 57.14 66.67 7\n",
" transport_descriptor (label_id: 50) 0.00 0.00 0.00 5\n",
" transport_name (label_id: 51) 0.00 0.00 0.00 0\n",
" transport_type (label_id: 52) 88.89 100.00 94.12 40\n",
" weather_descriptor (label_id: 53) 87.50 87.50 87.50 8\n",
" O (label_id: 54) 97.07 97.52 97.30 5408\n",
" -------------------\n",
" micro avg 94.24 94.24 94.24 6582\n",
" macro avg 64.87 59.93 59.17 6582\n",
" weighted avg 94.23 94.24 93.95 6582\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-44x5PqyrOeQ"
},
"source": [
"## 1.4 (Optional) To train/ test a GPT2 model on the assistant dataset, run the cell below "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "QyqQbpR4rNHT"
},
"outputs": [],
"source": [
"# model.dataset.data_dir: folder to load data from\n",
"# model.dataset.dialogues_example_dir: folder that stores predictions for each sample\n",
"# model.tokenizer.special_tokens=\"{pad_token:'<|endoftext|>'}\": gpt2 doesn't specify a pad token, therefore using its EOS token as the pad token\n",
"# model.dataset.target_template=with_slots: this perform slot filling with intent classification\n",
"!(python NeMo/examples/nlp/dialogue/dialogue.py \\\n",
" do_training=True \\\n",
" model.dataset.data_dir='./assistant' \\\n",
" model.dataset.dialogues_example_dir='./assistant_gpt2_examples' \\\n",
" model.dataset.task='assistant' \\\n",
" model.language_model.pretrained_model_name='gpt2' \\\n",
" trainer.max_epochs=1 \\\n",
" model.tokenizer.special_tokens=\"{pad_token:'<|endoftext|>'}\" \\\n",
" model.dataset.target_template=with_slots \\\n",
" model.dataset.eval_mode=generation \\\n",
" exp_manager.create_wandb_logger=False)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "FbQ-6TVM1yQg"
},
"source": [
"**After 1 epoch:**\n",
"\n",
"More epochs would be helpful\n",
"\n",
"Intent report:\n",
"\n",
" ```\n",
" label precision recall f1 support \n",
" transport query (label_id: 0) 72.73 84.21 78.05 19\n",
" weather query (label_id: 1) 94.74 94.74 94.74 19\n",
" play game (label_id: 2) 92.86 68.42 78.79 19\n",
" qa currency (label_id: 3) 100.00 100.00 100.00 19\n",
" qa maths (label_id: 4) 100.00 100.00 100.00 14\n",
" iot wemo off (label_id: 5) 75.00 100.00 85.71 9\n",
" datetime convert (label_id: 6) 46.67 87.50 60.87 8\n",
" email addcontact (label_id: 7) 70.00 87.50 77.78 8\n",
" music likeness (label_id: 8) 57.89 61.11 59.46 18\n",
" music query (label_id: 9) 78.57 57.89 66.67 19\n",
" general negate (label_id: 10) 95.00 100.00 97.44 19\n",
" email sendemail (label_id: 11) 92.86 68.42 78.79 19\n",
" general affirm (label_id: 12) 95.00 100.00 97.44 19\n",
" play audiobook (label_id: 13) 57.69 78.95 66.67 19\n",
" general praise (label_id: 14) 100.00 94.74 97.30 19\n",
" alarm set (label_id: 15) 85.71 94.74 90.00 19\n",
" general explain (label_id: 16) 100.00 89.47 94.44 19\n",
" iot wemo on (label_id: 17) 83.33 71.43 76.92 7\n",
" cooking recipe (label_id: 18) 90.00 94.74 92.31 19\n",
" music settings (label_id: 19) 60.00 42.86 50.00 7\n",
" social post (label_id: 20) 84.21 84.21 84.21 19\n",
" recommendation events (label_id: 21) 72.73 84.21 78.05 19\n",
" audio volume up (label_id: 22) 76.47 100.00 86.67 13\n",
" lists remove (label_id: 23) 73.08 100.00 84.44 19\n",
" transport ticket (label_id: 24) 94.74 94.74 94.74 19\n",
" general joke (label_id: 25) 100.00 100.00 100.00 12\n",
" play podcasts (label_id: 26) 94.12 84.21 88.89 19\n",
" iot hue lightchange (label_id: 27) 85.71 63.16 72.73 19\n",
" audio volume mute (label_id: 28) 84.62 73.33 78.57 15\n",
