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Upload model_training_and_testing.ipynb
Browse files- model_training_and_testing.ipynb +1241 -0
model_training_and_testing.ipynb
ADDED
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1 |
+
{
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2 |
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"cells": [
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3 |
+
{
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4 |
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"cell_type": "markdown",
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5 |
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"metadata": {
|
6 |
+
"id": "yViS84WpSENo"
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},
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"source": [
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9 |
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"### Sentiment analysis on twitter dataset\n",
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"\n",
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11 |
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"Возьмем модель `sentiment-roberta-large-english` и дообучим ее на датасете твитов `carblacac/twitter-sentiment-analysis`."
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]
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},
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{
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15 |
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
|
18 |
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"id": "X-X7InJOGwT_"
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},
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"outputs": [],
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"source": [
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"#!streamlit run /home/theodore/anaconda3/envs/ds/lib/python3.10/site-packages/ipykernel_launcher.py"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"colab": {
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30 |
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"base_uri": "https://localhost:8080/"
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},
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32 |
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"id": "RKHGyhG9G6WF",
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"outputs": [
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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"Requirement already satisfied: transformers in /usr/local/lib/python3.9/dist-packages (4.28.1)\n",
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"Requirement already satisfied: datasets in /usr/local/lib/python3.9/dist-packages (2.11.0)\n",
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"Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.9/dist-packages (from aiohttp->datasets) (1.3.1)\n",
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"Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.9/dist-packages (from huggingface-hub<1.0,>=0.11.0->transformers) (4.5.0)\n",
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"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (2022.12.7)\n",
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"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (1.26.15)\n",
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"Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.9/dist-packages (from pandas->datasets) (2.8.2)\n",
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"Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.9/dist-packages (from pandas->datasets) (2022.7.1)\n",
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"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.9/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n"
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]
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}
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],
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"source": [
|
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"!pip install transformers datasets"
|
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"id": "ohaBadE4GwUA"
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},
|
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"outputs": [],
|
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"source": [
|
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+
"# import json\n",
|
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+
"\n",
|
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+
"# with open(\"arxivData.json\", 'r') as f:\n",
|
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"# arxiv_data = json.load(f)\n",
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"\n",
|
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"# arxiv_data[0]['tag']"
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]
|
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"id": "HAP_PN0iGwUB"
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},
|
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+
"outputs": [],
|
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"source": [
|
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"from datasets import load_dataset\n",
|
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+
"import transformers\n",
|
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+
"import pandas as pd\n",
|
