start(draft).ipynb

{"cells":[{"cell_type":"markdown","source":["**Описание задания**\n","\n","Имеется: \n","\n","*  набор комментариев, некоторые из которых, содержат описания проектов. \n","*  небольшой размеченный набор текстов, в состав которого входит 8 текстов из положительного класса с меткой \"1\" и 12 текстов из класса - \"0\"\n","\n","Задача: создать семантическую поисковую систему, которая сможет из разных наборов текстов извлекать тексты с описанием проектов\n","\n"],"metadata":{"id":"jgtp4Xg2EWOI"},"id":"jgtp4Xg2EWOI"},{"cell_type":"markdown","source":["**Подход:  zero-shot**\n","\n","---\n","\n","Кратко суть подхода - используя значения множественной zero-shot классификации, создать некоторый аналог поискового индекса, который повзволил бы извлекать нужные тексты из набора\n","В качестве основы будут использоваться mодели SBERT, которые имеют встроенную zero-shot классификацию\n","А в качестве набора признаков будет использован некий список слов-категорий, близких к категории ПРОЕКТ\n","например, такой:  \n","`list_words = ['project', 'proposal', 'job', 'solution', 'research',  'idea', 'algorithm', 'plan', 'initiative', 'announcement']`\n","\n","\n"],"metadata":{"id":"jyyV2wjQBwK7"},"id":"jyyV2wjQBwK7"},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"O-BhWSEg3x-N","executionInfo":{"status":"ok","timestamp":1663964705239,"user_tz":-180,"elapsed":21242,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}},"outputId":"b58f6852-0f71-4549-dda9-784387fe2037"},"id":"O-BhWSEg3x-N","execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["!pip install sentence_transformers\n","!pip install flair\n","!pip install git+https://github.com/pmbaumgartner/setfit\n","!pip install git+https://github.com/LIAAD/yake\n","!pip install keybert\n","!pip install gensim"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UA0_wkIcJ3Sc","outputId":"2b55bb4e-8971-4a1e-c1d9-d5b2904e1280"},"id":"UA0_wkIcJ3Sc","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting sentence_transformers\n","  Downloading sentence-transformers-2.2.2.tar.gz (85 kB)\n","\u001b[K     |████████████████████████████████| 85 kB 2.7 MB/s \n","\u001b[?25hCollecting transformers<5.0.0,>=4.6.0\n","  Downloading transformers-4.22.1-py3-none-any.whl (4.9 MB)\n","\u001b[K     |████████████████████████████████| 4.9 MB 34.1 MB/s \n","\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from sentence_transformers) (4.64.1)\n","Requirement already satisfied: torch>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from sentence_transformers) (1.12.1+cu113)\n","Requirement already satisfied: 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requests\n","    Found existing installation: requests 2.23.0\n","    Uninstalling requests-2.23.0:\n","      Successfully uninstalled requests-2.23.0\n","  Attempting uninstall: importlib-metadata\n","    Found existing installation: importlib-metadata 4.12.0\n","    Uninstalling importlib-metadata-4.12.0:\n","      Successfully uninstalled importlib-metadata-4.12.0\n","  Attempting uninstall: sentencepiece\n","    Found existing installation: sentencepiece 0.1.97\n","    Uninstalling sentencepiece-0.1.97:\n","      Successfully uninstalled sentencepiece-0.1.97\n"]}]},{"cell_type":"code","source":["# !python -m spacy download en_core_web_sm"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MG-78QXWdyUE","executionInfo":{"status":"ok","timestamp":1661961584781,"user_tz":-180,"elapsed":22949,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}},"outputId":"a165925f-88c8-4cb9-c77b-199fddbb9ca6"},"id":"MG-78QXWdyUE","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting en-core-web-sm==3.4.0\n","  Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.4.0/en_core_web_sm-3.4.0-py3-none-any.whl (12.8 MB)\n","\u001b[K     |████████████████████████████████| 12.8 MB 8.2 MB/s \n","\u001b[?25hRequirement already satisfied: spacy<3.5.0,>=3.4.0 in /usr/local/lib/python3.7/dist-packages (from en-core-web-sm==3.4.0) (3.4.1)\n","Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.0) (57.4.0)\n","Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.0) (1.0.8)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.0) (21.3)\n","Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.0) (1.21.6)\n","Requirement already satisfied: srsly<3.0.0,>=2.4.3 in /usr/local/lib/python3.7/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.0) (2.4.4)\n","Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /usr/local/lib/python3.7/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.0) (2.0.8)\n","Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.0) (2.0.6)\n","Requirement already satisfied: typer<0.5.0,>=0.3.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.0) (0.4.2)\n","Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.0) (1.0.3)\n","Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.9 in /usr/local/lib/python3.7/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.0) (3.0.10)\n","Requirement already satisfied: typing-extensions<4.2.0,>=3.7.4 in /usr/local/lib/python3.7/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.0) (4.1.1)\n","Requirement already satisfied: thinc<8.2.0,>=8.1.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.0) (8.1.0)\n","Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.0) (4.64.0)\n","Requirement already satisfied: pathy>=0.3.5 in /usr/local/lib/python3.7/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.0) (0.6.2)\n","Requirement already satisfied: wasabi<1.1.0,>=0.9.1 in /usr/local/lib/python3.7/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.0) (0.10.1)\n","Requirement already satisfied: jinja2 in /usr/local/lib/python3.7/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.0) (2.11.3)\n","Requirement already satisfied: langcodes<4.0.0,>=3.2.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.0) (3.3.0)\n","Requirement already satisfied: pydantic!=1.8,!=1.8.1,<1.10.0,>=1.7.4 in /usr/local/lib/python3.7/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.0) (1.9.2)\n","Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.0) (2.28.1)\n","Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.0) (3.0.7)\n","Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from catalogue<2.1.0,>=2.0.6->spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.0) (3.8.1)\n","Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.0) (3.0.9)\n","Requirement already satisfied: smart-open<6.0.0,>=5.2.1 in /usr/local/lib/python3.7/dist-packages (from pathy>=0.3.5->spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.0) (5.2.1)\n","Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.0) (1.24.3)\n","Requirement already satisfied: charset-normalizer<3,>=2 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.0) (2.1.1)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.0) (2022.6.15)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.0) (2.10)\n","Requirement already satisfied: blis<0.8.0,>=0.7.8 in /usr/local/lib/python3.7/dist-packages (from thinc<8.2.0,>=8.1.0->spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.0) (0.7.8)\n","Requirement already satisfied: click<9.0.0,>=7.1.1 in /usr/local/lib/python3.7/dist-packages (from typer<0.5.0,>=0.3.0->spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.0) (7.1.2)\n","Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2->spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.0) (2.0.1)\n","\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n","You can now load the package via spacy.load('en_core_web_sm')\n"]}]},{"cell_type":"code","source":["from sklearn.metrics import accuracy_score, f1_score\n","from sklearn.linear_model import LogisticRegression\n","from sentence_transformers import SentenceTransformer, InputExample, losses, models, datasets, evaluation\n","from torch.utils.data import DataLoader\n","\n","from sklearn.manifold import TSNE\n","from matplotlib import pyplot as plt\n","\n","# import warnings filter\n","from warnings import simplefilter\n","# ignore all future warnings\n","simplefilter(action='ignore', category=FutureWarning)\n","\n","import torch\n","import random\n","\n","import json\n","from pandas import Series, DataFrame\n","import pandas as pd\n","from bs4 import BeautifulSoup\n","from tqdm.notebook import tqdm\n","import numpy as np\n","import gzip\n","\n","from transformers import pipeline\n","# classifier = pipeline(\"zero-shot-classification\", model=\"facebook/xglm-564M\")\n","import gensim.downloader\n","from setfit import SetFitClassifier\n","from random import choice, randint, shuffle\n","\n","def set_seed(seed):\n","  random.seed(seed)\n","  np.random.seed(seed)\n","  torch.manual_seed(seed)\n","\n","tqdm.pandas()\n","\n","list_words = ['project', 'proposal', 'job', 'solution', 'research',  'idea', 'algorithm', 'plan', 'initiative', 'announcement']"],"metadata":{"id":"iXDpMwBjVkRG"},"id":"iXDpMwBjVkRG","execution_count":null,"outputs":[]},{"cell_type":"code","source":["import sqlite3 as db\n","conn = db.connect('/content/drive/MyDrive/WORK/data/labels.db')\n","train_df = pd.read_sql_query(\"SELECT text,label FROM labels\", conn)\n","# train_df['label'] = np.array(['', 'project proposal'])[train_df['label'].values]\n","train_df.sort_values(\"label\", ascending=False).head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":225},"id":"U_WQJ-HbD11w","executionInfo":{"status":"ok","timestamp":1662126638285,"user_tz":-180,"elapsed":755,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}},"outputId":"1dd67236-8698-49ba-d40f-a77adb8ed374"},"id":"U_WQJ-HbD11w","execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                                 text  label\n","0   The suggested EA project / org: work to identi...      1\n","18  As a concrete program related to the safety of...      1\n","43  One possible area for exploration is around Sc...      1\n","40  I propose an infrastructure to generate more a...      1\n","58  Hi Vaidehi,What kind of research do you plan o...      1"],"text/html":["\n","  <div id=\"df-e039c0ee-caa1-476a-aa10-547370f358a4\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>text</th>\n","      <th>label</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>The suggested EA project / org: work to identi...</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>As a concrete program related to the safety of...</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>43</th>\n","      <td>One possible area for exploration is around Sc...</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>40</th>\n","      <td>I propose an infrastructure to generate more a...</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>58</th>\n","      <td>Hi Vaidehi,What kind of research do you plan o...</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e039c0ee-caa1-476a-aa10-547370f358a4')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg 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dataset] \n","labels = [i[1] for i in dataset]"],"metadata":{"id":"Rczk5PH2d_Uc"},"id":"Rczk5PH2d_Uc","execution_count":null,"outputs":[]},{"cell_type":"code","source":["clf = SetFitClassifier('paraphrase-mpnet-base-v2')\n","clf.fit(docs, labels) \n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":87,"referenced_widgets":["76926b4d7c2e4daeb286a67f82c9d01d","bc1324d6fc904279bda69e42bed43e5a","6a3af5fbb53047a39f179c85c8b4cdd2","f7da0c59d1b541a580d10e6738d67c72","6f9315676f2d4f58a4935441fb795c00","b423dfea4f234b8db93b8df03172de1b","dd0a0241d5684068bae6b6a045ac50e8","952f7a439e04484697e9057c1b5af75c","e8c2bcc455f948faa0e9a8a6a149b490","33e0fdc69e754b82bf69119d5155ade6","2954388fdff84e6eadbbc965265b786f","396fcdf50b4b4e1ab516e83cfec983b0","7170d32e072f4fbe9f5e42bdf5e8ca2f","acf33adb792641869842b0d51d270f8c","a5d0bd7b377e4d39897d9b9c86911358","026c46d49e444a92b3d0559eeb2ae532","fc8c2d9c802042a7ac15141e8a7c43ce","9d671ce884b447248383098d4b744649","ea85f141c9cd41be9010e92c5ee2d6fd","e23382b23b0040bebd351b3024863b5d","ad19715327f4417b8371656e621b6c74","b8eed37485e24c69953ddfd662ab8107"]},"id":"DKwRCvJNZKM-","executionInfo":{"status":"ok","timestamp":1662129370675,"user_tz":-180,"elapsed":765209,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}},"outputId":"a62f3aad-6dc7-4224-f187-0b04ca1af7c4"},"id":"DKwRCvJNZKM-","execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Epoch:   0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"76926b4d7c2e4daeb286a67f82c9d01d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Iteration:   0%|          | 0/13 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"396fcdf50b4b4e1ab516e83cfec983b0"}},"metadata":{}}]},{"cell_type":"code","source":["shuffle(dataset)\n","docs_eval = [i[0] for i in dataset] \n","labels_eval = [i[1] for i in dataset]\n"],"metadata":{"id":"pjbpRPGZIKxO"},"id":"pjbpRPGZIKxO","execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(labels_eval)\n","y_pred_eval = clf.predict(docs_eval)\n","print(y_pred_eval)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Eb1k-D8MIK0X","executionInfo":{"status":"ok","timestamp":1662129400576,"user_tz":-180,"elapsed":12612,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}},"outputId":"c0d62666-1544-443c-e1ba-ca90519bfe6d"},"id":"Eb1k-D8MIK0X","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1]\n","[1 0 0 0 1 0 1 1 1 0 0 0 1 0 0 1 0 0 0 1]\n"]}]},{"cell_type":"code","source":["accuracy_score(labels_eval, y_pred_eval)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jXVEH6wdIK30","executionInfo":{"status":"ok","timestamp":1662129400577,"user_tz":-180,"elapsed":11,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}},"outputId":"d36c70d7-b660-4820-b2e9-f505a3562c40"},"id":"jXVEH6wdIK30","execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1.0"]},"metadata":{},"execution_count":41}]},{"cell_type":"code","source":["clf.save('/content/drive/MyDrive/WORK/models/setfit_20.mdl')"],"metadata":{"id":"vyVLJHCpILDU"},"id":"vyVLJHCpILDU","execution_count":null,"outputs":[]},{"cell_type":"code","source":["clf_test = SetFitClassifier.load('/content/drive/MyDrive/WORK/models/setfit_20.mdl')"],"metadata":{"id":"p4l4Of-urnqv"},"id":"p4l4Of-urnqv","execution_count":null,"outputs":[]},{"cell_type":"code","source":["clf_test.predict(docs_eval)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4mDvYmQSsPW_","executionInfo":{"status":"ok","timestamp":1662130525150,"user_tz":-180,"elapsed":12868,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}},"outputId":"6714da40-bbf9-4677-c5e2-401927cee617"},"id":"4mDvYmQSsPW_","execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1])"]},"metadata":{},"execution_count":47}]},{"cell_type":"code","source":["import spacy\n","# nlp = spacy.load(\"en_core_web_sm\")"],"metadata":{"id":"SFSLIioKdexs"},"id":"SFSLIioKdexs","execution_count":null,"outputs":[]},{"cell_type":"code","source":["# glove_vectors = gensim.downloader.load('glove-wiki-gigaword-300')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"d3O-fIJxSQJd","executionInfo":{"status":"ok","timestamp":1661961802573,"user_tz":-180,"elapsed":202571,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}},"outputId":"6a4359f7-2322-4c9e-db8d-9b48ad70e640"},"id":"d3O-fIJxSQJd","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[==================================================] 100.0% 376.1/376.1MB downloaded\n"]}]},{"cell_type":"code","source":["level_1 = glove_vectors.most_similar('project', topn=20)\n","level_1"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"LmV1ZJYiJo2C","executionInfo":{"status":"ok","timestamp":1661884942588,"user_tz":-180,"elapsed":590,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}},"outputId":"cc845c97-e03f-4116-ea40-ba99fb86b628"},"id":"LmV1ZJYiJo2C","execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[('projects', 0.8065688610076904),\n"," ('development', 0.6217209696769714),\n"," ('construction', 0.5998873114585876),\n"," ('build', 0.5511942505836487),\n"," ('program', 0.5421556234359741),\n"," ('plans', 0.5386307835578918),\n"," ('plan', 0.5361279845237732),\n"," ('planned', 0.5305522084236145),\n"," ('renovation', 0.5172025561332703),\n"," ('initiative', 0.516783595085144),\n"," ('completed', 0.5117099285125732),\n"," ('work', 0.5102672576904297),\n"," ('ambitious', 0.5065388083457947),\n"," ('funded', 0.5033539533615112),\n"," ('funding', 0.494592547416687),\n"," ('completion', 0.49187761545181274),\n"," ('planning', 0.4888250231742859),\n"," ('proposed', 0.4873683750629425),\n"," ('research', 0.4872983396053314),\n"," ('design', 0.4817836582660675)]"]},"metadata":{},"execution_count":7}]},{"cell_type":"code","source":["root_word = 'project'\n","level_2 = {w[0]: glove_vectors.most_similar(w[0], topn=20) for w in level_1}\n","level_3 = dict()\n","set_level_2 = set()\n","for k, v in level_2.items():\n","  if k not in level_3:\n","    level_3[k] = []\n","  for w in v:\n","    if glove_vectors.similarity(root_word, w[0]) > 0.3:\n","      level_3[k].append(w[0])\n","      set_level_2.add(w[0])\n","\n","for k, v in level_3.items():\n","  print(k, len(v))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"BzGpypx1K9Ro","executionInfo":{"status":"ok","timestamp":1661884944250,"user_tz":-180,"elapsed":1668,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}},"outputId":"15ff8748-1d32-4a65-9abc-0f9f40865920"},"id":"BzGpypx1K9Ro","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["projects 20\n","development 