" general dontcare (label_id: 29) 95.00 100.00 97.44 19\n",
" qa definition (label_id: 30) 77.27 89.47 82.93 19\n",
" email querycontact (label_id: 31) 58.33 73.68 65.12 19\n",
" general commandstop (label_id: 32) 100.00 100.00 100.00 19\n",
" calendar remove (label_id: 33) 94.44 89.47 91.89 19\n",
" news query (label_id: 34) 100.00 57.89 73.33 19\n",
" calendar query (label_id: 35) 63.16 63.16 63.16 19\n",
" social query (label_id: 36) 88.24 83.33 85.71 18\n",
" transport traffic (label_id: 37) 90.48 100.00 95.00 19\n",
" transport taxi (label_id: 38) 100.00 94.44 97.14 18\n",
" alarm query (label_id: 39) 100.00 94.74 97.30 19\n",
" iot hue lightoff (label_id: 40) 88.89 84.21 86.49 19\n",
" takeaway order (label_id: 41) 81.25 68.42 74.29 19\n",
" iot coffee (label_id: 42) 100.00 94.74 97.30 19\n",
" recommendation movies (label_id: 43) 75.00 90.00 81.82 10\n",
" iot hue lightup (label_id: 44) 78.57 78.57 78.57 14\n",
" email query (label_id: 45) 85.71 94.74 90.00 19\n",
" lists createoradd (label_id: 46) 82.35 73.68 77.78 19\n",
" play radio (label_id: 47) 84.21 84.21 84.21 19\n",
" audio volume down (label_id: 48) 100.00 87.50 93.33 8\n",
" general quirky (label_id: 49) 30.00 15.79 20.69 19\n",
" play music (label_id: 50) 71.43 52.63 60.61 19\n",
" qa stock (label_id: 51) 90.48 100.00 95.00 19\n",
" iot cleaning (label_id: 52) 93.33 87.50 90.32 16\n",
" iot hue lightdim (label_id: 53) 100.00 100.00 100.00 12\n",
" recommendation locations (label_id: 54) 100.00 89.47 94.44 19\n",
" general repeat (label_id: 55) 100.00 100.00 100.00 19\n",
" takeaway query (label_id: 56) 77.27 89.47 82.93 19\n",
" alarm remove (label_id: 57) 100.00 100.00 100.00 11\n",
" datetime query (label_id: 58) 75.00 63.16 68.57 19\n",
" iot hue lighton (label_id: 59) 60.00 100.00 75.00 3\n",
" qa factoid (label_id: 60) 50.00 57.89 53.66 19\n",
" calendar set (label_id: 61) 75.00 78.95 76.92 19\n",
" general confirm (label_id: 62) 100.00 100.00 100.00 19\n",
" lists query (label_id: 63) 66.67 73.68 70.00 19\n",
" label_id: 64 0.00 0.00 0.00 0\n",
" -------------------\n",
" micro avg 83.55 83.55 83.55 1076\n",
" macro avg 83.53 83.93 83.01 1076\n",
" weighted avg 84.26 83.55 83.30 1076\n",
" \n",
"```\n",
"\n",
"```\n",
"────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
" Test metric DataLoader 0\n",
"────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
" intent_f1 83.55018615722656\n",
" intent_precision 83.55018615722656\n",
" intent_recall 83.55018615722656\n",
" slot_f1 73.99985919756773\n",
"slot_joint_goal_accuracy 65.89219330855019\n",
" slot_precision 73.85223048327137\n",
" slot_recall 74.14807930607186\n",
" test_intent_accuracy 83.55018587360595\n",
" test_loss_epoch 0.019178826361894608\n",
"────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Gd42arYoEq3J"
},
"source": [
"# 2. Schema Guided Dialogue (SGD)\n",
"\n",
"## 2.1 Task Description\n",
"---\n",
"\n",
"SGD is a multi-domain intent classification dataset from Google with close to 100k examples.\n",
"\n",
"An example is:\n",
"\n",
"* utterance: I will be eating there at 11:30 am so make the reservation for then.\n",
"* intent: ReserveRestaurant\n",
"* slots: {\"time\": \"11:30 am\"}\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "neH8rXwjEq3J"
},
"source": [
"## 2.2 Download the dataset"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "IgD8eavfJ5pi"
},
"outputs": [],
"source": [
