set-fit-idea.ipynb

{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2595,"status":"ok","timestamp":1664137723950,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"},"user_tz":-180},"id":"O-BhWSEg3x-N","outputId":"d8b05f21-2f3d-4851-b21d-9154dab9fa32"},"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')"],"id":"O-BhWSEg3x-N"},{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UA0_wkIcJ3Sc","executionInfo":{"status":"ok","timestamp":1664137743440,"user_tz":-180,"elapsed":19496,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}},"outputId":"851174cb-4b01-4425-8e90-79ea30c26b2b"},"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: sentence_transformers in /usr/local/lib/python3.7/dist-packages (2.2.2)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from sentence_transformers) (1.21.6)\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: torchvision in /usr/local/lib/python3.7/dist-packages (from sentence_transformers) (0.13.1+cu113)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from sentence_transformers) (4.64.1)\n","Requirement already satisfied: nltk in /usr/local/lib/python3.7/dist-packages (from sentence_transformers) (3.7)\n","Requirement already satisfied: sentencepiece in /usr/local/lib/python3.7/dist-packages (from sentence_transformers) (0.1.97)\n","Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from sentence_transformers) (1.0.2)\n","Requirement already satisfied: transformers<5.0.0,>=4.6.0 in /usr/local/lib/python3.7/dist-packages (from sentence_transformers) (4.22.1)\n","Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from sentence_transformers) (1.7.3)\n","Requirement already satisfied: huggingface-hub>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from sentence_transformers) (0.9.1)\n","Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from huggingface-hub>=0.4.0->sentence_transformers) (2.23.0)\n","Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from huggingface-hub>=0.4.0->sentence_transformers) (4.12.0)\n","Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub>=0.4.0->sentence_transformers) (21.3)\n","Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub>=0.4.0->sentence_transformers) (4.1.1)\n","Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub>=0.4.0->sentence_transformers) (6.0)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from huggingface-hub>=0.4.0->sentence_transformers) (3.8.0)\n","Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.9->huggingface-hub>=0.4.0->sentence_transformers) (3.0.9)\n","Requirement already satisfied: tokenizers!=0.11.3,<0.13,>=0.11.1 in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=4.6.0->sentence_transformers) (0.12.1)\n","Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=4.6.0->sentence_transformers) (2022.6.2)\n","Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->huggingface-hub>=0.4.0->sentence_transformers) (3.8.1)\n","Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from nltk->sentence_transformers) (1.1.0)\n","Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from nltk->sentence_transformers) (7.1.2)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface-hub>=0.4.0->sentence_transformers) (3.0.4)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface-hub>=0.4.0->sentence_transformers) (2.10)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface-hub>=0.4.0->sentence_transformers) (2022.6.15)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface-hub>=0.4.0->sentence_transformers) (1.24.3)\n","Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->sentence_transformers) (3.1.0)\n","Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.7/dist-packages (from torchvision->sentence_transformers) (7.1.2)\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting git+https://github.com/pmbaumgartner/setfit\n","  Cloning https://github.com/pmbaumgartner/setfit to /tmp/pip-req-build-gmwur_4g\n","  Running command git clone -q https://github.com/pmbaumgartner/setfit /tmp/pip-req-build-gmwur_4g\n","  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n","  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n","    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n","Requirement already satisfied: torch<2.0.0,>=1.10.0 in /usr/local/lib/python3.7/dist-packages (from setfit==0.1.4) (1.12.1+cu113)\n","Requirement already satisfied: sentence-transformers<3.0.0,>=2.1.0 in /usr/local/lib/python3.7/dist-packages (from setfit==0.1.4) (2.2.2)\n","Requirement already satisfied: scikit-learn<2.0.0,>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from setfit==0.1.4) (1.0.2)\n","Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn<2.0.0,>=1.0.1->setfit==0.1.4) (3.1.0)\n","Requirement already satisfied: numpy>=1.14.6 in /usr/local/lib/python3.7/dist-packages (from scikit-learn<2.0.0,>=1.0.1->setfit==0.1.4) (1.21.6)\n","Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn<2.0.0,>=1.0.1->setfit==0.1.4) (1.1.0)\n","Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn<2.0.0,>=1.0.1->setfit==0.1.4) (1.7.3)\n","Requirement already satisfied: nltk in /usr/local/lib/python3.7/dist-packages (from sentence-transformers<3.0.0,>=2.1.0->setfit==0.1.4) (3.7)\n","Requirement already satisfied: torchvision in /usr/local/lib/python3.7/dist-packages (from sentence-transformers<3.0.0,>=2.1.0->setfit==0.1.4) (0.13.1+cu113)\n","Requirement already satisfied: transformers<5.0.0,>=4.6.0 in /usr/local/lib/python3.7/dist-packages (from sentence-transformers<3.0.0,>=2.1.0->setfit==0.1.4) (4.22.1)\n","Requirement already satisfied: huggingface-hub>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from sentence-transformers<3.0.0,>=2.1.0->setfit==0.1.4) (0.9.1)\n","Requirement already satisfied: sentencepiece in /usr/local/lib/python3.7/dist-packages (from sentence-transformers<3.0.0,>=2.1.0->setfit==0.1.4) (0.1.97)\n","Requirement already satisfied: tqdm in 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importlib-metadata->huggingface-hub>=0.4.0->sentence-transformers<3.0.0,>=2.1.0->setfit==0.1.4) (3.8.1)\n","Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from nltk->sentence-transformers<3.0.0,>=2.1.0->setfit==0.1.4) (7.1.2)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers<3.0.0,>=2.1.0->setfit==0.1.4) (2.10)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers<3.0.0,>=2.1.0->setfit==0.1.4) (3.0.4)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers<3.0.0,>=2.1.0->setfit==0.1.4) (1.24.3)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers<3.0.0,>=2.1.0->setfit==0.1.4) (2022.6.15)\n","Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.7/dist-packages (from torchvision->sentence-transformers<3.0.0,>=2.1.0->setfit==0.1.4) (7.1.2)\n"]}],"source":["!pip install sentence_transformers\n","!pip install git+https://github.com/pmbaumgartner/setfit\n"],"id":"UA0_wkIcJ3Sc"},{"cell_type":"code","execution_count":3,"metadata":{"id":"iXDpMwBjVkRG","executionInfo":{"status":"ok","timestamp":1664137748173,"user_tz":-180,"elapsed":4740,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}}},"outputs":[],"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 torch\n","import random\n","\n","import json\n","from pandas import Series, DataFrame\n","import pandas as pd\n","from tqdm.notebook import tqdm\n","import numpy as np\n","import gzip\n","\n","from transformers import pipeline\n","import sqlite3 as db\n","from setfit import SetFitClassifier\n","from random import choice, randint, shuffle\n","\n","\n","from pathlib import Path\n","data_dir_pref = Path('/content/drive/MyDrive/WORK/data') \n","\n","\n","\n","def test_model_saved(name_model_saved, texts):\n","  clf = clf_test = SetFitClassifier.load(name_model_saved)\n","  selected = clf.predict(texts)\n","  return