17\n","construction 17\n","build 17\n","program 18\n","plans 15\n","plan 17\n","planned 16\n","renovation 12\n","initiative 19\n","completed 13\n","work 17\n","ambitious 15\n","funded 17\n","funding 17\n","completion 13\n","planning 11\n","proposed 8\n","research 12\n","design 10\n"]}]},{"cell_type":"code","source":["print(len(set_level_2))\n","doc = nlp(' '.join(set_level_2))\n","set_l_2 = set()\n","for w in set_level_2:\n","  for token in nlp(w):\n","    if token.tag_ in ['VB', 'NN', 'NNS']:\n","      set_l_2.add(token.lemma_)\n","list_labels = list(set_level_2)\n","print(len(set_l_2))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Btw3RSTkZjN7","executionInfo":{"status":"ok","timestamp":1661884944252,"user_tz":-180,"elapsed":37,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}},"outputId":"cf2e2b93-2cc3-4a39-d0e7-c85fcdd09218"},"id":"Btw3RSTkZjN7","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["167\n","88\n"]}]},{"cell_type":"code","source":["l_w = glove_vectors.most_similar('project', topn=20)\n","l_1 = ['project', 'news'] + [i[0] for i in l_w]\n","len(l_1), len(set(l_1))\n","label_coef = {i[0]:i[1] for i in l_w}\n","label_coef['news'] = glove_vectors.similarity('project', 'news')"],"metadata":{"id":"rHESHxU8gCxx"},"id":"rHESHxU8gCxx","execution_count":null,"outputs":[]},{"cell_type":"code","source":["label_coef"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8SeG0VEdH3kg","executionInfo":{"status":"ok","timestamp":1661961803396,"user_tz":-180,"elapsed":33,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}},"outputId":"e997706d-b02c-47b6-c14a-618c03b2126d"},"id":"8SeG0VEdH3kg","execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'projects': 0.8065688610076904,\n"," 'development': 0.6217209696769714,\n"," 'construction': 0.5998873114585876,\n"," 'build': 0.5511942505836487,\n"," 'program': 0.5421556234359741,\n"," 'plans': 0.5386307835578918,\n"," 'plan': 0.5361279845237732,\n"," 'planned': 0.5305522084236145,\n"," 'renovation': 0.5172025561332703,\n"," 'initiative': 0.516783595085144,\n"," 'completed': 0.5117099285125732,\n"," 'work': 0.5102672576904297,\n"," 'ambitious': 0.5065388083457947,\n"," 'funded': 0.5033539533615112,\n"," 'funding': 0.494592547416687,\n"," 'completion': 0.49187761545181274,\n"," 'planning': 0.4888250231742859,\n"," 'proposed': 0.4873683750629425,\n"," 'research': 0.4872983396053314,\n"," 'design': 0.4817836582660675,\n"," 'news': 0.16294658}"]},"metadata":{},"execution_count":8}]},{"cell_type":"code","source":["root_word = 'project'\n","label_coef_m = dict()\n","set_l_1 = set()\n","for w in l_1:\n","  for token in nlp(w):\n","    if token.tag_ in ['VB', 'NN', 'NNS']:\n","      set_l_1.add(token.lemma_)\n","      if token.text == root_word:\n","        continue\n","      if token.lemma_ in label_coef_m:\n","        continue\n","      label_coef_m[token.lemma_] = label_coef[token.text]\n","list_labels_1 = list(set_l_1)\n","len(list_labels_1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7VMsyvnoJpMw","executionInfo":{"status":"ok","timestamp":1661961803398,"user_tz":-180,"elapsed":29,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}},"outputId":"a950b941-7c0d-47fe-da50-79019ab537ce"},"id":"7VMsyvnoJpMw","execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["14"]},"metadata":{},"execution_count":9}]},{"cell_type":"code","source":["label_coef_m"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qHQEIxBEOlNj","executionInfo":{"status":"ok","timestamp":1661961803399,"user_tz":-180,"elapsed":20,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}},"outputId":"fdf40292-3203-4635-e4b9-a7a80a68e807"},"id":"qHQEIxBEOlNj","execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'news': 0.16294658,\n"," 'project': 0.8065688610076904,\n"," 'development': 0.6217209696769714,\n"," 'construction': 0.5998873114585876,\n"," 'build': 0.5511942505836487,\n"," 'program': 0.5421556234359741,\n"," 'plan': 0.5386307835578918,\n"," 'renovation': 0.5172025561332703,\n"," 'initiative': 0.516783595085144,\n"," 'work': 0.5102672576904297,\n"," 'funding': 0.494592547416687,\n"," 'completion': 0.49187761545181274,\n"," 'research': 0.4872983396053314,\n"," 'design': 0.4817836582660675}"]},"metadata":{},"execution_count":10}]},{"cell_type":"code","source":["# from transformers import pipeline\n","classifier = pipeline(\"zero-shot-classification\", model=\"facebook/bart-large-mnli\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":226,"referenced_widgets":["19f70745cb0447458308fcd10c710e05","b74a020131df41b6b2509aeafc6e9693","2a3f8b4423d84071a9cdeac3c68fad0e","45a13b71c82c4b4b81564b9633171216","f58e79ee091441e38167eeae48168817","1bc550a36791457590317383c991ac83","05c48e39834d4c67afda2d3de21a052b","4528e67bddd8422aa96124f2f8ffd6f9","9f896b83a7f24619b38ee7b73896bf70","5119ed613b7641f9844c641be3c80771","6c153c1a7e4b44cebc2e095ef9ff02af","501915c1cdb9493dae974ad7f2b9092e","055eedd8449e4d029f3158e68bded602","7e763ef7c22b43779c524a4d8bd94005","0a42159c12e54716bfaeb68205e42604","cadf5f8eadfb408e96fa1323b80c97dd","306c15dc7c314b53814260723719199c","bd6fb1e8464d4753bd658e12ec2e3517","3b6d91a6ef914f7b973741ef7f12bb60","6d2cafe9578546cfa5664ada44c348ec","19b3b5dd37ba41cf844e978aebc101ed","81ccf0602f4a4063ac74a211c6cf6849","8341a2a09a9042d7a47d527379866425","8b2093f621c4402c956441172c985f11","547f84d2687349e5b51193ac8f667303","19325090258741fc8cf22dc0cd5de246","0c742b5d109d42598ca60e3cb5d8d177","4182a3e0f9874a909c9e32c68b131591","cb7fcda8115a4fa29e602473f0bd4d58","f3693f8ccbe3477d90b24d24de2e3de2","ab998f7f5567444f8b7ed587c29cd2e0","dc3b50170b2a4a83b87dad470ecba5a0","f78729d9d46d4b719552c26dec9b41e1","327cef308a5d4e188254683da35bb85e","4619d26262d24e70bc379f0ccf3c6ba5","1262778677d540de913cabaf1afe0516","fd1965cb31f94112ba88adcbf83f09ba","f7ca28cbb48748658ba2d617cf088d12","59c06f6d70744b4da8db9e2a2d39a1d9","2dccaa177e924ceaaf10fa4adb7a9cd7","e435cb4aacb04323a2aad0dd285c0ab3","22a4356862e94ad188e16232801b2fc9","3613349beb504c50a761f6b79e3f9abd","d69da11c76e14839afbe6d8622746e66","cb424113f2694b5db42774e000e08b0b","c87eb5daf500498796c46140c77647b4","bab4de85aa4946bbb83ca46445df1ec3","205f3dbe68d84bac8686d081baab8622","0f94bd0391ab4f58a6b01f514d61d8d4","56b73e2900f547fbbcefd037bf8d1358","b227f58fe807471ebb0a37d0221e8166","acf2a0e6014a4aaeb9df3f24d16bd4b5","bd57ae7b61ad42ff96a6deac777ba538","d98c9e1a1527474d87fb88608511d46a","d9d1d239753f457c84aa5b4f15fd5ee7","f97b8c94f8f24769a790d0d9aa8142e9","298c0886b0cc41cf94210456a03ea296","eff0bd820d4a4c728b213c3c3eb3663f","43157a6823d542cfbd78c4750085ed2b","39e5444114e4454c8fd00d8590c058b1","559779733bfa4bfb89cd428266029a82","a5d7544d7a4e45cba0580b9f4de81b10","4acf5ca17d994f06a8ee3e54bab67143","7d9fa0ec735f4560be65ccaf27aeb68d","8318e9248f9d43c687c34b77c6eb9fb5","af6584bd6a00404e95420db83ea5e408"]},"id":"_dnNF6GkE4WM","executionInfo":{"status":"ok","timestamp":1661961861866,"user_tz":-180,"elapsed":58477,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}},"outputId":"cb5995f3-adbe-4cf4-d885-fb8c41b045c7"},"id":"_dnNF6GkE4WM","execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading config.json:   0%|          | 0.00/1.13k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"19f70745cb0447458308fcd10c710e05"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading pytorch_model.bin:   0%|          | 0.00/1.52G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"501915c1cdb9493dae974ad7f2b9092e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading tokenizer_config.json:   0%|          | 0.00/26.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8341a2a09a9042d7a47d527379866425"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading vocab.json:   0%|          | 0.00/878k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"327cef308a5d4e188254683da35bb85e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading merges.txt:   0%|          | 0.00/446k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"cb424113f2694b5db42774e000e08b0b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading tokenizer.json:   0%|          | 0.00/1.29M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f97b8c94f8f24769a790d0d9aa8142e9"}},"metadata":{}}]},{"cell_type":"code","source":["def calculate_value_labels(r):\n","  top_label = 'project'\n","  labels = r['labels']\n","  scores = r['scores']\n","  indx = labels.index(top_label)\n","  if indx:\n","    if (scores[0] - scores[indx]) > 0.2:\n","          return 0\n","  return sum(scores)\n","\n","def calc_using_coef(r):\n","  sum = 0\n","  top_label = 'project'\n","  labels = r['labels']\n","  scores = r['scores']\n","  for num, name in enumerate(labels):\n","    if name == top_label:\n","       sum += scores[num]\n","       rel = scores[num]\n","       continue\n","    sum += label_coef_m[name] * scores[num]\n","  return sum, rel/sum, rel"],"metadata":{"id":"od-0I4iyFLxb"},"id":"od-0I4iyFLxb","execution_count":null,"outputs":[]},{"cell_type":"code","source":["classifier('The suggested EA project / org: work to identify important, simple, and uncontroversial ideas as they arise and use social media to get people to pay attention to them.', ['research',  'idea']) #, multi_label=True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9Ennp82csch6","executionInfo":{"status":"ok","timestamp":1661962868417,"user_tz":-180,"elapsed":2010,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}},"outputId":"978bf548-a1c7-4316-a7ff-b646ddc96dfe"},"id":"9Ennp82csch6","execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'sequence': 'The suggested EA project / org: work to identify important, simple, and uncontroversial ideas as they arise and use social media to get people to pay attention to them.',\n"," 'labels': ['idea', 'research'],\n"," 'scores': [0.7449327111244202, 0.25506722927093506]}"]},"metadata":{},"execution_count":22}]},{"cell_type":"code","source":["results = []\n","res_s = []\n","for index, row in list(train_df.iterrows())[:10]:\n","  res = classifier(row['text'], list_labels_1, multi_label=True)\n","  res_s.append(res)\n","  val = calculate_value_labels(res)\n","  sss, rel, r_p = calc_using_coef(res)\n","  results.append((index, val, row['label']))\n","  print('index=', index, 'val=', val, r_p/val, 'sum_coef=', sss, 'rel=', rel, 'project=', r_p, 'label=', row['label'] )\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"q5qRalVZEMhG","executionInfo":{"status":"ok","timestamp":1661961958447,"user_tz":-180,"elapsed":93865,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}},"outputId":"7620e678-042a-45fd-ca11-2161f23b547d"},"id":"q5qRalVZEMhG","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["index= 0 val= 5.446270535700023 0.14691393757310378 sum_coef= 3.2152286318409753 rel= 0.24885727925044246 project= 0.8001330494880676 label= 1\n","index= 1 val= 7.479547753930092 0.11908104807868858 sum_coef= 4.170614848129502 rel= 0.2135590118306077 project= 0.8906723856925964 label= 0\n","index= 2 val= 4.8902445925050415 0.18778206162469147 sum_coef= 2.9628599594858973 rel= 0.30993709590949936 project= 0.918300211429596 label= 0\n","index= 3 val= 5.2701094606891274 0.13796402829607057 sum_coef= 3.0445451758457995 rel= 0.2388158127940781 project= 0.727085530757904 label= 0\n","index= 4 val= 6.4645152115263045 0.14646929445783813 sum_coef= 3.636164193742058 rel= 0.26039885208533237 project= 0.94685298204422 label= 0\n","index= 5 val= 7.609414666891098 0.10680170802140529 sum_coef= 4.061007443135431 rel= 0.2001223821544223 project= 0.812698483467102 label= 0\n","index= 6 val= 6.157619446516037 0.1286235635952501 sum_coef= 3.434288513645493 rel= 0.23061980766245563 project= 0.7920149564743042 label= 0\n","index= 7 val= 3.9502616450190544 0.12518454926698563 sum_coef= 2.215154387729649 rel= 0.22324029704548287 project= 0.4945117235183716 label= 0\n","index= 8 val= 4.422310492023826 0.18247110755521093 sum_coef= 2.700127346816702 rel= 0.29885401308347864 project= 0.8069438934326172 label= 0\n","index= 9 val= 3.2340300753712654 0.1493757942503658 sum_coef= 1.8837151045740947 rel= 0.2564537545858762 project= 0.4830858111381531 label= 0\n"]}]},{"cell_type":"code","source":["res_s[0]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sjTdVqhzEMtv","executionInfo":{"status":"ok","timestamp":1661963039337,"user_tz":-180,"elapsed":1548,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}},"outputId":"95d27a1a-732b-4254-d293-df46219e1dd7"},"id":"sjTdVqhzEMtv","execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'sequence': 'The suggested EA project / org: work to identify important, simple, and uncontroversial ideas as they arise and use social media to get people to pay attention to them.',\n"," 'labels': ['work',\n","  'project',\n","  'plan',\n","  'program',\n","  'research',\n","  'initiative',\n","  'design',\n","  'development',\n","  'construction',\n","  'build',\n","  'renovation',\n","  'news',\n","  'funding',\n","  'completion'],\n"," 'scores': [0.8951610326766968,\n","  0.8001330494880676,\n","  0.7185404896736145,\n","  0.5206565856933594,\n","  0.49306896328926086,\n","  0.47426173090934753,\n","  0.4259122610092163,\n","  0.3591938614845276,\n","  0.2263416200876236,\n","  0.20287057757377625,\n","  0.1823922097682953,\n","  0.12008605152368546,\n","  0.013871738687157631,\n","  0.013780363835394382]}"]},"metadata":{},"execution_count":24}]},{"cell_type":"code","source":["res_s[1]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"S-e4E-ieEMxe","executionInfo":{"status":"ok","timestamp":1661962023909,"user_tz":-180,"elapsed":441,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}},"outputId":"f7aba930-51b9-4250-9ac9-5ca8557fc470"},"id":"S-e4E-ieEMxe","execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'sequence': '4) Between mentors, high-value sponsors, and proposer, they submit some manner of updates on the progress of the project on the forum every Z months.',\n"," 'labels': ['project',\n","  'work',\n","  'program',\n","  'plan',\n","  'news',\n","  'development',\n","  'funding',\n","  'initiative',\n","  'design',\n","  'construction',\n","  'renovation',\n","  'build',\n","  'research',\n","  'completion'],\n"," 'scores': [0.8906723856925964,\n","  0.7777547836303711,\n","  0.7101582288742065,\n","  0.6805189251899719,\n","  0.6608580350875854,\n","  0.6584587693214417,\n","  0.5815821290016174,\n","  0.5333424210548401,\n","  0.46154576539993286,\n","  0.45102280378341675,\n","  0.43263256549835205,\n","  0.3419344127178192,\n","  0.23566758632659912,\n","  0.06339894235134125]}"]},"metadata":{},"execution_count":16}]},{"cell_type":"markdown","id":"d9b35af4-e1b8-42c1-9b96-18cac3f34ee7","metadata":{"id":"d9b35af4-e1b8-42c1-9b96-18cac3f34ee7"},"source":["## Load comments"]},{"cell_type":"code","execution_count":null,"id":"aae1b4cb-6a9c-4998-8b17-28116ae6df05","metadata":{"id":"aae1b4cb-6a9c-4998-8b17-28116ae6df05"},"outputs":[],"source":["import re\n","def split_html_paragraphs(html:str):\n","    # return [s for s in re.split(r\"</?p>|<br/?>\", html) if len(s.strip()) > 0]\n","    return [s for s in re.split(r\"<br/?>\", html) if len(s.strip()) > 0]\n","\n"]},{"cell_type":"code","source":["from pathlib import Path\n","pref = Path('/content/drive/MyDrive/WORK/data') "],"metadata":{"id":"0BqDPXgqR_fr"},"id":"0BqDPXgqR_fr","execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"id":"ddb12069-8ca3-4d01-ae00-c0c38a1e3e60","metadata":{"id":"ddb12069-8ca3-4d01-ae00-c0c38a1e3e60"},"outputs":[],"source":["file_json_gz = Path.joinpath(pref, 'comments.json.gz')\n","\n","with gzip.open(file_json_gz, 'r') as fin:        \n","    json_bytes = fin.read()                      \n","\n","json_str = json_bytes.decode('utf-8')            \n","comment_dict = json.loads(json_str)                      \n","\n","\n","comment_dict = [c for c in comment_dict if (c['pageUrl'] is not None) and (c['htmlBody'] is not None)]\n","\n","for c in comment_dict:\n","    if \"?commentId=\" in c['pageUrl']:\n","        c['commentId'] = c['pageUrl'].split(\"?commentId=\")[1]\n","    else:\n","        c['commentId'] = \"discussion#\" + c['pageUrl'].split(\"discussion#\")[1]\n","\n","comment_info = DataFrame(comment_dict).set_index(\"commentId\")\n","comment_info = comment_info[~comment_info.index.duplicated()]\n","# comment_htmls = Series({c['commentId']: c['htmlBody'] for c in comment_dict})\n","\n","# comment_texts = comment_info.htmlBody.progress_map(lambda h: BeautifulSoup(h, features=\"html.parser\").get_text())\n","# comment_texts = comment_texts.map(str.strip)\n","# comment_texts = comment_texts[comment_texts.map(len) > 50]\n","# comment_texts.to_csv(\"./data/comment_texts.csv\")\n","\n","# comment_texts = pd.read_csv(\"./data/comment_texts.csv\").set_index(\"text\").postId\n","# comment_texts.size"]},{"cell_type":"code","source":["comment_info_split"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":179},"id":"5ZtpbVHATW_5","executionInfo":{"status":"error","timestamp":1661934311279,"user_tz":-180,"elapsed":545,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}},"outputId":"b237f4ac-40e6-499a-bbfb-a4dcbe8334b5"},"id":"5ZtpbVHATW_5","execution_count":null,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-14-bff6a468d6af>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcomment_info_split\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mNameError\u001b[0m: name 'comment_info_split' is not defined"]}]},{"cell_type":"code","source":["h = comment_dict[0]['htmlBody']\n","BeautifulSoup(h, features=\"html.parser\").get_text().replace('\\xa0', ' ')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":57},"id":"A1aprOXKxGhS","executionInfo":{"status":"ok","timestamp":1661934334685,"user_tz":-180,"elapsed":278,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}},"outputId":"05a7e355-fc32-4b51-e88f-70863e262527"},"id":"A1aprOXKxGhS","execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["\"You don't have to be smart, or a college graduate. What I'm attempting to do at least for my edification is breaking things down into manageable chunks, so that it can easily be explained. I also enjoy looking at some of the EA numbers and shaking my head.The table is small, but that's because we need people to help us build more extensions to the table. Come get some tools and help me make an addition.