"!git clone https://github.com/google-research-datasets/dstc8-schema-guided-dialogue.git"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7G7uPrUpEq3J"
},
"source": [
"## 2.3 Training and/or Testing the model\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "gqo-rwQlEq3K"
},
"outputs": [],
"source": [
"# model.dataset.data_dir: folder to load data from\n",
"# model.dataset.dialogues_example_dir: folder that stores predictions for each sample\n",
"# model.tokenizer.special_tokens=\"{pad_token:'<|endoftext|>'}\": gpt2 doesn't specify a pad token, therefore using its EOS token as the pad token\n",
"\n",
"!(python NeMo/examples/nlp/dialogue/dialogue.py \\\n",
" do_training=True \\\n",
" model.dataset.data_dir='./dstc8-schema-guided-dialogue' \\\n",
" model.dataset.dialogues_example_dir='./sgd_gpt2_predictions' \\\n",
" model.dataset.task='sgd' \\\n",
" model.language_model.pretrained_model_name='gpt2' \\\n",
" trainer.max_epochs=1 \\\n",
" model.tokenizer.special_tokens=\"{pad_token:'<|endoftext|>'}\" \\\n",
" exp_manager.create_wandb_logger=False)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "kGDlV5HvI2PQ"
},
"outputs": [],
"source": [
"!ls sgd_gpt2_predictions"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "p8g0f5KDTu9K"
},
"source": [
"**After 1 epoch:**\n",
"\n",
"More epochs would needed to reach convergence.\n",
"\n",
"\n",
"```\n",
" label precision recall f1 support \n",
" check balance (label_id: 0) 0.00 0.00 0.00 0\n",
" find trains (label_id: 1) 80.20 91.95 85.68 348\n",
" make payment (label_id: 2) 83.12 28.07 41.97 228\n",
" book appointment (label_id: 3) 86.93 87.15 87.04 397\n",
" get cars available (label_id: 4) 96.88 90.51 93.58 274\n",
" get event dates (label_id: 5) 0.00 0.00 0.00 0\n",
" buy bus ticket (label_id: 6) 78.61 91.33 84.49 173\n",
" add event (label_id: 7) 0.00 0.00 0.00 0\n",
" get alarms (label_id: 8) 58.33 77.78 66.67 45\n",
" reserve car (label_id: 9) 83.75 72.43 77.68 185\n",
" get events (label_id: 10) 0.00 0.00 0.00 0\n",
" reserve roundtrip flights (label_id: 11) 0.00 0.00 0.00 0\n",
" lookup music (label_id: 12) 89.83 86.89 88.33 61\n",
" book house (label_id: 13) 91.13 92.50 91.81 200\n",
" search oneway flight (label_id: 14) 74.77 47.70 58.25 174\n",
" buy event tickets (label_id: 15) 72.19 95.31 82.15 128\n",
" find apartment (label_id: 16) 0.00 0.00 0.00 0\n",
" schedule visit (label_id: 17) 77.27 66.06 71.23 386\n",
" play media (label_id: 18) 92.94 86.81 89.77 91\n",
" get ride (label_id: 19) 99.41 98.82 99.12 170\n",
" reserve oneway flight (label_id: 20) 0.00 0.00 0.00 0\n",
" find bus (label_id: 21) 96.64 87.53 91.86 361\n",
" find restaurants (label_id: 22) 77.14 91.22 83.59 148\n",
" get times for movie (label_id: 23) 0.00 0.00 0.00 0\n",
" transfer money (label_id: 24) 0.00 0.00 0.00 0\n",
" request payment (label_id: 25) 46.71 63.39 53.79 112\n",
" play movie (label_id: 26) 100.00 65.11 78.87 321\n",
" search house (label_id: 27) 97.91 91.83 94.77 306\n",
" search roundtrip flights (label_id: 28) 67.49 82.41 74.21 199\n",
" find provider (label_id: 29) 95.11 90.53 92.77 602\n",
" find attractions (label_id: 30) 100.00 89.01 94.19 91\n",
" reserve hotel (label_id: 31) 56.75 97.04 71.62 169\n",
" lookup song (label_id: 32) 0.00 0.00 0.00 0\n",
" add alarm (label_id: 33) 95.68 60.18 73.89 221\n",
" find home by area (label_id: 34) 48.95 59.79 53.83 194\n",
" get available time (label_id: 35) 0.00 0.00 0.00 0\n",
" buy movie tickets (label_id: 36) 100.00 29.39 45.42 473\n",
" reserve restaurant (label_id: 37) 95.71 84.80 89.92 342\n",