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+
"import numpy as np"
|
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 86,
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"referenced_widgets": [
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"06de297475874730a793d3255cfe9a0d",
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"1a192819128a429b96c571f48c7de552",
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"4e50c449ee2047148e35c76fddf38e08",
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"12483d6de04546feb5ee3b99ede894dc",
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"6bc412e1b7c04160b58f83db5841d255",
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+
"8e44d4f0218f4c43bb28a22a1b0472f7",
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"9b9e621263e24472be473d87b2d54db5",
|
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+
"f4947eda1c18457a80aa7c68df79d32a",
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"11585997b0494e3db0f0121538e4f9f4",
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"dc511eb900064df8a10177ba5ea1e7ff",
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"2973a9eaa7f34f70bb02d4ffaea67462"
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]
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},
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"id": "ctTETRGcGwUC",
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"outputId": "49c41fe7-acc0-4678-d257-aac1502edc72"
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},
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"outputs": [
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{
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"name": "stderr",
|
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"output_type": "stream",
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"text": [
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"Found cached dataset twitter-sentiment-analysis (/home/theodore/.cache/huggingface/datasets/carblacac___twitter-sentiment-analysis/default/1.0.0/cd65e23e456de6a4f7264e305380b0ffe804d6f5bfd361c0ec0f68d8d1fab95b)\n"
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
|
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"model_id": "ebd17ca95bce4cc3b9d0f9597a5d9a91",
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"version_major": 2,
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"version_minor": 0
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"text/plain": [
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"metadata": {},
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"output_type": "display_data"
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}
|
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],
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"source": [
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"data = load_dataset(\"carblacac/twitter-sentiment-analysis\")"
|
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
|
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"metadata": {
|
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+
"colab": {
|
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+
"base_uri": "https://localhost:8080/"
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+
},
|
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"id": "HRevJqXIGwUC",
|
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"outputId": "87eb5fc6-817f-4abe-c34d-57320c91229f"
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},
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"outputs": [
|
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{
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"data": {
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"text/plain": [
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"DatasetDict({\n",
|
177 |
+
" train: Dataset({\n",
|
178 |
+
" features: ['text', 'feeling'],\n",
|
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+
" num_rows: 119988\n",
|
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+
" })\n",
|
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+
" validation: Dataset({\n",
|
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+
" features: ['text', 'feeling'],\n",
|
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+
" num_rows: 29997\n",
|
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+
" })\n",
|
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+
" test: Dataset({\n",
|
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+
" features: ['text', 'feeling'],\n",
|
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+
" num_rows: 61998\n",
|
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+
" })\n",
|
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+
"})"
|
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+
]
|
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},
|
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"execution_count": 3,
|
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"metadata": {},
|
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"output_type": "execute_result"
|
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}
|
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],
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"source": [
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"data"
|
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|
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},
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{
|