\""],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":15}]},{"cell_type":"code","execution_count":null,"id":"ad57f5f3-e48d-483a-812e-f0246971b45d","metadata":{"id":"ad57f5f3-e48d-483a-812e-f0246971b45d"},"outputs":[],"source":["html_split = comment_info.htmlBody.map(split_html_paragraphs)\n","comment_htmls_split = pd.concat(\n","    [comment_info.htmlBody[html_split.map(len) == 1]] + \n","    [Series(ts, index=[f'{cid}--{i}' for i in range(len(ts))]) for cid,ts in html_split[html_split.map(len) > 1].items()]\n",")"]},{"cell_type":"code","execution_count":null,"id":"1257d8bf-c698-432a-b599-a6de0aa6e971","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["7f20208d24db46699e07a1a7f96eca31","28779867590a4c82ae1ecf9e2d3a57ee","360a1d72293e4613838c9e64c37e12fb","ea295da8707c43bcbc6e91712a108f90","363b367761c1447a8be99b6120ba8236","20f8e51fed2841f5adcaa229fbbae8aa","811c656ec2fc4ab58256029726661481","c71de1a214d2416b8c1cb713dfb46ac7","d2064a1c8f98483d9d257a4bd070a3b6","f4a0d2d83d2444a195ba0f5bc4820e63","05a636ed52854b128c9e288fdec5dbc5"]},"id":"1257d8bf-c698-432a-b599-a6de0aa6e971","executionInfo":{"status":"ok","timestamp":1661934367504,"user_tz":-180,"elapsed":28332,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}},"outputId":"cc1f5ce0-f4b4-47d0-c764-71a2c1746830"},"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/106839 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7f20208d24db46699e07a1a7f96eca31"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/bs4/__init__.py:273: UserWarning: \"b'.'\" looks like a filename, not markup. You should probably open this file and pass the filehandle into Beautiful Soup.\n","  ' Beautiful Soup.' % markup)\n","/usr/local/lib/python3.7/dist-packages/bs4/__init__.py:336: UserWarning: \"http://climate.envsci.rutgers.edu/robock/talks/NuclearWinter109Lamont.pptx\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n","  ' that document to Beautiful Soup.' % decoded_markup\n","/usr/local/lib/python3.7/dist-packages/bs4/__init__.py:336: UserWarning: \"https://carnegieendowment.org/2018/04/17/new-killer-pathogens-countering-coming-bioweapons-threat-pub-76009\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n","  ' that document to Beautiful Soup.' % decoded_markup\n","/usr/local/lib/python3.7/dist-packages/bs4/__init__.py:336: UserWarning: \"https://www.washingtonpost.com/news/checkpoint/wp/2016/09/02/why-the-last-u-s-company-making-cluster-bombs-wont-produce-them-anymore/\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n","  ' that document to Beautiful Soup.' % decoded_markup\n","/usr/local/lib/python3.7/dist-packages/bs4/__init__.py:336: UserWarning: \"https://slatestarcodex.com/2020/03/23/face-masks-much-more-than-you-wanted-to-know/\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n","  ' that document to Beautiful Soup.' % decoded_markup\n","/usr/local/lib/python3.7/dist-packages/bs4/__init__.py:336: UserWarning: \"https://en.wikipedia.org/wiki/Face_masks_during_the_COVID-19_pandemic_in_the_United_States\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n","  ' that document to Beautiful Soup.' % decoded_markup\n","/usr/local/lib/python3.7/dist-packages/bs4/__init__.py:336: UserWarning: \"https://goodjudgment.com/about/the-science-of-superforecasting/&nbsp;&nbsp;\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n","  ' that document to Beautiful Soup.' % decoded_markup\n","/usr/local/lib/python3.7/dist-packages/bs4/__init__.py:336: UserWarning: \"https://www.foreignaffairs.com/articles/united-states/2020-10-13/better-crystal-ball&nbsp;\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n","  ' that document to Beautiful Soup.' % decoded_markup\n","/usr/local/lib/python3.7/dist-packages/bs4/__init__.py:336: UserWarning: \"https://warontherocks.com/2021/07/did-sino-american-relations-have-to-deteriorate-a-better-way-of-doing-counterfactual-thought-experiments/\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n","  ' that document to Beautiful Soup.' % decoded_markup\n","/usr/local/lib/python3.7/dist-packages/bs4/__init__.py:336: UserWarning: \"https://www.techtotherescue.org/case-studies\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n","  ' that document to Beautiful Soup.' % decoded_markup\n","/usr/local/lib/python3.7/dist-packages/bs4/__init__.py:336: UserWarning: \"https://www.chatrandom.one/\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n","  ' that document to Beautiful Soup.' % decoded_markup\n","/usr/local/lib/python3.7/dist-packages/bs4/__init__.py:336: UserWarning: \"https://www.bazoocam.fyi/\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n","  ' that document to Beautiful Soup.' % decoded_markup\n","/usr/local/lib/python3.7/dist-packages/bs4/__init__.py:336: UserWarning: \"https://www.mattball.org/2021/12/ethics-is-not-simple-math-problem.html\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n","  ' that document to Beautiful Soup.' % decoded_markup\n","/usr/local/lib/python3.7/dist-packages/bs4/__init__.py:336: UserWarning: \"https://www.lazard.com/perspective/levelized-cost-of-energy-levelized-cost-of-storage-and-levelized-cost-of-hydrogen/\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n","  ' that document to Beautiful Soup.' % decoded_markup\n","/usr/local/lib/python3.7/dist-packages/bs4/__init__.py:336: UserWarning: \"https://en.wikipedia.org/wiki/House_of_Saud\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n","  ' that document to Beautiful Soup.' % decoded_markup\n","/usr/local/lib/python3.7/dist-packages/bs4/__init__.py:336: UserWarning: \"https://founders-pledge.jobs.personio.de/job/378212\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n","  ' that document to Beautiful Soup.' % decoded_markup\n","/usr/local/lib/python3.7/dist-packages/bs4/__init__.py:336: UserWarning: \"https://twitter.com/pmddomingos/status/1346940377840848898\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n","  ' that document to Beautiful Soup.' % decoded_markup\n","/usr/local/lib/python3.7/dist-packages/bs4/__init__.py:336: UserWarning: \"https://perceptiongap.us/\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n","  ' that document to Beautiful Soup.' % decoded_markup\n","/usr/local/lib/python3.7/dist-packages/bs4/__init__.py:336: UserWarning: \"https://www.nationalgeographic.com/culture/2018/09/south-asia-america-motels-immigration/\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n","  ' that document to Beautiful Soup.' % decoded_markup\n","/usr/local/lib/python3.7/dist-packages/bs4/__init__.py:336: UserWarning: \"https://opendocs.ids.ac.uk/opendocs/handle/123456789/14653\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n","  ' that document to Beautiful Soup.' % decoded_markup\n","/usr/local/lib/python3.7/dist-packages/bs4/__init__.py:336: UserWarning: \"https://www.forbes.com/sites/amorylovins/2019/11/18/does-nuclear-power-slow-or-speed-climate-change/#3873b5506b40\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n","  ' that document to Beautiful Soup.' % decoded_markup\n","/usr/local/lib/python3.7/dist-packages/bs4/__init__.py:336: UserWarning: \"https://climatenewsnetwork.net/nuclear-power-cannot-rival-renewable-energy/\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n","  ' that document to Beautiful Soup.' % decoded_markup\n","/usr/local/lib/python3.7/dist-packages/bs4/__init__.py:336: UserWarning: \"https://amp.reddit.com/r/TabForACause/comments/ewsx5p/2020_tab_for_a_cause_charity_spotlight_nomination/\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n","  ' that document to Beautiful Soup.' % decoded_markup\n","/usr/local/lib/python3.7/dist-packages/bs4/__init__.py:336: UserWarning: \"https://gladly.zendesk.com/hc/en-us/articles/205954618-How-does-Tab-for-a-Cause-raise-money-for-charity?fbclid=IwAR2hWqYClpDjoSuxefpU4naqlbVWkREivVW__0nlTSVrxFP5TMdm_W3IASw&amp;mobile_site=true\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n","  ' that document to Beautiful Soup.' % decoded_markup\n","/usr/local/lib/python3.7/dist-packages/bs4/__init__.py:336: UserWarning: \"https://www.youtube.com/watch?v=pGJcwaUWNZg&amp;t=3m30s\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n","  ' that document to Beautiful Soup.' % decoded_markup\n","/usr/local/lib/python3.7/dist-packages/bs4/__init__.py:336: UserWarning: \"https://www.youtube.com/watch?v=lVWVP3tKlZc\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n","  ' that document to Beautiful Soup.' % decoded_markup\n","/usr/local/lib/python3.7/dist-packages/bs4/__init__.py:336: UserWarning: \"https://www.un.org/ldcportal/least-developed-countries-fund-ldcf/\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n","  ' that document to Beautiful Soup.' % decoded_markup\n","/usr/local/lib/python3.7/dist-packages/bs4/__init__.py:336: UserWarning: \"https://fiftrustee.worldbank.org/en/about/unit/dfi/fiftrustee/fund-detail/adapt\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n","  ' that document to Beautiful Soup.' % decoded_markup\n","/usr/local/lib/python3.7/dist-packages/bs4/__init__.py:336: UserWarning: \"https://www.mysequester.com.au/events/offset-projects/free-streaming-england-vs-south-africa-live-stream-icc-cricket-world-cup-2019-1\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n","  ' that document to Beautiful Soup.' % decoded_markup\n","/usr/local/lib/python3.7/dist-packages/bs4/__init__.py:336: UserWarning: \"https://www.mysequester.com.au/events/offset-projects/watch-free-icc-cricket-world-cup-2019-england-vs-south-africa-live-stream-free-icc-cricket-world-cup-2019-live\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n","  ' that document to Beautiful Soup.' % decoded_markup\n","/usr/local/lib/python3.7/dist-packages/bs4/__init__.py:336: UserWarning: \"https://bit.ly/2X8gdWm\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n","  ' that document to Beautiful Soup.' % decoded_markup\n","/usr/local/lib/python3.7/dist-packages/bs4/__init__.py:336: UserWarning: \"http://microtv.website/Europa-League-final-2019\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n","  ' that document to Beautiful Soup.' % decoded_markup\n","/usr/local/lib/python3.7/dist-packages/bs4/__init__.py:336: UserWarning: \"https://www.reddit.com/live/130tvjbslhyoj/\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n","  ' that document to Beautiful Soup.' % decoded_markup\n","/usr/local/lib/python3.7/dist-packages/bs4/__init__.py:336: UserWarning: \"https://www.reddit.com/live/130tvrsz4es6t/\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n","  ' that document to Beautiful Soup.' % decoded_markup\n","/usr/local/lib/python3.7/dist-packages/bs4/__init__.py:336: UserWarning: \"https://www.reddit.com/live/130tw1lbzxz97/\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n","  ' that document to Beautiful Soup.' % decoded_markup\n","/usr/local/lib/python3.7/dist-packages/bs4/__init__.py:336: UserWarning: \"https://youtu.be/Xl5SMS9eKD4\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n","  ' that document to Beautiful Soup.' % decoded_markup\n","/usr/local/lib/python3.7/dist-packages/bs4/__init__.py:336: UserWarning: \"https://youtu.be/lCKc_eDXebM\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n","  ' that document to Beautiful Soup.' % decoded_markup\n"]},{"output_type":"execute_result","data":{"text/plain":["(90793, 5)"]},"metadata":{},"execution_count":17}],"source":["comment_info_split = comment_info.loc[comment_htmls_split.index.map(lambda x: x.split('--')[0])].copy()\n","comment_info_split.index = comment_htmls_split.index\n","comment_info_split['htmlBody'] = comment_htmls_split.values\n","comment_info_split['text'] = comment_info_split.htmlBody.progress_map(lambda h: BeautifulSoup(h, features=\"html.parser\").get_text().replace('\\xa0', ' '))\n","\n","comment_info_split['text'] = comment_info_split['text'].map(lambda x: ' '.join([w for w in x.split() if len(w) < 40])) # Remove non-words\n","# TODO: there are still some people writing all in bullet-points, so we also need to split lists with long bullet-points, but those are minorities\n","# Removing these long comments:\n","# comment_info_split = comment_info_split[(comment_info_split.text.map(len) < 5000) & (comment_info_split.text.map(len) > 70)]\n","comment_info_split = comment_info_split[comment_info_split.text.map(len) > 70]\n","comment_info_split.shape"]},{"cell_type":"code","source":["text_100 = comment_info_split.text.to_list()[:100]"],"metadata":{"id":"5eW9vJcc_FmH","colab":{"base_uri":"https://localhost:8080/","height":179},"executionInfo":{"status":"error","timestamp":1661889604330,"user_tz":-180,"elapsed":433,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}},"outputId":"486c218e-3390-473d-cf31-35f0dbca779a"},"id":"5eW9vJcc_FmH","execution_count":null,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-2-f6d8f6ec795b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtext_100\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcomment_info_split\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mNameError\u001b[0m: name 'comment_info_split' is not defined"]}]},{"cell_type":"code","source":["from transformers import pipeline\n","# classifier = pipeline(\"zero-shot-classification\", model=\"facebook/xglm-564M\") #vicgalle/xlm-roberta-large-xnli-anli\")\n","classifier = pipeline(\"zero-shot-classification\", model=\"facebook/bart-large-mnli\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":226,"referenced_widgets":["1f26259af53a4fb2a65a8bd5556cd312","3194a6aa57f141998240a6d750a3b5c9","120f7b3450304ec5b1a9296854da5186","1e0c27c178c4440e9a88eb3e5a49da10","02f40a7ec6aa418b8432652707c27208","a061e9ea83114a65bac7bfa64178e552","25fe90214a6842809e1ebf261dd45e05","7c95de5b7bf34021acf052d19bfe7ae9","f210f3ae5f214a2eb1c4d36e8868f16c","aedd7fd9807245a79facb231766b6198","17f4824d07de4623a609af4b96a2b40c","8683b35857854b788995cca16128373d","d686c37840ea4c80a173b5541383e2df","2d3936daa7cf44e7889e983ae80951fb","fd701c696eaa41369584f28db4771430","d24c6c877b554a6494d75969c5b09e8a","1a1dfa3331d74eed8e9c74f76f05ae80","0863aab7121f4d5a84f276aa744b83ff","fa050d9eeb8b48fa9699d94ee0d46b0a","f4eb43ea59ca4d8e95ea60995c6981e1","96a123a4ad1e4681a7ba2011f0698812","0c68506c0837447ab16ad59c227eede6","d2a42442de064c1199e7d826d82eb8cc","d39d0a0fd1b143d3b93078c6485aa338","ec7dbbaf6e7a4efa8d4fec5314449cbc","7392cf48ee774384be38290a508bb0b0","1228633ca74f44b7860f927b7f284f8c","5435f41a37084375ad8f23de6e5fc843","8b438e9827f444b4aa9622be3507310d","100b11a0306d4921abf67336a892a1ab","72f2e0c2ef124a2d9b2a856a1c5b1c8d","ecad7f36d09243748cc420dea87ba73c","1a21da5f4dd343f2a61335ed7147a754","9604d161fa75425b859a9afc533a84ab","368533b159f148b29a9f2ba401174028","9321e712e36d487fbc9e902c3b8d452b","aca262487295459189db17e80d178d48","2d3e69e0e82d41aaa7eb8d761a9da200","7fe33ae8037c4ec2b2d208989bcdf4f5","1ed3addded374aca9158755c3d3a8001","11dcd50b88c04204b01e179dbf993d37","32640c5720bb4b3dbac9eba88f5fe556","f71f4515a80e48d1b99ec3d11bf7c951","39b78118e6614d5ebcc5cd99b8462c3b","af6b1f89ab4a4471904a3e05e4bd1044","1adf083ae9b24499af130a12e8b1877b","57fe0add83214c32befc52ef79cee6a3","242a9ae24e2a4509b50d04a22d427bc5","8ca1e47b9b9044c48cd724720ba14c4d","d42ffb4bd583400ebcbeabc6aa11911b","bbc7b71cddf5439094fb5c353a585a6b","3bcdd7ecd7b840b09e7ce4ba82480fac","1b79223629814d339a05e111dc52c4bd","267ac88807de4ab08b49bb2a51142437","f253bdc071884fb384c66974a466f790","1f62469c7d6d4016b7505ae38f33708d","4f17b7f8b30a455dae1fd5215ec99fd0","fa407ef46af24d3a8bad3475ad1e59b9","84636cde2d7b478884040fb4a6ff966f","dadde6b46f6944e3ab801f172da383b0","4c120d341d9e4f47965c7c7ea2eb074c","10e1724a74444465a40d2ec88bca9970","03b51483f76d4b4293301f00b26a9dc0","23ec726e63c84f11aa307cfd5fbf2bd5","5215751f70a04840b4cc11c3b404492e","063c665ef6f64b5daffdd1b6172905c3"]},"id":"zJG7WdNY_sGt","executionInfo":{"status":"ok","timestamp":1661885041426,"user_tz":-180,"elapsed":65647,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}},"outputId":"5974d1d8-84cb-4d31-9d4f-9f31efcc37b8"},"id":"zJG7WdNY_sGt","execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading config.json:   0%|          | 0.00/1.13k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1f26259af53a4fb2a65a8bd5556cd312"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading pytorch_model.bin:   0%|          | 0.00/1.52G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8683b35857854b788995cca16128373d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading tokenizer_config.json:   0%|          | 0.00/26.