selected\n","\n","\n","\n","tqdm.pandas()\n"],"id":"iXDpMwBjVkRG"},{"cell_type":"code","execution_count":4,"metadata":{"id":"Rczk5PH2d_Uc","executionInfo":{"status":"ok","timestamp":1664137748174,"user_tz":-180,"elapsed":13,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}}},"outputs":[],"source":["\"\"\" Prepare dataset for train: 20 samples \"\"\"\n","\n","def read_db_to_df():\n","  \"\"\" Чтение размеченных текстов из БД в датафрейм \"\"\"\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.sort_values(\"label\", ascending=False).head()\n","  return train_df\n"],"id":"Rczk5PH2d_Uc"},{"cell_type":"code","source":["data_df = read_db_to_df()\n","index = data_df.sort_values(\"label\", ascending=False).index.to_list()\n","dataset = list(zip(data_df.text[index].to_list()[:20], data_df.label[index].to_list()))\n","shuffle(dataset)\n","docs = [i[0] for i in dataset] \n","labels = [i[1] for i in dataset]"],"metadata":{"id":"XZQ-Uw84Lv5w","executionInfo":{"status":"ok","timestamp":1664137748175,"user_tz":-180,"elapsed":11,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}}},"id":"XZQ-Uw84Lv5w","execution_count":5,"outputs":[]},{"cell_type":"code","source":["\"\"\" train model \"\"\"\n","\n","clf = SetFitClassifier('cross-encoder/quora-distilroberta-base')  #'sentence-transformers/all-mpnet-base-v1')   \n","clf.fit(docs, labels) "],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":687,"referenced_widgets":["3080a34290404659b1fac555f7fa757e","6aaa0062e5e142f1827308e090b5fc82","ccce4c1c28a447b3b50a72550184189e","7a3da027266847d6b78295101c1f3b90","649a94aeb5474f60bce02dab85b73ce9","f37c9339fa98451e97e31b9e6d333168","f30b7489ff874cfe8b61d70445119f36","a402c42ff0d24c67ba1a9f99f1e977f7","d13f508bc1974299ace759710c6b3556","a1ebecf36af2426a8c4ce22c0e6d583c","38b92b34f9c046868efdc1f931bea0fd","4761553c72804824887220802f4ddf15","ecb94b41c0554c67849fdc8d0ebb3337","58498e4a330f41c998a1165904a56742","2636175e34b540e9b2cfe1ee17a393a3","1ae0c782dbaf46d5abb0c5e5575902e2","9f745e021f8b4eae95ce24633d04a07e","946c7c3c3c214bb59ee6b77be38f9e6f","f5c40bc2dae04e7e8846fcfa3f7dff5a","c390fc22502341d8b748efcedfaa3327","299e630797394a1f943c1c8e613f187d","93dd6d2f026a44e4947f7947e3941b55","8e695adf6daa4a59b9ed0e14148613ca","36e40d412c6c434daf8cb402ed6e3a3e","845a98bf7ff44cd1831df72d1d43468d","23e1f84011bf4bb3801b7856c8a64085","b2f1aa310b6d49e89362e9113d70656c","0d81600df4d64a29970bae86f2e00436","d8900afb192c44e6ac8cf80e337c2b9c","62d8d7900342440fbb130490833d2287","044776c8d81a4736a9a3d3ea7c48266c","07fa5850577d48a6ad60728cd02db0ab","2d03fd0b48f6452c88e5a4ea72ce798d","d486deb3ae544c3094806e95110c1ba1","128229367fd444b8a646e3935457c08b","55dc16e8ea8a435fa5ffad2a907ca829","375d5843eda049fe8d3ff3abe866305f","cbe71aeb3b204c9d8c6bbc65509b11ed","7db173b2d32d425da176a888f334854c","cb11586382f9413282d50417d5e41751","38b6b9e6636e4a3db9a7cba7b458d9ff","7683ae9834e8497aafaf9c818ca08b39","a7eaa75523d740668b1d6159a2553bcc","e5d6136a02cb44f881dbd79e7fafdab8","aa52a62245294aa8a93af3768c5c8937","16732256ffb44183aab825023ac1af2a","e855bb2c797d42e6af57da26202a0310","5ab591bb99404837a61d6117e803de65","1cec9c8b3aba40e38a6ee998306db658","6ce4eed70f5c4db687630aafc7943fb2","898824599004486db013a9385a4a81b9","4fad8a41285a419884022a7e9c7e7859","16683b7ebdd24373b570bf1ea8369a08","0dde683c26bc4fa2a0bb218c69448a17","6a61680e2e894b6d91367b2778f578d0","28d4f94e9528430ab50af65df26ad137","31424e2f2900481d8dbb91ea68b28280","7f54b8a1333c4a8f81b2c1349fdc40e1","d1c5f89e2a0f476ea853a8112dace964","78ade2a529bf497bb0dce5f23b8ce77f","9c367ea0206a4c159d4642f8e072c230","ba50110a114443e3b947633f9e9686e8","ee13e2a0c8a7406096bcda8f4d5dd1aa","d0011e5cd48445ea9eeea9150b7d77e9","0ff9efe69a6c440f9ffca3399e2ec119","d60642d9a0d34bcabeda53450eb4aef3","67a1713d4d6f47c78bb2b7a8030fed03","3eda60f4388f4df996d6db579628e033","13cd03169c1449baa7387817978107c3","f4a7ed0d83eb402b81b63c816f285ba1","510be06ef520400