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"cell_type": "code",
|
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+
"execution_count": 4,
|
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"metadata": {
|
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+
"id": "CAUZ4zWgGwUD"
|
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+
},
|
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+
"outputs": [],
|
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+
"source": [
|
209 |
+
"# tweets_df = pd.read_csv(\"twitter_sentiment.csv\", names=[\"target\", \"ids\", \"date\", \"flag\", \"user\", \"text\"],\n",
|
210 |
+
"# encoding='utf-8', encoding_errors='ignore')\n",
|
211 |
+
"# tweets_df.head()\n",
|
212 |
+
"# #tweets_df.shape"
|
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+
]
|
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+
},
|
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{
|
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"cell_type": "code",
|
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"execution_count": 5,
|
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"metadata": {
|
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+
"colab": {
|
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"base_uri": "https://localhost:8080/",
|
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"height": 72,
|
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"referenced_widgets": [
|
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"5e4bdf9e5c8347159c83772d892f2e4f",
|
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"dfc20d27805d4ba9a388cfa24384285e",
|
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+
"de9e434a5192455ab2882dff5f0ed3d9",
|
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+
"c41e383cd66949e7979a9f83c932c3bf",
|
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+
"af522a70d4e04cfa8bdf5807cf30153b",
|
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+
"330be1100e454f9da15ad9553bfaf4a2",
|
229 |
+
"f815ae8495f44970b648c942d2ac11c0",
|
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+
"6018eca5deaf41858ce694efc6b3624d",
|
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+
"51291a2ef41f4e9fbe68d1e4151fb4e2",
|
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+
"ec6d9ab9eda24231b577c75136f902f8",
|
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+
"7fa05036dfb345898c06ecb09dca0363"
|
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+
]
|
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+
},
|
236 |
+
"id": "utD3wZZLGwUE",
|
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+
"outputId": "8c7db1ba-21f4-48cb-d89f-95b286bc8ee7"
|
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+
},
|
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+
"outputs": [
|
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+
{
|
241 |
+
"name": "stderr",
|
242 |
+
"output_type": "stream",
|
243 |
+
"text": [
|
244 |
+
"Loading cached processed dataset at /home/theodore/.cache/huggingface/datasets/carblacac___twitter-sentiment-analysis/default/1.0.0/cd65e23e456de6a4f7264e305380b0ffe804d6f5bfd361c0ec0f68d8d1fab95b/cache-798641a24e025595.arrow\n",
|
245 |
+
"Loading cached processed dataset at /home/theodore/.cache/huggingface/datasets/carblacac___twitter-sentiment-analysis/default/1.0.0/cd65e23e456de6a4f7264e305380b0ffe804d6f5bfd361c0ec0f68d8d1fab95b/cache-5ac250acc1a0a444.arrow\n",
|
246 |
+
"Loading cached processed dataset at /home/theodore/.cache/huggingface/datasets/carblacac___twitter-sentiment-analysis/default/1.0.0/cd65e23e456de6a4f7264e305380b0ffe804d6f5bfd361c0ec0f68d8d1fab95b/cache-6ee382dff31af9fb.arrow\n"
|
247 |
+
]
|
248 |
+
}
|
249 |
+
],
|
250 |
+
"source": [
|
251 |
+
"from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoModel, Trainer, TrainingArguments, LineByLineTextDataset\n",
|
252 |
+
"from transformers import pipeline\n",
|
253 |
+
"\n",
|
254 |
+
"# classifier = pipeline('sentiment-analysis', model=\"siebert/sentiment-roberta-large-english\")\n",
|
255 |
+
"\n",
|
256 |
+
"\n",
|
257 |
+
"# print(\"Preparing the training data...\")\n",
|
258 |
+
"# # dataset = LineByLineTextDataset(\n",
|
259 |
+
"# # file_path=<MAKE_YOUR_DATA_HERE>, tokenizer=tokenizer, block_size=128)\n",
|
260 |
+
"\n",
|
261 |
+
"tokenizer = AutoTokenizer.from_pretrained(\"siebert/sentiment-roberta-large-english\")\n",
|
262 |
+
"model = AutoModelForSequenceClassification.from_pretrained(\"siebert/sentiment-roberta-large-english\", num_labels=2)\n",
|
263 |
+
"\n",
|
264 |
+
"# print(\"Dataset ready!\")\n",
|
265 |
+
"dataset = data.map(lambda xs: tokenizer(xs[\"text\"], truncation=True, padding='max_length'))"
|
266 |
+
]
|
267 |
+
},
|
268 |
+
{
|
269 |
+
"cell_type": "code",
|
270 |
+
"execution_count": 10,
|
271 |
+
"metadata": {
|
272 |
+
"id": "J2Q492BsOedY"
|
273 |
+
},
|
274 |
+
"outputs": [],
|
275 |
+
"source": [
|
276 |
+
"# model.to('cpu');"
|
277 |
+
]
|
278 |
+
},
|
279 |
+
{
|
280 |
+
"cell_type": "code",
|
281 |
+
"execution_count": 7,
|
282 |
+
"metadata": {
|
283 |
+
"id": "xRYsJ1KTGwUF"
|
284 |
+
},
|
285 |
+
"outputs": [],
|
286 |
+
"source": [
|
287 |
+
"from datasets import ClassLabel\n",
|
288 |
+
"\n",
|
289 |
+
"dataset = dataset.rename_column(\"feeling\", \"labels\")\n",
|
290 |
+
"dataset = dataset.cast_column(\"labels\", ClassLabel(names=['0', '1']))\n",
|
291 |
+
"dataset = dataset.align_labels_with_mapping({'0': 1, '1': 0}, \"labels\")"
|
292 |
+
]
|
293 |
+
},
|
294 |
+
{
|
295 |
+
"cell_type": "code",
|
296 |
+