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d2a42442de064c1199e7d826d82eb8cc"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading vocab.json:   0%|          | 0.00/878k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"9604d161fa75425b859a9afc533a84ab"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading merges.txt:   0%|          | 0.00/446k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"af6b1f89ab4a4471904a3e05e4bd1044"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading tokenizer.json:   0%|          | 0.00/1.29M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1f62469c7d6d4016b7505ae38f33708d"}},"metadata":{}}]},{"cell_type":"code","source":["r = classifier(text_100[0], list_labels_1, multi_label=True)"],"metadata":{"id":"6Yz74Q42BIVb"},"id":"6Yz74Q42BIVb","execution_count":null,"outputs":[]},{"cell_type":"code","source":["def calculate_value_labels(r):\n","  top_label = 'project'\n","  labels = r['labels']\n","  scores = r['scores']\n","  indx = labels.index(top_label)\n","  if indx:\n","    if (scores[0] - scores[indx]) > 0.2:\n","          return 0\n","  return sum(scores[:10])\n","\n","calculate_value_labels(r)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NHSWC3kjBT6W","executionInfo":{"status":"ok","timestamp":1661887933885,"user_tz":-180,"elapsed":432,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}},"outputId":"6ffa0362-e242-4ac7-b24c-4c4c6077046a"},"id":"NHSWC3kjBT6W","execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0"]},"metadata":{},"execution_count":46}]},{"cell_type":"code","source":["t = \"The suggested EA project / org: work to identify important, simple, and uncontroversial ideas as they arise and use social media to get people to pay attention to them.\"\n","rr = classifier(t, list_labels_1, multi_label=True)\n","calculate_value_labels(rr)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lvFPNy5LUAel","executionInfo":{"status":"ok","timestamp":1661889258487,"user_tz":-180,"elapsed":32245,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}},"outputId":"a1d4bcf3-de3b-4eeb-a228-a4d53e2e1b03"},"id":"lvFPNy5LUAel","execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["7.213144481182098"]},"metadata":{},"execution_count":55}]},{"cell_type":"code","source":["rr"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Uz5DqDDoUhXi","executionInfo":{"status":"ok","timestamp":1661889292158,"user_tz":-180,"elapsed":445,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}},"outputId":"5bc6df5d-7f27-46c3-d79c-e1adfb4a461f"},"id":"Uz5DqDDoUhXi","execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'sequence': 'The suggested EA project / org: work to identify important, simple, and uncontroversial ideas as they arise and use social media to get people to pay attention to them.',\n"," 'labels': ['work',\n","  'effort',\n","  'part',\n","  'project',\n","  'collaboration',\n","  'plan',\n","  'develop',\n","  'venture',\n","  'feasibility',\n","  'program',\n","  'programme',\n","  'research',\n","  'initiative',\n","  'study',\n","  'design',\n","  'site',\n","  'developer',\n","  'scheme',\n","  'development',\n","  'exploration',\n","  'conservation',\n","  'launch',\n","  'infrastructure',\n","  'construction',\n","  'experiment',\n","  'build',\n","  'creation',\n","  'renovation',\n","  'pipeline',\n","  'phase',\n","  'restoration',\n","  'consortium',\n","  'redevelopment',\n","  'installation',\n","  'production',\n","  'construct',\n","  'cost',\n","  'reconstruction',\n","  'funding',\n","  'completion'],\n"," 'scores': [0.8951612114906311,\n","  0.8396821618080139,\n","  0.8234682083129883,\n","  0.8001334071159363,\n","  0.7988818287849426,\n","  0.718540608882904,\n","  0.639255940914154,\n","  0.6301458477973938,\n","  0.5472177863121033,\n","  0.520657479763031,\n","  0.5013656616210938,\n","  0.4930676817893982,\n","  0.4742617905139923,\n","  0.4376991391181946,\n","  0.42591241002082825,\n","  0.41217976808547974,\n","  0.41050294041633606,\n","  0.38115400075912476,\n","  0.359193354845047,\n","  0.3548259139060974,\n","  0.323930025100708,\n","  0.3136196434497833,\n","  0.26142942905426025,\n","  0.22634145617485046,\n","  0.22171950340270996,\n","  0.202870711684227,\n","  0.19068647921085358,\n","  0.18239198625087738,\n","  0.179595485329628,\n","  0.15960508584976196,\n","  0.1587228626012802,\n","  0.12840594351291656,\n","  0.12426501512527466,\n","  0.11873985826969147,\n","  0.11363692581653595,\n","  0.08497901260852814,\n","  0.08458853513002396,\n","  0.06059667468070984,\n","  0.013871844857931137,\n","  0.013780241832137108]}"]},"metadata":{},"execution_count":57}]},{"cell_type":"code","source":["results = []\n","for num, text in enumerate(text_100[:5]):\n","  res = classifier(text, list_labels_1, multi_label=True)\n","  val = calculate_value_labels(res)\n","  results.append((num, val))\n","  print('num=', num, 'val=', val)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":255},"id":"icMf-KqZBT-p","executionInfo":{"status":"error","timestamp":1661889585237,"user_tz":-180,"elapsed":434,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}},"outputId":"b46a9adc-3f58-4065-eaae-664e780619a5"},"id":"icMf-KqZBT-p","execution_count":null,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-1-124aee10575f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mnum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtext\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext_100\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m   \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist_labels_1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmulti_label\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m   \u001b[0mval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcalculate_value_labels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m   \u001b[0mresults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'text_100' is not defined"]}]},{"cell_type":"code","source":[],"metadata":{"id":"_HZLxBVZBUMG"},"id":"_HZLxBVZBUMG","execution_count":null,"outputs":[]},{"cell_type":"code","source":["classifier_fb.task"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":38},"id":"Urkn4zlaqMGc","executionInfo":{"status":"ok","timestamp":1661240813082,"user_tz":-180,"elapsed":268,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}},"outputId":"c10856af-5c35-4c9a-f003-1ecedc857f92"},"id":"Urkn4zlaqMGc","execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'zero-shot-classification'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":8}]},{"cell_type":"code","source":["candidate_labels = ['job', 'project'] #, 'joke']\n","for s in text_100[:20]:\n","  print(classifier_fb(s, candidate_labels))\n","  print(classifier_vic(s, candidate_labels))\n","  print(100*'-')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rKX4IxGGATEd","executionInfo":{"status":"ok","timestamp":1661089099736,"user_tz":-180,"elapsed":166723,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}},"outputId":"79671cb6-550f-41e1-e4bd-48b94d442d9e"},"id":"rKX4IxGGATEd","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["{'sequence': \"You don't have to be smart, or a college graduate. What I'm attempting to do at least for my edification is breaking things down into manageable chunks, so that it can easily be explained. I also enjoy looking at some of the EA numbers and shaking my head.The table is small, but that's because we need people to help us build more extensions to the table. Come get some tools and help me make an addition.\", 'labels': ['project', 'job'], 'scores': [0.8233757019042969, 0.17662426829338074]}\n","{'sequence': \"You don't have to be smart, or a college graduate. What I'm attempting to do at least for my edification is breaking things down into manageable chunks, so that it can easily be explained. I also enjoy looking at some of the EA numbers and shaking my head.The table is small, but that's because we need people to help us build more extensions to the table. Come get some tools and help me make an addition.\", 'labels': ['project', 'job'], 'scores': [0.8616064190864563, 0.1383936107158661]}\n","----------------------------------------------------------------------------------------------------\n","{'sequence': 'I love the idea of a Library of EA! It would be helpful to eventually augment it with auxiliary and meta-information, probably through crowdsourcing among EAs. Each book could also be associated with short and medium summaries of the key arguments and takeaways, and warnings about which sections were later disproven or controversial (or a warning that the whole thing is a partial story/misleading). There\\'s also a lot of overlap and superseding within the books (especially within the rationality and epistemology section), so it would be good to say \"If you\\'ve read X, you don\\'t need to read Y\". It would also be great to have a \"Summary of Y for people who have already read X\" that just covers the key information.I do strongly feel that a smaller library would be better. While there are advantages to being comprehensive, a smaller library is better at directing people to the most important books. It is really valuable to say that someone should start with a particular book on a subject, rather than their uninformed choice from a list. Parsimony in recommendations, at least on a personal level, is also important for conveying the importance of the recommendations you do make. It somewhat feels like you weren\\'t confident enough to cut a book that was recommended by some subgroup, even if there were better options available.There\\'s a Pareto principle at play here, where reading 20% of the books will provide 80% of the value, and a repeated Pareto principle where 4% provide 64% of the value. I think you could genuinely recommend four or five books from this list that provide two-thirds of the EA value of the entire list between them. My picks would be The Most Good You Can Do, The Precipice, Reasons and Persons, and Scout Mindset. Curious what others would pick.', 'labels': ['job', 'project'], 'scores': [0.5839900970458984, 0.41600990295410156]}\n","{'sequence': 'I love the idea of a Library of EA! It would be helpful to eventually augment it with auxiliary and meta-information, probably through crowdsourcing among EAs. Each book could also be associated with short and medium summaries of the key arguments and takeaways, and warnings about which sections were later disproven or controversial (or a warning that the whole thing is a partial story/misleading). There\\'s also a lot of overlap and superseding within the books (especially within the rationality and epistemology section), so it would be good to say \"If you\\'ve read X, you don\\'t need to read Y\". It would also be great to have a \"Summary of Y for people who have already read X\" that just covers the key information.I do strongly feel that a smaller library would be better. While there are advantages to being comprehensive, a smaller library is better at directing people to the most important books. It is really valuable to say that someone should start with a particular book on a subject, rather than their uninformed choice from a list. Parsimony in recommendations, at least on a personal level, is also important for conveying the importance of the recommendations you do make. It somewhat feels like you weren\\'t confident enough to cut a book that was recommended by some subgroup, even if there were better options available.There\\'s a Pareto principle at play here, where reading 20% of the books will provide 80% of the value, and a repeated Pareto principle where 4% provide 64% of the value. I think you could genuinely recommend four or five books from this list that provide two-thirds of the EA value of the entire list between them. My picks would be The Most Good You Can Do, The Precipice, Reasons and Persons, and Scout Mindset. Curious what others would pick.', 'labels': ['project', 'job'], 'scores': [0.515292763710022, 0.48470720648765564]}\n","----------------------------------------------------------------------------------------------------\n","{'sequence': \"Although that would be great, from all of the research I read about donations, it doesn't seem that effectiveness is a big factor for consumers. I do think a certification for effectiveness could help, but based on the data I'm pessimistic how much it would add compared to marketing your charity effectively.\", 'labels': ['project', 'job'], 'scores': [0.588933527469635, 0.411066472530365]}\n","{'sequence': \"Although that would be great, from all of the research I read about donations, it doesn't seem that effectiveness is a big factor for consumers. I do think a certification for effectiveness could help, but based on the data I'm pessimistic how much it would add compared to marketing your charity effectively.\", 'labels': ['project', 'job'], 'scores': [0.5634356141090393, 0.4365644156932831]}\n","----------------------------------------------------------------------------------------------------\n","{'sequence': 'Please note that my latest comment includes the research paper with additional data. Looking forward to receiving more feedback.', 'labels': ['project', 'job'], 'scores': [0.7450573444366455, 0.2549426257610321]}\n","{'sequence': 'Please note that my latest comment includes the research paper with additional data. Looking forward to receiving more feedback.', 'labels': ['project', 'job'], 'scores': [0.8178019523620605, 0.18219801783561707]}\n","----------------------------------------------------------------------------------------------------\n","{'sequence': 'Since sociology is probably an underrepresented degree in effective altruism, maybe you can consider it a comparative advantage rather than \"the wrong degree\". The way I see it, EA could use a lot more sociological inquiry.', 'labels': ['project', 'job'], 'scores': [0.7538236379623413, 0.24617637693881989]}\n","{'sequence': 'Since sociology is probably an underrepresented degree in effective altruism, maybe you can consider it a comparative advantage rather than \"the wrong degree\". The way I see it, EA could use a lot more sociological inquiry.', 'labels': ['job', 'project'], 'scores': [0.5102611184120178, 0.4897388517856598]}\n","----------------------------------------------------------------------------------------------------\n","{'sequence': 'There are also easy-access savings accounts giving a bit more than 1.3%:', 'labels': ['project', 'job'], 'scores': [0.6422807574272156, 0.3577192723751068]}\n","{'sequence': 'There are also easy-access savings accounts giving a bit more than 1.3%:', 'labels': ['project', 'job'], 'scores': [0.6997818946838379, 0.3002181053161621]}\n","----------------------------------------------------------------------------------------------------\n","{'sequence': 'If you are under 40 and might want to spend the money on a first property costing <450k, you could consider a Lifetime ISA (either cash or stocks & shares):https://www.gov.uk/lifetime-isa', 'labels': ['project', 'job'], 'scores': [0.8224896788597107, 0.1775103658437729]}\n","{'sequence': 'If you are under 40 and might want to spend the money on a first property costing <450k, you could consider a Lifetime ISA (either cash or stocks & shares):https://www.gov.uk/lifetime-isa', 'labels': ['job', 'project'], 'scores': [0.542938232421875, 0.4570617079734802]}\n","----------------------------------------------------------------------------------------------------\n","{'sequence': 'My thoughts aren\\'t really well-formed on this so you should take all this with a grain of salt. These are just my own perspectives rather than some kind of a well-researched argument.If I recall correctly (and I might be misremembering this) the main difference between operations and projects is that operations are ongoing and projects have specific end dates. I view them as very similar; a handful of skillsets are distinct, but there is massive overlap.My current view is that an aspect of the hiring difficulty in EA isn\\'t so much ops-y versus project-y; it is more so an organization that knows what they will be doing versus an organization that doesn\\'t know what they will be doing.I can easily imagine a more established team (even a smallish organization with less than 50 people) hiring a project manager and doing it well if they have a clear idea of what kinds of projects they will be doing. I think the difficulty of hiring that many small organizations have is that they don\\'t have a clear idea of what projects the new hire would be doing. They have vague ideas, but even those vague ideas aren\\'t very reliable because things might shift.To make it more concrete, if my team is distributing bednets, or sending engineers to rural India to design wells, or even researching AI safety, we have a pretty clear idea of what a new hire would be doing and what skills are helpful. But if my team is trying to figure out how to do the most good in CAUSE_AREA, or how to have the most impact at the intersection of longtermism and TOPIC, or even trying to figure out how to best nudge young people into impactful careers, then both our future projects and our future operations are really nebulous. Once the \"figuring out\" stage is done[1] then hiring will be a lot easier because you have a more concrete idea regarding what the task is.