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"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", 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7, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Downloading: 0%| | 0.00/481 [00:00