e9bd1024bd0903dd1","44229d06f65b4a5aadf034476796df7b","74d8b7c1ecf0446fa22fa3ee7ddfaf14","5c3a32cb0cec4988807474301e26d92a","c9973330adab474bbaeb3492562d5d54","76dd150f450f43c48256f55ee56bdf28","babbc04e03de4da8be6faff58a5ecfd9","fb8fb7562d464726bc7fdc34cf9b5a80","5762b52197944981a31af9dfdfab3b63","246c46467df045269e1a9d0fc4e60f88","d81ffab3949f429cb844263bfd51841a","de2bf26d88584760ad280a1f40c5b2bf","0a74abca0cc14fa789d751e9d641ee3f","d9bf03728e4b4b6fa23b077194ba3283","49a7315c99b74fc8b79ef3bbb04b572b","1b1efe815e144d67831af853533a2f39","59cdfe0322ac4ee5b1ac3a8c0f22af04","272ca56ee5cd49b9b46ea6882f616926","72367ab5794d489f8cc0d8ed0bba95a6","451de69569a64307a1ae93f86efe79fc","045af2caa59a4525bde7384d347e16ef","4ffc3ff5657d4ac8a528c14c1f63b330","e2d9253f3720457dba5587e12f822620","063cd0428a3a48218de23009bb7e6c77","092e36f01cab493ab5e337909ca90de1","e74d0f535ca0401284e8492102b14950","4319c525e84a44e5b2e95b17f6d3655a","dbd2d3de5d9a487cba440ab409df3204","197550b35c1347f89c5bd2e2e57fcf2b","fbe7b97e4e9144e78989cebc951fc409","4d306ea45b8645b5aa0bec6e68d8bd48","aa47a6ea432041cfa92ef07f1e0589b7","f54f6e5be5a44f1789e7dce0e696858d","511dded121014510ba0a29f2605ce1b0","32d84fa359a046a899de747b692d000b","06844d98c5414a3ba748596b02b9a62e","a050c85d76e74810880d4ea678d9d1af","4bc0d03c2941481a844b887d9d7386f0","2c3f5416b5ba4836b527c359b4d49181","ec1fcbff87f04b3390d5e484d87e1f44","92f3e45ce62b4b3ba019c566c56e4bcd","0c53a18618814285b002072ec4adcca8","2a0f4d81c7c5451fa56f9f97ed602b28","4c1ff4955df4462d8adf648623ed1287","dc3e95301efd410eaafeaebb9aa2ab1b","18c57d7ade644fa68bccd9132b36ae17","eb9693b27a644b70a86a026a4640de89","4d37c939f62c46278edf15b806dc4930","8bc1558a2ee34cdc98663d8abfd43979","be90337853474fad9ef14fbd6916ad7f","d2d5561798fb4b42826fcbd1e3635df4"]},"id":"w4tXJhrAKUdl","executionInfo":{"status":"ok","timestamp":1664137781110,"user_tz":-180,"elapsed":32945,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}},"outputId":"4888321f-0a42-4e21-89c9-1630f8f22e37"},"id":"w4tXJhrAKUdl","execution_count":6,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/391 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"3080a34290404659b1fac555f7fa757e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/2.07k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4761553c72804824887220802f4ddf15"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/1.07k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8e695adf6daa4a59b9ed0e14148613ca"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/607 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d486deb3ae544c3094806e95110c1ba1"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/456k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"aa52a62245294aa8a93af3768c5c8937"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/329M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"28d4f94e9528430ab50af65df26ad137"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/772 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"67a1713d4d6f47c78bb2b7a8030fed03"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|        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Creating a new one with MEAN pooling.\n","Some weights of the model checkpoint at /root/.cache/torch/sentence_transformers/cross-encoder_quora-distilroberta-base were not used when initializing RobertaModel: ['classifier.out_proj.weight', 'classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias']\n","- This IS expected if you are initializing RobertaModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n","- This IS NOT expected if you are initializing RobertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n","Some weights of RobertaModel were not initialized from the model checkpoint at /root/.cache/torch/sentence_transformers/cross-encoder_quora-distilroberta-base and are newly