^In reality I guess it is never really done, but for purposes of simplicity we can imagine a stage at which a lot of stuff has been figured our decently confidently and figuring things out isn\\'t as big of a focus anymore.', 'labels': ['project', 'job'], 'scores': [0.7115581035614014, 0.288441926240921]}\n","{'sequence': 'My thoughts aren\\'t really well-formed on this so you should take all this with a grain of salt. These are just my own perspectives rather than some kind of a well-researched argument.If I recall correctly (and I might be misremembering this) the main difference between operations and projects is that operations are ongoing and projects have specific end dates. I view them as very similar; a handful of skillsets are distinct, but there is massive overlap.My current view is that an aspect of the hiring difficulty in EA isn\\'t so much ops-y versus project-y; it is more so an organization that knows what they will be doing versus an organization that doesn\\'t know what they will be doing.I can easily imagine a more established team (even a smallish organization with less than 50 people) hiring a project manager and doing it well if they have a clear idea of what kinds of projects they will be doing. I think the difficulty of hiring that many small organizations have is that they don\\'t have a clear idea of what projects the new hire would be doing. They have vague ideas, but even those vague ideas aren\\'t very reliable because things might shift.To make it more concrete, if my team is distributing bednets, or sending engineers to rural India to design wells, or even researching AI safety, we have a pretty clear idea of what a new hire would be doing and what skills are helpful. But if my team is trying to figure out how to do the most good in CAUSE_AREA, or how to have the most impact at the intersection of longtermism and TOPIC, or even trying to figure out how to best nudge young people into impactful careers, then both our future projects and our future operations are really nebulous. Once the \"figuring out\" stage is done[1] then hiring will be a lot easier because you have a more concrete idea regarding what the task is.^In reality I guess it is never really done, but for purposes of simplicity we can imagine a stage at which a lot of stuff has been figured our decently confidently and figuring things out isn\\'t as big of a focus anymore.', 'labels': ['project', 'job'], 'scores': [0.5002743005752563, 0.49972566962242126]}\n","----------------------------------------------------------------------------------------------------\n","{'sequence': \"You don't need to have a PhD to give a portion of your income to effective charities and do a lot of good. That's part of what makes effective giving such a powerful idea.\", 'labels': ['project', 'job'], 'scores': [0.6156218647956848, 0.38437819480895996]}\n","{'sequence': \"You don't need to have a PhD to give a portion of your income to effective charities and do a lot of good. That's part of what makes effective giving such a powerful idea.\", 'labels': ['project', 'job'], 'scores': [0.5433964133262634, 0.4566035568714142]}\n","----------------------------------------------------------------------------------------------------\n","{'sequence': \"EAs should stay aware of the world’s biggest challenges even if we’re currently better able to help in other places. I completely agree. I don’t think mass civilian casualties actually threaten the stability of the regime. I'd actually be interested to know if there are more developed theories about this (mine sets a pretty low bar).\", 'labels': ['job', 'project'], 'scores': [0.6602250337600708, 0.339775025844574]}\n","{'sequence': \"EAs should stay aware of the world’s biggest challenges even if we’re currently better able to help in other places. I completely agree. I don’t think mass civilian casualties actually threaten the stability of the regime. I'd actually be interested to know if there are more developed theories about this (mine sets a pretty low bar).\", 'labels': ['job', 'project'], 'scores': [0.5070734024047852, 0.49292656779289246]}\n","----------------------------------------------------------------------------------------------------\n","{'sequence': 'I don\\'t think we disagree much here, but where we do I\\'m trying to bottom out the cruxes... I think it\\'s primarily risk appetite. I do agree though that the wrong hire can make things hellish, on many levels. But in my experience that\\'s usually been less driven by what people thought was important and moreso by the individual\\'s characteristics, behaviours, degree of self-awareness, tendency towards defensiveness / self protection vs. openness. Usually if it doesn\\'t work out in terms of irreconcilably different views on a problem, people just agree to disagree and move on!Perhaps we also have different things in our heads as meaningful signals of being a good leader for the org, and maybe different models of how a \"signed up to doing good but not every EA doctrinal belief\" person would operate. As mentioned in the post how you (dis)agree is often the most important thing; which reflects what you\\'re saying about flexible and open-minded people with their own perspectives. I think I stand by the IIDM example, illustrating how you don\\'t need to be signed up to every EA idea to add a lot of value to an organisation. I think it\\'s similar for X-risk oriented pandemic preparedness, AI risk, etc; that sometimes the most strategically sound thing to do would be more near-term, but those with a long-term orientation could not have that in their immediate view. Similarly would apply for e.g. deciding which funders / partners to work with; skills / talent requirements within the team; etc. (That said, if there\\'s an instinctive feeling that an EA adjacent / non-EA hire - senior or otherwise - could threaten organisational alignment, it\\'s almost a recipe for unconscious ostracism and exclusion; almost in a self-fulfilling prophecy kind of way. It\\'s just very human to react negatively to someone who you feel is threatening. So yeah - another thing to reflect on if you are working in an EA org).Maybe another crux is how much those people are exceptions? As I argued in the post, my hunch is there\\'s many more people like that who are not getting a shot - referring to the \\'wild card\\' example in the post again. I suspect this question could really only be answered by orgs doing the post mortem on their recruitments, to see why people fell off at different stages and if the question is asked (ironically, like in an actual post-mortem) if \\'anything different could have been done?\\'', 'labels': ['job', 'project'], 'scores': [0.658662736415863, 0.34133726358413696]}\n","{'sequence': 'I don\\'t think we disagree much here, but where we do I\\'m trying to bottom out the cruxes... I think it\\'s primarily risk appetite. I do agree though that the wrong hire can make things hellish, on many levels. But in my experience that\\'s usually been less driven by what people thought was important and moreso by the individual\\'s characteristics, behaviours, degree of self-awareness, tendency towards defensiveness / self protection vs. openness. Usually if it doesn\\'t work out in terms of irreconcilably different views on a problem, people just agree to disagree and move on!Perhaps we also have different things in our heads as meaningful signals of being a good leader for the org, and maybe different models of how a \"signed up to doing good but not every EA doctrinal belief\" person would operate. As mentioned in the post how you (dis)agree is often the most important thing; which reflects what you\\'re saying about flexible and open-minded people with their own perspectives. I think I stand by the IIDM example, illustrating how you don\\'t need to be signed up to every EA idea to add a lot of value to an organisation. I think it\\'s similar for X-risk oriented pandemic preparedness, AI risk, etc; that sometimes the most strategically sound thing to do would be more near-term, but those with a long-term orientation could not have that in their immediate view. Similarly would apply for e.g. deciding which funders / partners to work with; skills / talent requirements within the team; etc. (That said, if there\\'s an instinctive feeling that an EA adjacent / non-EA hire - senior or otherwise - could threaten organisational alignment, it\\'s almost a recipe for unconscious ostracism and exclusion; almost in a self-fulfilling prophecy kind of way. It\\'s just very human to react negatively to someone who you feel is threatening. So yeah - another thing to reflect on if you are working in an EA org).Maybe another crux is how much those people are exceptions? As I argued in the post, my hunch is there\\'s many more people like that who are not getting a shot - referring to the \\'wild card\\' example in the post again. I suspect this question could really only be answered by orgs doing the post mortem on their recruitments, to see why people fell off at different stages and if the question is asked (ironically, like in an actual post-mortem) if \\'anything different could have been done?\\'', 'labels': ['job', 'project'], 'scores': [0.5202712416648865, 0.4797287583351135]}\n","----------------------------------------------------------------------------------------------------\n","{'sequence': \"Right. (Which is significantly about level of context not just innate properties of the people; also as I alluded to briefly depends on the coordination problem of finding the right roles for people.) But I don't really think introducing this changes the concept of funding overhangs?\", 'labels': ['project', 'job'], 'scores': [0.579918622970581, 0.42008140683174133]}\n","{'sequence': \"Right. (Which is significantly about level of context not just innate properties of the people; also as I alluded to briefly depends on the coordination problem of finding the right roles for people.) But I don't really think introducing this changes the concept of funding overhangs?\", 'labels': ['job', 'project'], 'scores': [0.7511980533599854, 0.24880200624465942]}\n","----------------------------------------------------------------------------------------------------\n","{'sequence': \"If we want to move people away from person-affecting views entirely, we need other arguments.Fwiw, I wasn't particularly trying to do this. I'm not super happy with any particular view on population ethics and I wouldn't be that surprised if the actual view I settled on after a long reflection was pretty different from anything that exists today, and does incorporate something vaguely like person-affecting intuitions.I mostly notice that people who have some but not much experience with longtermism are often very aware of the Repugnant Conclusion and other objections to total utilitarianism, and conclude that actually person-affecting intuitions are the right way to go. In at least two cases they seemed to significantly reconsider upon presenting this argument. It seems to me like, amongst the population of people who haven't engaged with the population ethics literature, critiques of total utilitarianism are very well known but critiques of person affecting intuitions are barely known at all. I'm just trying to fix that discrepancy.Also a minor terminological note, you've called your argument a Dutch book and so have I. But I think it would be more standard to call it a money pump.Thanks, I've changed this.\", 'labels': ['project', 'job'], 'scores': [0.5485534071922302, 0.45144662261009216]}\n","{'sequence': \"If we want to move people away from person-affecting views entirely, we need other arguments.Fwiw, I wasn't particularly trying to do this. I'm not super happy with any particular view on population ethics and I wouldn't be that surprised if the actual view I settled on after a long reflection was pretty different from anything that exists today, and does incorporate something vaguely like person-affecting intuitions.I mostly notice that people who have some but not much experience with longtermism are often very aware of the Repugnant Conclusion and other objections to total utilitarianism, and conclude that actually person-affecting intuitions are the right way to go. In at least two cases they seemed to significantly reconsider upon presenting this argument. It seems to me like, amongst the population of people who haven't engaged with the population ethics literature, critiques of total utilitarianism are very well known but critiques of person affecting intuitions are barely known at all. I'm just trying to fix that discrepancy.Also a minor terminological note, you've called your argument a Dutch book and so have I. But I think it would be more standard to call it a money pump.Thanks, I've changed this.\", 'labels': ['job', 'project'], 'scores': [0.5085629224777222, 0.4914371073246002]}\n","----------------------------------------------------------------------------------------------------\n","{'sequence': 'I agree, it doesn’t seem like an actionable opportunity to help with our current resources. I’d be more optimistic about sending supplies to reduce suffering because I don’t think mass civilian casualties actually threaten the stability of the regime. But that’s debatable and difficult to do. I’m mainly interested because the North Korean government seems like one of the largest sources of suffering in the world, and EAs should stay aware of the world’s biggest challenges even if we’re currently better able to help in other places.', 'labels': ['project', 'job'], 'scores': [0.5144339203834534, 0.48556604981422424]}\n","{'sequence': 'I agree, it doesn’t seem like an actionable opportunity to help with our current resources. I’d be more optimistic about sending supplies to reduce suffering because I don’t think mass civilian casualties actually threaten the stability of the regime. But that’s debatable and difficult to do. I’m mainly interested because the North Korean government seems like one of the largest sources of suffering in the world, and EAs should stay aware of the world’s biggest challenges even if we’re currently better able to help in other places.', 'labels': ['project', 'job'], 'scores': [0.8292365074157715, 0.17076343297958374]}\n","----------------------------------------------------------------------------------------------------\n","{'sequence': 'This doesn\\'t address the elephant which is \"quality\" of talent. EA has a funding overhang with respect to some implicit \"quality line\" at which people will be hired. Getting more people over that line lowers the funding overhang, but only getting more people under the line doesn\\'t change anything.', 'labels': ['job', 'project'], 'scores': [0.6218067407608032, 0.37819328904151917]}\n","{'sequence': 'This doesn\\'t address the elephant which is \"quality\" of talent. EA has a funding overhang with respect to some implicit \"quality line\" at which people will be hired. Getting more people over that line lowers the funding overhang, but only getting more people under the line doesn\\'t change anything.', 'labels': ['project', 'job'], 'scores': [0.6023309826850891, 0.3976690173149109]}\n","----------------------------------------------------------------------------------------------------\n","{'sequence': 'This is really exciting - congratulations! Amongst the EA community, some people are really excited by the prospect of humans spreading into space. Other people are quite nervous about this happening, worrying that it could spread suffering much more widely across the universe.Do you have a view on this crucial consideration? Would you consider consulting with people from both communities to try to ensure that your actions/policy recommendations are “robust” to both sides? Many thanks for considering this! :)', 'labels': ['project', 'job'], 'scores': [0.5849927067756653, 0.4150073230266571]}\n","{'sequence': 'This is really exciting - congratulations! Amongst the EA community, some people are really excited by the prospect of humans spreading into space. Other people are quite nervous about this happening, worrying that it could spread suffering much more widely across the universe.Do you have a view on this crucial consideration? Would you consider consulting with people from both communities to try to ensure that your actions/policy recommendations are “robust” to both sides? Many thanks for considering this! :)', 'labels': ['project', 'job'], 'scores': [0.9875181317329407, 0.012481888756155968]}\n","----------------------------------------------------------------------------------------------------\n","{'sequence': \"I love this! How to Measure Anything is in the Global Poverty section, so don't forget to fix that!Governance could probably handle The Myth of the Rational Voter.I wonder if The Model Thinker should be included as a high-value easy to read mathematical modelling book.\", 'labels': ['job', 'project'], 'scores': [0.5128228664398193, 0.48717716336250305]}\n","{'sequence': \"I love this! How to Measure Anything is in the Global Poverty section, so don't forget to fix that!Governance could probably handle The Myth of the Rational Voter.I wonder if The Model Thinker should be included as a high-value easy to read mathematical modelling book.