"execution_count": 13,
|
297 |
+
"metadata": {
|
298 |
+
"colab": {
|
299 |
+
"base_uri": "https://localhost:8080/",
|
300 |
+
"height": 339
|
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+
},
|
302 |
+
"id": "BsGX2H5tGwUG",
|
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+
"outputId": "dbcb10ea-a7e8-431c-b96e-9fad8b448eb0"
|
304 |
+
},
|
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+
"outputs": [
|
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+
{
|
307 |
+
"name": "stderr",
|
308 |
+
"output_type": "stream",
|
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+
"text": [
|
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+
"/usr/local/lib/python3.9/dist-packages/transformers/optimization.py:391: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
|
311 |
+
" warnings.warn(\n"
|
312 |
+
]
|
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+
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|
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+
{
|
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+
"data": {
|
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"text/html": [
|
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+
"\n",
|
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+
" <div>\n",
|
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+
" \n",
|
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+
" <progress value='2500' max='2500' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
|
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+
" [2500/2500 54:00, Epoch 1/1]\n",
|
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+
" </div>\n",
|
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+
" <table border=\"1\" class=\"dataframe\">\n",
|
324 |
+
" <thead>\n",
|
325 |
+
" <tr style=\"text-align: left;\">\n",
|
326 |
+
" <th>Step</th>\n",
|
327 |
+
" <th>Training Loss</th>\n",
|
328 |
+
" </tr>\n",
|
329 |
+
" </thead>\n",
|
330 |
+
" <tbody>\n",
|
331 |
+
" <tr>\n",
|
332 |
+
" <td>500</td>\n",
|
333 |
+
" <td>0.721100</td>\n",
|
334 |
+
" </tr>\n",
|
335 |
+
" <tr>\n",
|
336 |
+
" <td>1000</td>\n",
|
337 |
+
" <td>0.709100</td>\n",
|
338 |
+
" </tr>\n",
|
339 |
+
" <tr>\n",
|
340 |
+
" <td>1500</td>\n",
|
341 |
+
" <td>0.708400</td>\n",
|
342 |
+
" </tr>\n",
|
343 |
+
" <tr>\n",
|
344 |
+
" <td>2000</td>\n",
|
345 |
+
" <td>0.703900</td>\n",
|
346 |
+
" </tr>\n",
|
347 |
+
" <tr>\n",
|
348 |
+
" <td>2500</td>\n",
|
349 |
+
" <td>0.701600</td>\n",
|
350 |
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" </tr>\n",
|
351 |
+
" </tbody>\n",
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352 |
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"</table><p>"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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356 |
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]
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357 |
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},
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358 |
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"metadata": {},
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359 |
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"output_type": "display_data"
|
360 |
+
},
|
361 |
+
{
|
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"data": {
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"text/plain": [
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364 |
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"TrainOutput(global_step=2500, training_loss=0.7088180541992187, metrics={'train_runtime': 3241.3296, 'train_samples_per_second': 3.085, 'train_steps_per_second': 0.771, 'total_flos': 9319313633280000.0, 'train_loss': 0.7088180541992187, 'epoch': 1.0})"
|
365 |
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]
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},
|
367 |
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"execution_count": 13,
|
368 |
+
"metadata": {},
|
369 |
+
"output_type": "execute_result"
|
370 |
+
}
|
371 |
+
],
|
372 |
+
"source": [
|
373 |
+
"trainer = Trainer(\n",
|
374 |
+
" model=model, train_dataset=dataset[\"train\"].shuffle().select(range(10000)),\n",
|
375 |
+
" eval_dataset = dataset['test'].select(range(5000)),\n",
|
376 |
+
" args=TrainingArguments(\n",
|
377 |
+
" output_dir=\"./my_saved_model\", overwrite_output_dir=True,\n",
|
378 |
+
" num_train_epochs=1, per_device_train_batch_size=4,\n",
|
379 |
+
" save_steps=10_000, save_total_limit=2),\n",
|
380 |
+
")\n",
|
381 |
+
"\n",
|
382 |
+
"trainer.train()"
|
383 |
+
]
|
384 |
+
},
|
385 |
+
{
|
386 |
+
"cell_type": "code",
|
387 |
+
"execution_count": 14,
|
388 |
+
"metadata": {
|
389 |
+
"id": "DZLeB7tSWoKO"
|
390 |
+
},
|
391 |
+
"outputs": [],
|
392 |
+
"source": [
|
393 |
+
"import torch\n",
|
394 |
+
"torch.save(model.state_dict(), \"./model_cached.pth\")"
|
395 |
+
]
|
396 |
+
},
|
397 |
+
{
|
398 |
+
"cell_type": "code",
|
399 |
+
"execution_count": null,
|
400 |
+
"metadata": {
|
401 |
+
"id": "PRFXZltKGwUH"
|
402 |
+
},
|
403 |
+
"outputs": [],
|
404 |
+
"source": [
|
405 |
+
"import pandas as pd\n",
|
406 |
+
"df = pd.read_json(\"./arxivData.json\")\n",
|
407 |
+
"df.loc[0]"
|
408 |
+
]
|
409 |
+
},
|
410 |
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{
|
411 |
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"cell_type": "code",
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"execution_count": 18,
|
413 |
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"metadata": {
|