initialized: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"]},{"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":"fbe7b97e4e9144e78989cebc951fc409"}},"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":"92f3e45ce62b4b3ba019c566c56e4bcd"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n"]}]},{"cell_type":"code","execution_count":7,"metadata":{"id":"pjbpRPGZIKxO","executionInfo":{"status":"ok","timestamp":1664137792224,"user_tz":-180,"elapsed":543,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}}},"outputs":[],"source":["\"\"\" Prepare dataset for eval of new model \"\"\"\n","\n","shuffle(dataset)\n","docs_eval = [i[0] for i in dataset] \n","labels_eval = [i[1] for i in dataset]\n"],"id":"pjbpRPGZIKxO"},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":353,"status":"ok","timestamp":1664137794473,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"},"user_tz":-180},"id":"Eb1k-D8MIK0X","outputId":"6362638d-91e5-4162-aabc-90e16f644604"},"outputs":[{"output_type":"stream","name":"stdout","text":["[0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0]\n","[0 1 0 0 1 1 1 1 0 0 0 1 0 1 0 0 0 0 0 0]\n","accuracy= 0.95\n"]}],"source":["\"\"\" eval of new model \"\"\"\n","\n","y_pred_eval = clf.predict(docs_eval)\n","\n","print(labels_eval)\n","print(y_pred_eval)\n","print('accuracy=', accuracy_score(labels_eval, y_pred_eval))"],"id":"Eb1k-D8MIK0X"},{"cell_type":"code","execution_count":9,"metadata":{"id":"vyVLJHCpILDU","executionInfo":{"status":"ok","timestamp":1664137811799,"user_tz":-180,"elapsed":13551,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}}},"outputs":[],"source":["\"\"\" save set-fit model \"\"\"\n","\n","path_model = '/content/drive/MyDrive/WORK/models/setfit_20.mdl'\n","clf.save(path_model)\n"],"id":"vyVLJHCpILDU"},{"cell_type":"markdown","source":["**ЗАГРУЗКА И ИСПОЛЬЗОВАНИЕ FT МОДЕЛИ **"],"metadata":{"id":"2e2MFzMFOJjT"},"id":"2e2MFzMFOJjT"},{"cell_type":"code","execution_count":10,"metadata":{"id":"rfhMXIP30qxg","executionInfo":{"status":"ok","timestamp":1664137825630,"user_tz":-180,"elapsed":541,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}}},"outputs":[],"source":["\"\"\" prepare data for using saved set-fit model\"\"\"\n","\n","path_model = '/content/drive/MyDrive/WORK/models/setfit_20.mdl'\n"],"id":"rfhMXIP30qxg"},{"cell_type":"code","execution_count":11,"metadata":{"id":"4-v2u64ssEMc","executionInfo":{"status":"ok","timestamp":1664137831451,"user_tz":-180,"elapsed":2269,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}}},"outputs":[],"source":["\"\"\" Подготовка набора из ппоследних 100 текстов комментариев \"\"\"\n","\n","name_texts_file = Path.joinpath(data_dir_pref, 'all_comments_texts.txt')\n","with open(name_texts_file, 'r') as file:\n","   texts = file.readlines()[:100]\n"],"id":"4-v2u64ssEMc"},{"cell_type":"code","execution_count":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"zsRDkBrO0q4G","outputId":"f44ad817-e2bc-4a91-b603-cf4136e78ba3","executionInfo":{"status":"ok","timestamp":1664137837832,"user_tz":-180,"elapsed":3464,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["23\n"]}],"source":["\"\"\" using saved set-fit model\"\"\"\n","\n","index_texts_selected = test_model_saved(path_model, texts)\n","print(sum(index_texts_selected))\n"],"id":"zsRDkBrO0q4G"},{"cell_type":"code","execution_count":13,"metadata":{"id":"D0hGfOOK3WHt","executionInfo":{"status":"ok","timestamp":1664137845603,"user_tz":-180,"elapsed":333,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}}},"outputs":[],"source":["\"\"\" get selected texts \"\"\"\n","\n","\n","mask = np.array(index_texts_selected, dtype=bool)\n","text_selected = list(np.array(texts)[mask])\n"],"id":"D0hGfOOK3WHt"},{"cell_type":"code","source":["text_selected"],"metadata":{"id":"2OnJcO79utH6","executionInfo":{"status":"ok","timestamp":1664137851295,"user_tz":-180,"elapsed":562,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}},"outputId":"27a403b6-01e4-46ff-b592-c008590499b8","colab":{"base_uri":"https://localhost:8080/"}},"id":"2OnJcO79utH6","execution_count":14,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[\"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.