" find movies (label_id: 38) 62.40 97.61 76.14 335\n",
" get weather (label_id: 39) 100.00 87.69 93.44 195\n",
" search hotel (label_id: 40) 99.35 52.60 68.78 289\n",
" find events (label_id: 41) 99.57 82.56 90.27 281\n",
" play song (label_id: 42) 0.00 0.00 0.00 0\n",
" rent movie (label_id: 43) 0.00 0.00 0.00 0\n",
" get train tickets (label_id: 44) 45.83 5.56 9.91 198\n",
" none (label_id: 45) 55.77 98.90 71.32 728\n",
" label_id: 46 0.00 0.00 0.00 0\n",
" -------------------\n",
" micro avg 77.23 77.23 77.23 8425\n",
" macro avg 82.01 76.68 76.56 8425\n",
" weighted avg 83.23 77.23 76.86 8425\n",
"\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "jUJb-9VLLBXo"
},
"source": [
"# 3. MS Marco\n",
"\n",
"## Task Description\n",
"\n",
"MS Marco NLGen is a dataset from Microsoft that takes extracted answers and questions and output fluent answers.\n",
"\n",
"An example is \n",
"\n",
"\n",
"* question: What county is Nine Mile in?\n",
"* extracted_answer: Onondaga\n",
"* fluent_answer: Nine Mile is in Onondaga county.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "VtXEKG_UQU9u"
},
"source": [
"## Download and unzip files"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "b9avsZ1CEq3K"
},
"outputs": [],
"source": [
"!mkdir ms_marco\n",
"os.chdir('ms_marco')\n",
"!wget https://msmarco.blob.core.windows.net/msmarco/train_v2.1.json.gz\n",
"!wget https://msmarco.blob.core.windows.net/msmarco/dev_v2.1.json.gz\n",
"\n",
"!gunzip train_v2.1.json.gz\n",
"!gunzip dev_v2.1.json.gz\n",
"\n",
"!python ../NeMo/examples/nlp/dialogue/remove_ms_marco_samples_without_wellFormedAnswers.py --filename train_v2.1.json \n",
"!python ../NeMo/examples/nlp/dialogue/remove_ms_marco_samples_without_wellFormedAnswers.py --filename dev_v2.1.json \n",
"\n",
"os.chdir('..')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "h7UZ9R8gQTFo"
},
"source": [
"## Training and/or Testing the model\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "fwGQCwbvRf2m"
},
"outputs": [],
"source": [
"# model.dataset.data_dir: folder to load data from\n",
"# model.dataset.dialogues_example_dir: folder that stores predictions for each sample\n",
"\n",
"!(python NeMo/examples/nlp/dialogue/dialogue.py \\\n",
" do_training=True \\\n",
" model.dataset.dialogues_example_dir='./marco_bart_predictions' \\\n",
" model.dataset.data_dir='./ms_marco' \\\n",
" model.save_model=True \\\n",
" model.dataset.debug_mode=True \\\n",
" model.dataset.task='ms_marco' \\\n",
" model.language_model.pretrained_model_name='facebook/bart-base' \\\n",
" trainer.max_epochs=1 \\\n",
" model.dataset.debug_mode=False \\\n",
" exp_manager.create_wandb_logger=False)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "UL7ekAOZ2abi"
},
"source": [
"**After 1 epoch:**\n",
"\n",
"Train more epochs for optimal performance\n",
"\n",
"```\n",
"────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
" Test metric DataLoader 0\n",
"────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
" bleu 65.46179962158203\n",
" f1 78.24439835896995\n",
" precision 81.92473076099847\n",
" recall 76.72508929408436\n",
" test_accuracy 25.563487607283225\n",
" test_loss 0.4419259166606655\n",
" test_loss_epoch 0.4420809745788574\n",
" test_ppl 1.5557004846779854\n",
"────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
"```"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"collapsed_sections": [],
"name": "Dialogue.ipynb",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.7"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
|