414 |
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"colab": {
|
415 |
+
"base_uri": "https://localhost:8080/"
|
416 |
+
},
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417 |
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"id": "tBObhO5PfOGZ",
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418 |
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"outputId": "3771e041-dac7-43ac-ff4e-fc202155a004"
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419 |
+
},
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420 |
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"outputs": [
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421 |
+
{
|
422 |
+
"name": "stdout",
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423 |
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"output_type": "stream",
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424 |
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"text": [
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425 |
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"Mounted at /content/gdrive\n"
|
426 |
+
]
|
427 |
+
}
|
428 |
+
],
|
429 |
+
"source": [
|
430 |
+
"from google.colab import drive\n",
|
431 |
+
"drive.mount('/content/gdrive')"
|
432 |
+
]
|
433 |
+
},
|
434 |
+
{
|
435 |
+
"cell_type": "code",
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"execution_count": 19,
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437 |
+
"metadata": {
|
438 |
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"id": "6O7BNuwXGwUI"
|
439 |
+
},
|
440 |
+
"outputs": [],
|
441 |
+
"source": [
|
442 |
+
"model_save_name = 'model_cached.pth'\n",
|
443 |
+
"path = F\"/content/gdrive/My Drive/cached_model.pth\" \n",
|
444 |
+
"torch.save(model.state_dict(), path)"
|
445 |
+
]
|
446 |
+
},
|
447 |
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{
|
448 |
+
"cell_type": "code",
|
449 |
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"execution_count": 6,
|
450 |
+
"metadata": {},
|
451 |
+
"outputs": [],
|
452 |
+
"source": [
|
453 |
+
"import numpy as np\n",
|
454 |
+
"import torch\n",
|
455 |
+
"from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoModel, Trainer, TrainingArguments, LineByLineTextDataset\n",
|
456 |
+
"import evaluate\n",
|
457 |
+
"\n",
|
458 |
+
"\n",
|
459 |
+
"model_enhanced = AutoModelForSequenceClassification.from_pretrained(\"siebert/sentiment-roberta-large-english\", num_labels=2)\n",
|
460 |
+
"model_enhanced.load_state_dict(torch.load('model_cached_2.pth', map_location=torch.device('cpu')))\n",
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461 |
+
"\n",
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462 |
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"metric = evaluate.load(\"accuracy\")\n",
|
463 |
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"\n",
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464 |
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"def compute_metrics(eval_pred):\n",
|
465 |
+
" logits, labels = eval_pred\n",
|
466 |
+
" predictions = np.argmax(logits, axis=-1)\n",
|
467 |
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" return metric.compute(predictions=predictions, references=labels)\n"
|
468 |
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]
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469 |
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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473 |
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"metadata": {},
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"outputs": [],
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475 |
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"source": [
|
476 |
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"task_evaluator = evaluate.evaluator(\"text-classification\")\n",
|
477 |
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"data_to_evaluate = dataset[\"test\"].select(range(500))\n",
|
478 |
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"data_to_evaluate = data_to_evaluate.rename_column(\"feeling\", \"label\")\n",
|
479 |
+
"\n",
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480 |
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"results = task_evaluator.compute(\n",
|
481 |
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" model_or_pipeline=model_enhanced,\n",
|
482 |
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" tokenizer=tokenizer,\n",
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483 |
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" data=data_to_evaluate,\n",
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484 |
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" label_mapping={\"POSITIVE\": 1.0, \"NEGATIVE\": 0.0},\n",
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485 |
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" metric=\"accuracy\",\n",
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486 |
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" strategy=\"simple\"\n",
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487 |
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")"
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]
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},
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{
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"cell_type": "code",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'accuracy': 0.758,\n",
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