\\n\",\n"," \"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.\\n\",\n"," 'FWIW, I wouldn\\'t say I\\'m \"dumb,\" but I dropped out of a University of Minnesota counseling psychology undergrad degree and have spent my entire \"EA\" career (at MIRI then Open Phil) working with people who are mostly very likely smarter than I am, and definitely better-credentialed. And I see plenty of posts on EA-related forums that require background knowledge or quantitative ability that I don\\'t have, and I mostly just skip those. Sometimes this makes me insecure, but mostly I\\'ve been able to just keep repeating to myself something like \"Whatever, I\\'m excited about this idea of helping others as much as possible, I\\'m able to contribute in various ways despite not being able to understand half of what Paul Christiano says, and other EAs are generally friendly to me.\" A couple things that have been helpful to me: comparative advantage and stoic philosophy. At some point it would also be cool if there was some kind of regular EA webzine that published only stuff suitable for a general audience, like The Economist or Scientific American but for EA topics.\\n',\n"," 'After reflecting further and talking to people I changed \"track\" in the title to \"camp\"; I think this more accurately conveys the point I\\'m making.\\n',\n"," \"Books that have significantly reshaped my thinking in a useful way since I got into EA that are EA or adjacent both in authorship and in content, and that I therefore find myself recommending to EAs all the time: Moral Tribes (delighted to see it on here) Hidden Games by Yoeli and Hoffman Elephant in the Brain by Simler and Hanson (and relatedly I'm somewhat surprised to not see Age of Em in the AI section; I haven't read it yet but it's one of the books that EA Books Direct gives out, if I'm not mistaken)\\n\",\n"," 'This post is a response to the thesis of Jan Brauner’s post The Expected Value of Extinction Risk Reduction Is Positive.The post is by Jan Brauner AND Friederike Grosse-Holz. I think correcting this is particularly important because the EA community struggles with gender diversity, so dropping the female co-author is extra bad.\\n',\n"," \"Isn't there a strong motivation for following-up with a conference introducing these professors to EA?\\n\",\n"," \"Hey, I just wanted to say I'm glad you posted this. It made me laugh and I'm glad you've voiced something that's probably pretty common but rarely talked about - I appreciate that.\\n\",\n"," \"Glad to hear. I skimmed your post, and felt it added a lot of value to mine as it highlighted a big difference between 'ops roles' in an operation - i.e. keeping the show on the road - Vs. projects - i.e. starting up a new show and running it at the same time. Very different skillsets again, because of difference levels of uncertainty; one more about optimising, one more about figuring out what the hell do we do? I think the ops skillset I set out is much more project-oriented, so would welcome more of your critque of hiring approaches from a more ops-y less project-y perspective.\\n\",\n"," 'I kinda suspect no ethics are future-proof in this senseWhich sense do you mean?I like Holden\\'s description:I expect some readers will be very motivated by something like \"Making ethical decisions that I will later approve of, after I\\'ve done more thinking and learning,\" while others will be more motivated by something like \"Making ethical decisions that future generations won\\'t find abhorrent.