\", 'labels': ['project', 'job'], 'scores': [0.5153862237930298, 0.4846138656139374]}\n","----------------------------------------------------------------------------------------------------\n","{'sequence': \"For another counterargument to your point about the fact that some positions don't look attractive to people who are overqualified, here's Ben West's article. I personally think that making the position challenge and a growth opportunity make people more motivated and excited.\", 'labels': ['job', 'project'], 'scores': [0.7069518566131592, 0.2930481433868408]}\n","{'sequence': \"For another counterargument to your point about the fact that some positions don't look attractive to people who are overqualified, here's Ben West's article. I personally think that making the position challenge and a growth opportunity make people more motivated and excited.\", 'labels': ['job', 'project'], 'scores': [0.6928719282150269, 0.30712807178497314]}\n","----------------------------------------------------------------------------------------------------\n","{'sequence': 'This was such a great post and I was nodding along throughout the whole article, except for the part about the importance of hiring people who are \"strategically aligned\". I think that you often need people at the top of the organization to deeply share the org\\'s ethics and long-term goals, otherwise you find yourself in very-long debates about theories of change, which ultimately affect a lot of the decisions (I wonder if you have experienced this?). The exception to this is when you find non-EA, but exceptional people who share EA goals while also having their own perspectives and motivations, who are quite flexible and open-minded - those can indeed bring a fresh perspective. But I think those people are rare enough that it would make sense to filter at least a little bit in the interview about the long-term goals, ethics, values of the person, and how they would approach the org\\'s theory of change.', 'labels': ['job', 'project'], 'scores': [0.6038336157798767, 0.3961663842201233]}\n","{'sequence': 'This was such a great post and I was nodding along throughout the whole article, except for the part about the importance of hiring people who are \"strategically aligned\". I think that you often need people at the top of the organization to deeply share the org\\'s ethics and long-term goals, otherwise you find yourself in very-long debates about theories of change, which ultimately affect a lot of the decisions (I wonder if you have experienced this?). The exception to this is when you find non-EA, but exceptional people who share EA goals while also having their own perspectives and motivations, who are quite flexible and open-minded - those can indeed bring a fresh perspective. But I think those people are rare enough that it would make sense to filter at least a little bit in the interview about the long-term goals, ethics, values of the person, and how they would approach the org\\'s theory of change.', 'labels': ['job', 'project'], 'scores': [0.7664416432380676, 0.2335582971572876]}\n","----------------------------------------------------------------------------------------------------\n","{'sequence': \"I'm now noticing I'm unsure whether you disagree that it's okay to approximate #1 as zero. I read your post as arguing against approximating #2 as zero, but maybe that's just because that's something I agree with, and you actually intended to cover both 1+2.\", 'labels': ['project', 'job'], 'scores': [0.5696805119514465, 0.4303194582462311]}\n","{'sequence': \"I'm now noticing I'm unsure whether you disagree that it's okay to approximate #1 as zero. I read your post as arguing against approximating #2 as zero, but maybe that's just because that's something I agree with, and you actually intended to cover both 1+2.\", 'labels': ['project', 'job'], 'scores': [0.5141472220420837, 0.48585283756256104]}\n","----------------------------------------------------------------------------------------------------\n"]}]},{"cell_type":"code","execution_count":null,"id":"78fee7d8-b34c-421c-9eb2-6319c5855d4f","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":282},"id":"78fee7d8-b34c-421c-9eb2-6319c5855d4f","executionInfo":{"status":"ok","timestamp":1659971335972,"user_tz":-180,"elapsed":2924,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}},"outputId":"77e4886b-72fd-4fe8-d3fc-51b3524516ed"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f61b8618690>"]},"metadata":{},"execution_count":26},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}],"source":["comment_info_split.text.map(len).hist(bins=100)"]},{"cell_type":"code","execution_count":null,"id":"4045e453-da65-461c-a79c-cc0df34aabfd","metadata":{"id":"4045e453-da65-461c-a79c-cc0df34aabfd"},"outputs":[],"source":["# tpid = comment_info_split.text.map(len)[comment_info_split.text.map(len) < 5000].sort_values().index[-8]\n","# comment_info_split.loc[tpid].pageUrl"]},{"cell_type":"markdown","id":"d0c6f3f2-0b65-4b31-ab8d-1b81ee0d09c5","metadata":{"id":"d0c6f3f2-0b65-4b31-ab8d-1b81ee0d09c5"},"source":["## Classify"]},{"cell_type":"code","execution_count":null,"id":"e38c689c-6e68-4869-9f87-c4e6929d90bc","metadata":{"id":"e38c689c-6e68-4869-9f87-c4e6929d90bc"},"outputs":[],"source":["# with open(\"./data/example_proposals.txt\") as f:\n","#     sents = f.read()\n","    \n","# sents = [s for s in sents.split(\"\\n\") if len(s) > 0]"]},{"cell_type":"code","execution_count":null,"id":"c1afda2c-1ad2-4b96-ba91-9d6392ad9943","metadata":{"id":"c1afda2c-1ad2-4b96-ba91-9d6392ad9943","outputId":"fcd5a856-0472-4999-e17c-83ea46d347f9","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1661793881135,"user_tz":-180,"elapsed":4405,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["2022-08-29 17:24:36,639 loading file /root/.flair/models/tars-base-v8.pt\n"]}],"source":["from flair.models import TARSClassifier\n","from flair.data import Sentence\n","\n","tars = TARSClassifier.load('tars-base')\n","# tars1 = TARSClassifier.load(\"facebook/bart-large-mnli\")"]},{"cell_type":"code","source":["sentence = Sentence(\"great\")\n","classes = [\"positive\", \"negative\"]\n","tars1.predict_zero_shot(sentence, classes)\n","print(sentence)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"na_WMxpsto7Z","executionInfo":{"status":"ok","timestamp":1661242449464,"user_tz":-180,"elapsed":271,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}},"outputId":"0c3ca100-1c9e-43ca-dcce-9ab716fcdcf6"},"id":"na_WMxpsto7Z","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Sentence: \"great\" → positive (0.9691)\n"]}]},{"cell_type":"code","source":["# tars.switch_to_task('AGNews') \n","tars.switch_to_task('NEWS_CATEGORY')"],"metadata":{"id":"VjWT7dLRrKWw"},"id":"VjWT7dLRrKWw","execution_count":null,"outputs":[]},{"cell_type":"code","source":["tars.get_current_label_dictionary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"HkCjUrcgr333","executionInfo":{"status":"ok","timestamp":1661241833963,"user_tz":-180,"elapsed":257,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}},"outputId":"1b0b783d-ca9f-464c-edef-ecf248ab9cb4"},"id":"HkCjUrcgr333","execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<flair.data.Dictionary at 0x7f62908a5990>"]},"metadata":{},"execution_count":22}]},{"cell_type":"code","source":["tars.list_existing_tasks() "],"metadata":{"id":"tpr09d1rJcjO","executionInfo":{"status":"ok","timestamp":1661240907201,"user_tz":-180,"elapsed":278,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}},"outputId":"563ee4bb-7e59-417e-c7fa-b4021eac6f8c","colab":{"base_uri":"https://localhost:8080/"}},"id":"tpr09d1rJcjO","execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'AGNews',\n"," 'Amazon',\n"," 'DBPedia',\n"," 'GO_EMOTIONS',\n"," 'IMDB',\n"," 'NEWS_CATEGORY',\n"," 'SST',\n"," 'TREC_6',\n"," 'Yelp'}"]},"metadata":{},"execution_count":10}]},{"cell_type":"code","source":["sent1 = Sentence(\"I absolutely love this !\")\n","sent = Sentence(\"You don't have to be smart, or a college graduate. What I'm attempting to do at least for my edification is breaking things down into manageable chunks, so that it can easily be explained. I also enjoy looking at some of the EA numbers and shaking my head.The table is small, but that's because we need people to help us build more extensions to the table. Come get some tools and help me make an addition.\")\n","clss = [\"job\", \"project\"]\n","# clss = [\"politics\", \"project\"]\n","tars.predict_zero_shot(sent, clss)\n","print(sent)\n","print(100*'_')\n","tars.predict(sent1)\n","print(sent1)\n","tars.switch_to_task(\"GO_EMOTIONS\")\n","tars.predict(sent1)\n","print(sent1)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"n6RW2aWgAp-p","executionInfo":{"status":"ok","timestamp":1661245178816,"user_tz":-180,"elapsed":4470,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}},"outputId":"d1cc18e1-8cf4-4a50-ee69-40f49303ee31"},"id":"n6RW2aWgAp-p","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Sentence: \"You do n't have to be smart , or a college graduate . What I 'm attempting to do at least for my edification is breaking things down into manageable chunks , so that it can easily be explained . I also enjoy looking at some of the EA numbers and shaking my head.The table is small , but that 's because we need people to help us build more extensions to the table . Come get some tools and help me make an addition .\" → project (0.5918)\n","____________________________________________________________________________________________________\n","Sentence: \"I absolutely love this !\" → very_positive_restaurant_sentiment_5 (0.995)\n","Sentence: \"I absolutely love this !\" → LOVE (0.9974)\n"]}]},{"cell_type":"code","source":["# tars.switch_to_task('AGNews') \n"],"metadata":{"id":"fRavWDyH7LuJ"},"execution_count":null,"outputs":[],"id":"fRavWDyH7LuJ"},{"cell_type":"code","execution_count":null,"id":"4a9bd84f-35c6-4ff2-a20e-9ae8c98d74a4","metadata":{"id":"4a9bd84f-35c6-4ff2-a20e-9ae8c98d74a4"},"outputs":[],"source":["def get_class_score(clf, cl_lab, text):\n","    st = Sentence(text)\n","    clf.predict_zero_shot(st, [cl_lab], multi_label=False)\n","\n","    lab = st.get_label()\n","    tag = lab.value\n","    score = lab.score if lab.value == cl_lab else 0.0\n","    return score"]},{"cell_type":"code","execution_count":null,"id":"668850d0-a43e-4145-a8e7-9f81a9b069e5","metadata":{"colab":{"referenced_widgets":["67a9f08377254308bf8ea938a1646d3a","b23aa6af38c64e13b91f3dc49ac8e231","df79570d5ec64a2f81046c19444b1bef","9c47653c9e204aa58a50b40b4ec5c883","03f0d6f687cb46658825753b0411ce5e","27bfeec967e34feba8de1c87b1e196f6","102638b261854aa2b7d170ebe5297966","5831b7d70ce44d448e2d0bf6732f6e9b","453c820ded8f4b41b4dfe96b6e359afc","ce7d439b113544619247aef0baefab6e","c63590ea55e843d7ae2d25fed825e263"],"base_uri":"https://localhost:8080/","height":448},"id":"668850d0-a43e-4145-a8e7-9f81a9b069e5","outputId":"70095dfb-50e8-45b2-80ba-a2605d65bb43","executionInfo":{"status":"error","timestamp":1661934410736,"user_tz":-180,"elapsed":294,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/2000 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"67a9f08377254308bf8ea938a1646d3a"}},"metadata":{}},{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-23-cd97313ac24d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0msamp_ids\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcomment_info_split\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mclass_probs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcomment_info_split\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msamp_ids\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprogress_map\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mget_class_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtars\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"project proposal\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;31m# class_probs.to_pickle(\"./class_probs.pkl\")\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# class_probs = pd.read_pickle(\"./class_probs.pkl\")\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mclass_probs\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0.75\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tqdm/std.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(df, func, *args, **kwargs)\u001b[0m\n\u001b[1;32m    812\u001b[0m                 \u001b[0;31m# on the df using our wrapper (which provides bar updating)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    813\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 814\u001b[0;31m                     \u001b[0;32mreturn\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf_function\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwrapper\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    815\u001b[0m                 \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    816\u001b[0m                     \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36mmap\u001b[0;34m(self, arg, na_action)\u001b[0m\n\u001b[1;32m   4159\u001b[0m         \u001b[0mdtype\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4160\u001b[0m         \"\"\"\n\u001b[0;32m-> 4161\u001b[0;31m         \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_map_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mna_action\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mna_action\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4162\u001b[0m         return self._constructor(new_values, index=self.index).__finalize__(\n\u001b[1;32m   4163\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"map\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/base.py\u001b[0m in \u001b[0;36m_map_values\u001b[0;34m(self, mapper, na_action)\u001b[0m\n\u001b[1;32m    868\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    869\u001b[0m         \u001b[0;31m# mapper is a function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 870\u001b[0;31m         \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmap_f\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmapper\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    871\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    872\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mnew_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/_libs/lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.map_infer\u001b[0;34m()\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tqdm/std.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    807\u001b[0m                     \u001b[0;31m# take a fast or slow code path; so stop when t.total==t.n\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    808\u001b[0m                     \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtotal\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtotal\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 809\u001b[0;31m                     \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    810\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    811\u001b[0m                 \u001b[0;31m# Apply the provided function (in **kwargs)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m<ipython-input-23-cd97313ac24d>\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0msamp_ids\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcomment_info_split\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mclass_probs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcomment_info_split\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msamp_ids\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprogress_map\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mget_class_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtars\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"project proposal\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;31m# class_probs.to_pickle(\"./class_probs.pkl\")\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# class_probs = pd.read_pickle(\"./class_probs.pkl\")\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mclass_probs\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0.75\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'tars' is not defined"]}],"source":["samp_ids = np.random.choice(comment_info_split.index, size=2000, replace=False)\n","class_probs = comment_info_split.text[samp_ids].progress_map(lambda t: get_class_score(tars, \"project proposal\", t))\n","# class_probs.to_pickle(\"./class_probs.pkl\")\n","# class_probs = pd.read_pickle(\"./class_probs.pkl\")\n","(class_probs > 0.75).mean()"]},{"cell_type":"markdown","id":"60ded9ea-516c-46c7-8e86-9865c1fabde7","metadata":{"id":"60ded9ea-516c-46c7-8e86-9865c1fabde7"},"source":["## Wrapper for the UI integration"]},{"cell_type":"code","execution_count":null,"id":"b8e51b63-b84d-4d43-929e-e31f5ccbf8b3","metadata":{"id":"b8e51b63-b84d-4d43-929e-e31f5ccbf8b3"},"outputs":[],"source":["import sqlite3 as db"]},{"cell_type":"markdown","id":"17d2d473-0f6f-4345-9ea1-a67baec20be2","metadata":{"id":"17d2d473-0f6f-4345-9ea1-a67baec20be2"},"source":["Create labeled table:"]},{"cell_type":"code","execution_count":null,"id":"006c39ab-82d1-43de-884d-458a16796cc2","metadata":{"id":"006c39ab-82d1-43de-884d-458a16796cc2"},"outputs":[],"source":["def get_target_prob_ids(class_probs:Series, target_prob:float, n_samples:int=10, labeled_ids=None):\n","    if labeled_ids is