\"Personally I\\'m thinking more of the former reason than the latter reason. I think \"things I\\'d approve of after more thinking and learning\" is reasonably precise as a definition, and seems pretty clearly like a thing that exists and can be approximated.\\n',\n"," 'I posed a puzzle in Is the potential astronomical waste in our universe too small to care about?, which seems to arise in any model of moral uncertainty where the representatives of different moral theories can trade resources, power, or influence with each other, like here. (Originally my post assumed a Moral Parliament where the delegates can trade votes with each other.) (There was some previous discussion of my puzzle on this forum, when the Greaves and Cotton-Barratt preprint came out.)\\n',\n"," \"Very nice! I'm curious whether this type of analysis could shed some light on the meat-eater problem\\n\",\n"," 'Thank you Dony, Denis, and Matt! I really enjoyed reading this post, and excited about the idea. Looking forward to seeing what posts are submitted!\\n',\n"," \"Because of this heavy tailed distribution of interventionsIs it actually heavy-tailed? It looks like an ordered bar chart, not a histogram, so it's hard to tell what the tails are like.\\n\",\n"," 'Zach and Kelly Weinersmith are writing a book on space settlement. Might be worth reaching out to them.\\n',\n"," '\"The Bay Area\" or \"The Bay\" is I think a totally normal abbreviation for the \"San Francisco Bay Area\", see e.g. this wikitravel article. I\\'ve used this abbreviation with lots of people outside of the EA/Rationality community, and it seems to be commonly understood.\\n',\n"," \"See also Benjamin Todd's post: Let's stop saying 'funding overhang' - EA Forum (effectivealtruism.org)\\n\",\n"," \"For what it's worth, consider the claim “The Judeo-Christian God, the one who listens to prayers and so on, doesn't exist.” I have such high confidence in this claim that I would absolutely state it as a fact without hedging, and psychoanalyze people for how they came to disagree with me. Yet there's a massive theology literature arguing to the contrary of that claim, including by some very smart and thoughtful people, and I've read essentially none of this theology literature, and if you asked me to do an anti-atheism ITT I would flunk it catastrophically.I'm not sure what lesson you'll take from that; for all I know you yourself are very religious, and this anecdote will convince you that I have terrible judgment. But if you happen to be on the same page as me, then maybe this would be an illustration of the fact that (I claim) one can rationally and correctly arrive at extremely-confident beliefs without it needing to pass through a deep understanding and engagement with the perspectives of the people who disagree with you.I agree that this isn’t too important a conversation, it’s just kinda interesting. :)\\n\",\n"," 'Un equilibrio inadecuado (Spotify - Apple Podcasts - Google Podcasts)Interviews in Spanish on EA topics. I particularly enjoyed the episode with Andrés Gómez Emilsson from Qualia Research Institute. Sadly, no new content since October 2021.\\n',\n"," 'I’m definitely not the best person to give feedback on this, but I’ll just briefly share a few thoughts: I’ve heard that EA grant makers often have relatively little time to review grant applications. This may or may not be true for EAIF, but supposing it is, even yellow flags like that offset article might cause a reviewer to quickly become pessimistic about providing grants (for some of the following reasons). I would have recommended not using the example of rape; murder offsets probably would have been a better alternative. I only skimmed the post, but it really didn’t help that towards the beginning you make the seemingly-intentionally-controversially phrased point about “[Sometimes rape is permissible… you probably agree deep down. That is, if it is to prevent more rape.]” This ordering (saying “you probably agree” before clarifying “if it were in some twisted trolley problem scenario”) and phrasing (e.g., “deep down…”) is needlessly controversy-inviting. To be honest, to me these kinds of details genuinely do reflect some lack of perspective/room-reading or rhetorical finesse, regardless of whether you ultimately oppose the idea of rape offsets. (It also very much gives me flashbacks to the infamous Robin Hanson post, which really hurt his reputation and reflected a similar lack of perspective…) This may not be such a problem if I am personally evaluating your character, but: Grant making probably is justified for being cautious about downside risks, including when it comes to optics risks. “EA grant makers fund writer of blog that callously discusses ‘rape offsets’” might be a very unfair social media characterization, but fairness doesn’t really matter, and I can’t be confident it won’t get pulled into some broader narrative attacking—however fairly or unfairly—EA overall. (Speaking as someone who’s never analyzed grant applications) I suspect you would have to have a really good case for potential upside to make it worth spending a few extra hours analyzing those optics risks, and in the end there may (or may not?) be plenty of other people to fund instead. As for your overall blog, I haven’t read it, but I wouldn’t be surprised if it is otherwise good, and I’m glad to see a blog discussing moral issues. But rape is a topic that needs to be treated with a lot of care and caution, and probably should be avoided when it is just being used to make a point separate from rape.\\n',\n"," 'Sort of, so firstly I have a field next to each prediction that automatically computes its \"bucket number\" (which is just FLOOR(<prediction> * 10)). To then get the average probability of a certain bucket, I run the following: =AVERAGE(INDEX(FILTER(C$19:K, K$19:K=A14), , 1)) - note that this is google sheets though and I\\'m not sure to which degree this transfers to Excel. For context, column C contains my predicted probabilities, column K contains the computed bucket numbers, and A14 here is the bucket for which I\\'m computing this. Similarly I count the number of predictions of a given bucket with =ROWS(FILTER(K$19:K, K$19:K<>\"\", K$19:K=A14)) and the ratio of predictions in that bucket that ended up true with =COUNTIF(FILTER(D$19:K, K$19:K=A14), \"=1\") / D14 (D19 onwards contains 1 and 0 values depending on if the prediction happened or not; D14 is the aforementioned number of predictions in that bucket).If this doesn\\'t help, let me know and I can clear up one such spreadsheet, see if I can export it as xlsx file and send it to you.\\n',\n"," \"The Lunar Society(I haven't listened yet and am not yet able to recommend it, but it seems EA-relevant)\\n\",\n"," \"Did you factor in the length of the current programs vs. undergraduate programs that would replace them? For example here in Israel an MD is an undergraduate program, but it takes 7 years to complete. There are some programs for people with an existing undergraduate degree, and those take 4 years (Edit: maybe 5) to complete. So it's only a loss of 1 year (Edit: maybe 2) or even 0 years, depending on which undergraduate degrees they accept.\\n\"]"]},"metadata":{},"execution_count":14}]},{"cell_type":"code","execution_count":16,"metadata":{"id":"EcfFnsFm2UpH","executionInfo":{"status":"ok","timestamp":1664137881459,"user_tz":-180,"elapsed":365,"user":{"displayName":"Viktor Petukhov","userId":"15611477980477129608"}}},"outputs":[],"source":["\"\"\" save results - selected texts to file \"\"\"\n","\n","\n","name_file_tests_selected = Path.joinpath(data_dir_pref, 'texts_model_selected_from_0_100_comments.txt')\n","\n","with open(name_file_tests_selected, 'w') as file:\n","  file.writelines([r+'\\n\\n' for r in 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