None:\n","        labeled_ids = []\n","    return (class_probs[class_probs.index.difference(labeled_ids)] - target_prob).abs().sort_values()[:n_samples].index"]},{"cell_type":"code","execution_count":null,"id":"6e780172-a3f3-46db-8aec-f58b4110dd61","metadata":{"id":"6e780172-a3f3-46db-8aec-f58b4110dd61","colab":{"base_uri":"https://localhost:8080/","height":217},"executionInfo":{"status":"error","timestamp":1661934422063,"user_tz":-180,"elapsed":506,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}},"outputId":"d04d3fe1-3820-4e94-d3a7-05f3ceb45728"},"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-24-e1eac619070c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m# labeled_ids = pd.read_sql_query(\"SELECT postId FROM labels\", conn).postId.values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mlabeled_ids\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mclosest_ids\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_target_prob_ids\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclass_probs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_prob\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_samples\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabeled_ids\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabeled_ids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mNameError\u001b[0m: name 'class_probs' is not defined"]}],"source":["# conn = db.connect('./backend/labels.db')\n","# labeled_ids = pd.read_sql_query(\"SELECT postId FROM labels\", conn).postId.values\n","labeled_ids = []\n","closest_ids = get_target_prob_ids(class_probs, target_prob=1.0, n_samples=20, labeled_ids=labeled_ids)"]},{"cell_type":"code","execution_count":null,"id":"ae4db3b1-2fa0-4669-927a-94042ecd06d1","metadata":{"id":"ae4db3b1-2fa0-4669-927a-94042ecd06d1"},"outputs":[],"source":["file_classify_csv = Path.joinpath(pref, 'to_classify.csv')\n","\n","comment_info_split.text[closest_ids].reset_index().to_csv(file_classify_csv, index=True)"]},{"cell_type":"code","execution_count":null,"id":"83e5d996-0090-4510-85b8-324fbf080d84","metadata":{"id":"83e5d996-0090-4510-85b8-324fbf080d84"},"outputs":[],"source":["# pd.read_sql_query(\"SELECT * FROM labels\", conn)"]},{"cell_type":"code","execution_count":null,"id":"b77fbf7b-81c0-41ec-be43-b2faba6837e6","metadata":{"id":"b77fbf7b-81c0-41ec-be43-b2faba6837e6"},"outputs":[],"source":["# classes = [\"project proposal\"]\n","\n","# for s in sents:\n","#     st = Sentence(s)\n","#     tars.predict_zero_shot(st, classes, multi_label=False)\n","#     print(st)"]},{"cell_type":"markdown","id":"5d2f4584-e950-40d5-9440-6710d9cfd90a","metadata":{"id":"5d2f4584-e950-40d5-9440-6710d9cfd90a"},"source":["## Fine-tune"]},{"cell_type":"code","execution_count":null,"id":"6c3704d6-bdbd-4666-938c-a33b8a38c224","metadata":{"id":"6c3704d6-bdbd-4666-938c-a33b8a38c224","outputId":"7f8d36a9-80b2-48e9-add4-05ed37b52c51","colab":{"base_uri":"https://localhost:8080/","height":380},"executionInfo":{"status":"ok","timestamp":1661934441269,"user_tz":-180,"elapsed":1885,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                                 text             label\n","0   The suggested EA project / org: work to identi...  project proposal\n","18  As a concrete program related to the safety of...  project proposal\n","43  One possible area for exploration is around Sc...  project proposal\n","40  I propose an infrastructure to generate more a...  project proposal\n","58  Hi Vaidehi,What kind of research do you plan o...  project proposal"],"text/html":["\n","  <div id=\"df-dc94282c-713b-423b-9ae6-074b64cc53a4\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>text</th>\n","      <th>label</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>The suggested EA project / org: work to identi...</td>\n","      <td>project proposal</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>As a concrete program related to the safety of...</td>\n","      <td>project proposal</td>\n","    </tr>\n","    <tr>\n","      <th>43</th>\n","      <td>One possible area for exploration is around Sc...</td>\n","      <td>project proposal</td>\n","    </tr>\n","    <tr>\n","      <th>40</th>\n","      <td>I propose an infrastructure to generate more a...</td>\n","      <td>project proposal</td>\n","    </tr>\n","    <tr>\n","      <th>58</th>\n","      <td>Hi Vaidehi,What kind of research do you plan o...</td>\n","      <td>project proposal</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dc94282c-713b-423b-9ae6-074b64cc53a4')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 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Progress:\n"]},{"name":"stderr","output_type":"stream","text":["3it [00:00, 781.79it/s]"]},{"name":"stdout","output_type":"stream","text":["2022-07-15 17:15:43,421 Dictionary created for label 'label' with 2 values: project proposal (seen 3 times)\n"]},{"name":"stderr","output_type":"stream","text":["\n"]}],"source":["from flair.data import Corpus\n","from flair.datasets import CSVClassificationCorpus\n","\n","column_name_map = {1: 'text', 2: 'label'}\n","corpus = CSVClassificationCorpus(\"./output/flair_train/\", column_name_map, label_type=\"label\", skip_header=True, delimiter='\\t', no_class_label=\"\")\n","label_dict = corpus.make_label_dictionary(label_type=\"label\")"]},{"cell_type":"code","execution_count":null,"id":"9c51c9ea-5c7d-4c62-9c31-36969864ecfd","metadata":{"id":"9c51c9ea-5c7d-4c62-9c31-36969864ecfd"},"outputs":[],"source":["tars.add_and_switch_to_new_task(\"jobs\", label_dictionary=label_dict, label_type=\"label\")"]},{"cell_type":"code","execution_count":null,"id":"4d02644d-17c0-45f2-9efc-f9ea647ebb31","metadata":{"collapsed":true,"jupyter":{"outputs_hidden":true},"tags":[],"id":"4d02644d-17c0-45f2-9efc-f9ea647ebb31","outputId":"bf2aecca-7b4c-48d2-e3b2-902298d2be8f"},"outputs":[{"name":"stdout","output_type":"stream","text":["2022-07-15 17:15:47,269 ----------------------------------------------------------------------------------------------------\n","2022-07-15 17:15:47,272 Model: \"TARSClassifier(\n","  (tars_model): TextClassifier(\n","    (decoder): Linear(in_features=768, out_features=2, bias=True)\n","    (dropout): Dropout(p=0.0, inplace=False)\n","    (locked_dropout): LockedDropout(p=0.0)\n","    (word_dropout): WordDropout(p=0.0)\n","    (loss_function): CrossEntropyLoss()\n","    (document_embeddings): TransformerDocumentEmbeddings(\n","      (model): BertModel(\n","        (embeddings): BertEmbeddings(\n","          (word_embeddings): Embedding(30522, 768, padding_idx=0)\n","          (position_embeddings): Embedding(512, 768)\n","          (token_type_embeddings): Embedding(2, 768)\n","          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","          (dropout): Dropout(p=0.1, inplace=False)\n","        )\n","        (encoder): BertEncoder(\n","          (layer): ModuleList(\n","            (0): BertLayer(\n","              (attention): BertAttention(\n","                (self): BertSelfAttention(\n","                  (query): Linear(in_features=768, out_features=768, bias=True)\n","                  (key): Linear(in_features=768, out_features=768, bias=True)\n","                  (value): Linear(in_features=768, out_features=768, bias=True)\n","                  (dropout): Dropout(p=0.1, inplace=False)\n","                )\n","                (output): BertSelfOutput(\n","                  (dense): Linear(in_features=768, out_features=768, bias=True)\n","                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","                  (dropout): Dropout(p=0.1, inplace=False)\n","                )\n","              )\n","              (intermediate): BertIntermediate(\n","                (dense): Linear(in_features=768, out_features=3072, bias=True)\n","                (intermediate_act_fn): GELUActivation()\n","              )\n","              (output): BertOutput(\n","                (dense): Linear(in_features=3072, out_features=768, bias=True)\n","                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","                (dropout): Dropout(p=0.1, inplace=False)\n","              )\n","            )\n","            (1): BertLayer(\n","              (attention): BertAttention(\n","                (self): BertSelfAttention(\n","                  (query): Linear(in_features=768, out_features=768, bias=True)\n","                  (key): Linear(in_features=768, out_features=768, bias=True)\n","                  (value): Linear(in_features=768, out_features=768, bias=True)\n","                  (dropout): Dropout(p=0.1, inplace=False)\n","                )\n","                (output): BertSelfOutput(\n","                  (dense): Linear(in_features=768, out_features=768, bias=True)\n","                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","                  (dropout): Dropout(p=0.1, inplace=False)\n","                )\n","              )\n","              (intermediate): BertIntermediate(\n","                (dense): Linear(in_features=768, out_features=3072, bias=True)\n","                (intermediate_act_fn): GELUActivation()\n","              )\n","              (output): BertOutput(\n","                (dense): Linear(in_features=3072, out_features=768, bias=True)\n","                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","                (dropout): Dropout(p=0.1, inplace=False)\n","              )\n","            )\n","            (2): BertLayer(\n","              (attention): BertAttention(\n","                (self): BertSelfAttention(\n","                  (query): Linear(in_features=768, out_features=768, bias=True)\n","                  (key): Linear(in_features=768, out_features=768, bias=True)\n","                  (value): Linear(in_features=768, out_features=768, bias=True)\n","                  (dropout): Dropout(p=0.1, inplace=False)\n","                )\n","                (output): BertSelfOutput(\n","                  (dense): Linear(in_features=768, out_features=768, bias=True)\n","                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","                  (dropout): Dropout(p=0.1, inplace=False)\n","                )\n","              )\n","              (intermediate): BertIntermediate(\n","                (dense): Linear(in_features=768, out_features=3072, bias=True)\n","                (intermediate_act_fn): GELUActivation()\n","              )\n","              (output): BertOutput(\n","                (dense): Linear(in_features=3072, out_features=768, bias=True)\n","                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","                (dropout): Dropout(p=0.1, inplace=False)\n","              )\n","            )\n","            (3): BertLayer(\n","              (attention): BertAttention(\n","                (self): BertSelfAttention(\n","                  (query): Linear(in_features=768, out_features=768, bias=True)\n","                  (key): Linear(in_features=768, out_features=768, bias=True)\n","                  (value): Linear(in_features=768, out_features=768, bias=True)\n","                  (dropout): Dropout(p=0.1, inplace=False)\n","                )\n","                (output): BertSelfOutput(\n","                  (dense): Linear(in_features=768, out_features=768, bias=True)\n","                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","                  (dropout): Dropout(p=0.1, inplace=False)\n","                )\n","              )\n","              (intermediate): BertIntermediate(\n","                (dense): Linear(in_features=768, out_features=3072, bias=True)\n","                (intermediate_act_fn): GELUActivation()\n","              )\n","              (output): BertOutput(\n","                (dense): Linear(in_features=3072, out_features=768, bias=True)\n","                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","                (dropout): Dropout(p=0.1, inplace=False)\n","              )\n","            )\n","            (4): BertLayer(\n","              (attention): BertAttention(\n","                (self): BertSelfAttention(\n","                  (query): Linear(in_features=768, out_features=768, bias=True)\n","                  (key): Linear(in_features=768, out_features=768, bias=True)\n","                  (value): Linear(in_features=768, out_features=768, bias=True)\n","                  (dropout): Dropout(p=0.1, inplace=False)\n","                )\n","                (output): BertSelfOutput(\n","                  (dense): Linear(in_features=768, out_features=768, bias=True)\n","                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","                  (dropout): Dropout(p=0.1, inplace=False)\n","                )\n","              )\n","              (intermediate): BertIntermediate(\n","                (dense): Linear(in_features=768, out_features=3072, bias=True)\n","                (intermediate_act_fn): GELUActivation()\n","              )\n","              (output): BertOutput(\n","                (dense): Linear(in_features=3072, out_features=768, bias=True)\n","                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","                (dropout): Dropout(p=0.1, inplace=False)\n","              )\n","            )\n","            (5): BertLayer(\n","              (attention): BertAttention(\n","                (self): BertSelfAttention(\n","                  (query): Linear(in_features=768, out_features=768, bias=True)\n","                  (key): Linear(in_features=768, out_features=768, bias=True)\n","                  (value): Linear(in_features=768, out_features=768, bias=True)\n","                  (dropout): Dropout(p=0.1, inplace=False)\n","                )\n","                (output): BertSelfOutput(\n","                  (dense): Linear(in_features=768, out_features=768, bias=True)\n","                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","                  (dropout): Dropout(p=0.1, inplace=False)\n","                )\n","              )\n","              (intermediate): BertIntermediate(\n","                (dense): Linear(in_features=768, out_features=3072, bias=True)\n","                (intermediate_act_fn): GELUActivation()\n","              )\n","              (output): BertOutput(\n","                (dense): Linear(in_features=3072, out_features=768, bias=True)\n","                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","                (dropout): Dropout(p=0.1, inplace=False)\n","              )\n","            )\n","            (6): BertLayer(\n","              (attention): BertAttention(\n","                (self): BertSelfAttention(\n","                  (query): Linear(in_features=768, out_features=768, bias=True)\n","                  (key): Linear(in_features=768, out_features=768, bias=True)\n","                  (value): Linear(in_features=768, out_features=768, bias=True)\n","                  (dropout): Dropout(p=0.1, inplace=False)\n","                )\n","                (output): BertSelfOutput(\n","                  (dense): Linear(in_features=768, out_features=768, bias=True)\n","                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","                  (dropout): Dropout(p=0.1, inplace=False)\n","                )\n","              )\n","              (intermediate): BertIntermediate(\n","                (dense): Linear(in_features=768, out_features=3072, bias=True)\n","                (intermediate_act_fn): GELUActivation()\n","              )\n","              (output): BertOutput(\n","                (dense): Linear(in_features=3072, out_features=768, bias=True)\n","                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","                (dropout): Dropout(p=0.1, inplace=False)\n","              )\n","            )\n","            (7): BertLayer(\n","              (attention): BertAttention(\n","                (self): BertSelfAttention(\n","                  (query): Linear(in_features=768, out_features=768, bias=True)\n","                  (key): Linear(in_features=768, out_features=768, bias=True)\n","                  (value): Linear(in_features=768, out_features=768, bias=True)\n","                  (dropout): Dropout(p=0.1, inplace=False)\n","                )\n","                (output): BertSelfOutput(\n","                  (dense): Linear(in_features=768, out_features=768, bias=True)\n","                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","                  (dropout): Dropout(p=0.1, inplace=False)\n","                )\n","              )\n","              (intermediate): BertIntermediate(\n","                (dense): Linear(in_features=768, out_features=3072, bias=True)\n","                (intermediate_act_fn): GELUActivation()\n","              )\n","              (output): BertOutput(\n","                (dense): Linear(in_features=3072, out_features=768, bias=True)\n","                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","                (dropout): Dropout(p=0.1, inplace=False)\n","              )\n","            )\n","            (8): BertLayer(\n","              (attention): BertAttention(\n","                (self): BertSelfAttention(\n","                  (query): Linear(in_features=768, out_features=768, bias=True)\n","                  (key): Linear(in_features=768, out_features=768, bias=True)\n","                  (value): Linear(in_features=768, out_features=768, bias=True)\n","                  (dropout): Dropout(p=0.1, inplace=False)\n","                )\n","                (output): BertSelfOutput(\n","                  (dense): Linear(in_features=768, out_features=768, bias=True)\n","                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","                  (dropout): Dropout(p=0.1, inplace=False)\n","                )\n","              )\n","              (intermediate): BertIntermediate(\n","                (dense): Linear(in_features=768, out_features=3072, bias=True)\n","                (intermediate_act_fn): GELUActivation()\n","              )\n","              (output): BertOutput(\n","                (dense): Linear(in_features=3072, out_features=768, bias=True)\n","                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","                (dropout): Dropout(p=0.1, inplace=False)\n","              )\n","            )\n","            (9): BertLayer(\n","              (attention): BertAttention(\n","                (self): BertSelfAttention(\n","                  (query): Linear(in_features=768, out_features=768, bias=True)\n","                  (key): Linear(in_features=768, out_features=768, bias=True)\n","                  (value): Linear(in_features=768, out_features=768, bias=True)\n","                  (dropout): Dropout(p=0.1, inplace=False)\n","                )\n","                (output): BertSelfOutput(\n","                  (dense): Linear(in_features=768, out_features=768, bias=True)\n","                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","                  (dropout): Dropout(p=0.1, inplace=False)\n","                )\n","              )\n","              (intermediate): BertIntermediate(\n","                (dense): Linear(in_features=768, out_features=3072, bias=True)\n","                (intermediate_act_fn): GELUActivation()\n","              )\n","              (output): BertOutput(\n","                (dense): Linear(in_features=3072, out_features=768, bias=True)\n","                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","                (dropout): Dropout(p=0.1, inplace=False)\n","              )\n","            )\n","            (10): BertLayer(\n","              (attention): BertAttention(\n","                (self): BertSelfAttention(\n","                  (query): Linear(in_features=768, out_features=768, bias=True)\n","                  (key): Linear(in_features=768, out_features=768, bias=True)\n","                  (value): Linear(in_features=768, out_features=768, bias=True)\n","                  (dropout): Dropout(p=0.1, inplace=False)\n","                )\n","                (output): BertSelfOutput(\n","                  (dense): Linear(in_features=768, out_features=768, bias=True)\n","                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","                  (dropout): Dropout(p=0.1, inplace=False)\n","                )\n","              )\n","              (intermediate): BertIntermediate(\n","                (dense): Linear(in_features=768, out_features=3072, bias=True)\n","                (intermediate_act_fn): GELUActivation()\n","              )\n","              (output): BertOutput(\n","                (dense): Linear(in_features=3072, out_features=768, bias=True)\n","                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","                (dropout): Dropout(p=0.1, inplace=False)\n","              )\n","            )\n","            (11): BertLayer(\n","              (attention): BertAttention(\n","                (self): BertSelfAttention(\n","                  (query): Linear(in_features=768, out_features=768, bias=True)\n","                  (key): Linear(in_features=768, out_features=768, bias=True)\n","                  (value): Linear(in_features=768, out_features=768, bias=True)\n","                  (dropout): Dropout(p=0.1, inplace=False)\n","                )\n","                (output): BertSelfOutput(\n","                  (dense): Linear(in_features=768, out_features=768, bias=True)\n","                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","                  (dropout): Dropout(p=0.1, inplace=False)\n","                )\n","              )\n","              (intermediate): BertIntermediate(\n","                (dense): Linear(in_features=768, out_features=3072, bias=True)\n","                (intermediate_act_fn): GELUActivation()\n","              )\n","              (output): BertOutput(\n","                (dense): Linear(in_features=3072, out_features=768, bias=True)\n","                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","                (dropout): Dropout(p=0.1, inplace=False)\n","              )\n","            )\n","          )\n","        )\n","        (pooler): BertPooler(\n","          (dense): Linear(in_features=768, out_features=768, bias=True)\n","          (activation): Tanh()\n","        )\n","      )\n","    )\n","  )\n",")\"\n","2022-07-15 17:15:47,272 ----------------------------------------------------------------------------------------------------\n","2022-07-15 17:15:47,273 Corpus: \"Corpus: 3 train + 3 dev + 0 test sentences\"\n","2022-07-15 17:15:47,274 ----------------------------------------------------------------------------------------------------\n","2022-07-15 17:15:47,274 Parameters:\n","2022-07-15 17:15:47,275  - learning_rate: \"0.020000\"\n","2022-07-15 17:15:47,276  - mini_batch_size: \"16\"\n","2022-07-15 17:15:47,276  - patience: \"3\"\n","2022-07-15 17:15:47,277  - anneal_factor: \"0.5\"\n","2022-07-15 17:15:47,279  - max_epochs: \"10\"\n","2022-07-15 17:15:47,279  - shuffle: \"True\"\n","2022-07-15 17:15:47,280  - train_with_dev: \"False\"\n","2022-07-15 17:15:47,280  - batch_growth_annealing: \"False\"\n","2022-07-15 17:15:47,281 ----------------------------------------------------------------------------------------------------\n","2022-07-15 17:15:47,281 Model training base path: \"output/flair_train/artifacts\"\n","2022-07-15 17:15:47,282 ----------------------------------------------------------------------------------------------------\n","2022-07-15 17:15:47,283 Device: cpu\n","2022-07-15 17:15:47,283 ----------------------------------------------------------------------------------------------------\n","2022-07-15 17:15:47,284 Embeddings storage mode: cpu\n","2022-07-15 17:15:47,284 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["/home/vpetukhov/local/miniconda3/envs/interactive_classification/lib/python3.8/site-packages/flair/trainers/trainer.py:64: UserWarning: There should be no best model saved at epoch 1 except there is a model from previous trainings in your training folder. All previous best models will be deleted.\n","  warnings.warn(\n"]},{"name":"stdout","output_type":"stream","text":["2022-07-15 17:15:48,503 epoch 1 - iter 1/1 - loss 0.05037894 - samples/sec: 13.77 - lr: 0.020000\n","2022-07-15 17:15:48,505 ----------------------------------------------------------------------------------------------------\n","2022-07-15 17:15:48,506 EPOCH 1 done: loss 0.0504 - lr 0.020000\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  2.98it/s]"]},{"name":"stdout","output_type":"stream","text":["2022-07-15 17:15:48,846 Evaluating as a multi-label problem: False\n","2022-07-15 17:15:48,858 DEV : loss 0.003526731627061963 - f1-score (micro avg)  1.0\n","2022-07-15 17:15:48,864 BAD EPOCHS (no improvement): 0\n","2022-07-15 17:15:48,865 saving best model\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2022-07-15 17:15:49,454 ----------------------------------------------------------------------------------------------------\n","2022-07-15 17:15:50,608 epoch 2 - iter 1/1 - loss 0.00097065 - samples/sec: 14.40 - lr: 0.020000\n","2022-07-15 17:15:50,611 ----------------------------------------------------------------------------------------------------\n","2022-07-15 17:15:50,612 EPOCH 2 done: loss 0.0010 - lr 0.020000\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  2.90it/s]"]},{"name":"stdout","output_type":"stream","text":["2022-07-15 17:15:50,961 Evaluating as a multi-label problem: False\n","2022-07-15 17:15:50,972 DEV : loss 0.002646335167810321 - f1-score (micro avg)  1.0\n","2022-07-15 17:15:50,978 BAD EPOCHS (no improvement): 0\n","2022-07-15 17:15:50,979 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2022-07-15 17:15:52,077 epoch 3 - iter 1/1 - loss 0.00172945 - samples/sec: 15.16 - lr: 0.020000\n","2022-07-15 17:15:52,080 ----------------------------------------------------------------------------------------------------\n","2022-07-15 17:15:52,081 EPOCH 3 done: loss 0.0017 - lr 0.020000\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  3.15it/s]"]},{"name":"stdout","output_type":"stream","text":["2022-07-15 17:15:52,403 Evaluating as a multi-label problem: False\n","2022-07-15 17:15:52,417 DEV : loss 0.001737547921948135 - f1-score (micro avg)  1.0\n","2022-07-15 17:15:52,423 BAD EPOCHS (no improvement): 0\n","2022-07-15 17:15:52,424 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2022-07-15 17:15:53,770 epoch 4 - iter 1/1 - loss 0.00105890 - samples/sec: 12.40 - lr: 0.020000\n","2022-07-15 17:15:53,774 ----------------------------------------------------------------------------------------------------\n","2022-07-15 17:15:53,775 EPOCH 4 done: loss 0.0011 - lr 0.020000\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  2.82it/s]"]},{"name":"stdout","output_type":"stream","text":["2022-07-15 17:15:54,139 Evaluating as a multi-label problem: False\n","2022-07-15 17:15:54,152 DEV : loss 0.0013952214503660798 - f1-score (micro avg)  1.0\n","2022-07-15 17:15:54,159 BAD EPOCHS (no improvement): 0\n","2022-07-15 17:15:54,162 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2022-07-15 17:15:55,468 epoch 5 - iter 1/1 - loss 0.00073950 - samples/sec: 12.70 - lr: 0.020000\n","2022-07-15 17:15:55,470 ----------------------------------------------------------------------------------------------------\n","2022-07-15 17:15:55,471 EPOCH 5 done: loss 0.0007 - lr 0.020000\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  2.67it/s]"]},{"name":"stdout","output_type":"stream","text":["2022-07-15 17:15:55,851 Evaluating as a multi-label problem: False\n","2022-07-15 17:15:55,864 DEV : loss 0.0011605862528085709 - f1-score (micro avg)  1.0\n","2022-07-15 17:15:55,870 BAD EPOCHS (no improvement): 0\n","2022-07-15 17:15:55,871 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2022-07-15 17:15:57,200 epoch 6 - iter 1/1 - loss 0.00051253 - samples/sec: 12.46 - lr: 0.020000\n","2022-07-15 17:15:57,202 ----------------------------------------------------------------------------------------------------\n","2022-07-15 17:15:57,202 EPOCH 6 done: loss 0.0005 - lr 0.020000\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  3.17it/s]"]},{"name":"stdout","output_type":"stream","text":["2022-07-15 17:15:57,522 Evaluating as a multi-label problem: False\n","2022-07-15 17:15:57,535 DEV : loss 0.001008349470794201 - f1-score (micro avg)  1.0\n","2022-07-15 17:15:57,542 BAD EPOCHS (no improvement): 0\n","2022-07-15 17:15:57,543 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2022-07-15 17:15:58,687 epoch 7 - iter 1/1 - loss 0.00072862 - samples/sec: 14.51 - lr: 0.020000\n","2022-07-15 17:15:58,690 ----------------------------------------------------------------------------------------------------\n","2022-07-15 17:15:58,691 EPOCH 7 done: loss 0.0007 - lr 0.020000\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  2.86it/s]"]},{"name":"stdout","output_type":"stream","text":["2022-07-15 17:15:59,045 Evaluating as a multi-label problem: False\n","2022-07-15 17:15:59,057 DEV : loss 0.0008032931946218014 - f1-score (micro avg)  1.0\n","2022-07-15 17:15:59,063 BAD EPOCHS (no improvement): 0\n","2022-07-15 17:15:59,064 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2022-07-15 17:16:00,313 epoch 8 - iter 1/1 - loss 0.00054428 - samples/sec: 13.27 - lr: 0.020000\n","2022-07-15 17:16:00,316 ----------------------------------------------------------------------------------------------------\n","2022-07-15 17:16:00,317 EPOCH 8 done: loss 0.0005 - lr 0.020000\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  2.71it/s]"]},{"name":"stdout","output_type":"stream","text":["2022-07-15 17:16:00,689 Evaluating as a multi-label problem: False\n","2022-07-15 17:16:00,702 DEV : loss 0.0007176553481258452 - f1-score (micro avg)  1.0\n","2022-07-15 17:16:00,708 BAD EPOCHS (no improvement): 0\n","2022-07-15 17:16:00,710 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2022-07-15 17:16:02,168 epoch 9 - iter 1/1 - loss 0.00045894 - samples/sec: 11.31 - lr: 0.020000\n","2022-07-15 17:16:02,172 ----------------------------------------------------------------------------------------------------\n","2022-07-15 17:16:02,173 EPOCH 9 done: loss 0.0005 - lr 0.020000\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  2.39it/s]"]},{"name":"stdout","output_type":"stream","text":["2022-07-15 17:16:02,596 Evaluating as a multi-label problem: False\n","2022-07-15 17:16:02,610 DEV : loss 0.0006246347329579294 - f1-score (micro avg)  1.0\n","2022-07-15 17:16:02,617 BAD EPOCHS (no improvement): 0\n","2022-07-15 17:16:02,619 ----------------------------------------------------------------------------------------------------\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2022-07-15 17:16:04,042 epoch 10 - iter 1/1 - loss 0.00029327 - samples/sec: 11.68 - lr: 0.020000\n","2022-07-15 17:16:04,044 ----------------------------------------------------------------------------------------------------\n","2022-07-15 17:16:04,046 EPOCH 10 done: loss 0.0003 - lr 0.020000\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  2.68it/s]"]},{"name":"stdout","output_type":"stream","text":["2022-07-15 17:16:04,424 Evaluating as a multi-label problem: False\n","2022-07-15 17:16:04,437 DEV : loss 0.0005788668640889227 - f1-score (micro avg)  1.0\n","2022-07-15 17:16:04,445 BAD EPOCHS (no improvement): 0\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"name":"stdout","output_type":"stream","text":["2022-07-15 17:16:05,211 Test data not provided setting final score to 0\n"]},{"data":{"text/plain":["{'test_score': 0,\n"," 'dev_score_history': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],\n"," 'train_loss_history': [0.05037894348303477,\n","  0.0009706520165006319,\n","  0.0017294536034266155,\n","  0.0010589007288217545,\n","  0.0007394989176342884,\n","  0.0005125256332879266,\n","  0.0007286227773874998,\n","  0.0005442828017597398,\n","  0.0004589410576348503,\n","  0.0002932692295871675],\n"," 'dev_loss_history': [0.003526731627061963,\n","  0.002646335167810321,\n","  0.001737547921948135,\n","  0.0013952214503660798,\n","  0.0011605862528085709,\n","  0.001008349470794201,\n","  0.0008032931946218014,\n","  0.0007176553481258452,\n","  0.0006246347329579294,\n","  0.0005788668640889227]}"]},"execution_count":23,"metadata":{},"output_type":"execute_result"}],"source":["from flair.models import TARSClassifier\n","from flair.trainers import ModelTrainer\n","\n","trainer = ModelTrainer(tars, corpus)\n","\n","trainer.train(base_path='./output/flair_train/artifacts/', # path to store the model artifacts\n","              learning_rate=0.02, # use very small learning rate\n","              mini_batch_size=16,\n","              mini_batch_chunk_size=4, # optionally set this if transformer is too much for your machine\n","              max_epochs=10, # terminate after 10 epochs\n","              )"]},{"cell_type":"code","execution_count":null,"id":"377440a6-8a54-4fb2-8911-a2f02f7b1740","metadata":{"colab":{"referenced_widgets":["a06dc23aca134e899a0e06ef4a97c144"]},"id":"377440a6-8a54-4fb2-8911-a2f02f7b1740","outputId":"8db51710-4037-4c50-d60d-3644a3954c8d"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"a06dc23aca134e899a0e06ef4a97c144","version_major":2,"version_minor":0},"text/plain":["  0%|          | 0/2000 [00:00<?, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"text/plain":["0.167"]},"execution_count":24,"metadata":{},"output_type":"execute_result"}],"source":["samp_ids = np.random.choice(comment_info_split.index, size=2000, replace=False)\n","class_probs = comment_info_split.text[samp_ids].progress_map(lambda t: get_class_score(tars, \"project proposal\", t))\n","(class_probs > 0.75).mean()"]},{"cell_type":"code","execution_count":null,"id":"7c8df2b7-34bc-4261-a107-127f182fcb76","metadata":{"id":"7c8df2b7-34bc-4261-a107-127f182fcb76"},"outputs":[],"source":["labeled_ids = pd.read_sql_query(\"SELECT postId FROM labels\", conn).postId.values\n","closest_ids = get_target_prob_ids(class_probs, target_prob=1.0, n_samples=50, labeled_ids=labeled_ids)"]},{"cell_type":"markdown","id":"4970f834-8180-4686-9e04-e871654b748b","metadata":{"id":"4970f834-8180-4686-9e04-e871654b748b"},"source":["- Can use Colab as a training set\n","- Can contribute GraphQL documentation as our result"]},{"cell_type":"code","execution_count":null,"id":"ee089d03-f1e4-45fd-9b77-5556f4b4e87a","metadata":{"id":"ee089d03-f1e4-45fd-9b77-5556f4b4e87a"},"outputs":[],"source":["comment_info_split.text[closest_ids].reset_index().to_csv(\"./data/to_classify.csv\", index=True)"]}],"metadata":{"kernelspec":{"display_name":"Python 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