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+ {
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+ "cells": [
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+ {
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+ "cell_type": "markdown",
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+ "metadata": {
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+ "id": "U0o6ydNXBF14"
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+ },
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+ "source": [
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+ "# Text task notebook template\n",
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+ "## Loading the necessary libraries"
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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": null,
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+ "metadata": {
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+ "colab": {
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+ "base_uri": "https://localhost:8080/",
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+ "height": 384
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+ },
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+ "collapsed": true,
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+ "id": "EqgD_yfoBF18",
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+ "outputId": "96e007c1-dbf7-457e-ca2e-8d9b56ac0f45"
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+ },
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+ "outputs": [
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+ {
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+ "output_type": "error",
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+ "ename": "ModuleNotFoundError",
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+ "evalue": "No module named 'fastapi'",
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+ "traceback": [
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+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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+ "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
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+ "\u001b[0;32m<ipython-input-1-dd0a0c7fd244>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mfastapi\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mAPIRouter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mdatetime\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mdatasets\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mload_dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0maccuracy_score\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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+ "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'fastapi'",
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+ "",
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+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
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+ ],
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+ "errorDetails": {
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+ "actions": [
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+ {
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+ "action": "open_url",
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+ "actionText": "Open Examples",
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+ "url": "/notebooks/snippets/importing_libraries.ipynb"
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+ }
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+ ]
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+ }
47
+ }
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+ ],
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+ "source": [
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+ "from fastapi import APIRouter\n",
51
+ "from datetime import datetime\n",
52
+ "from datasets import load_dataset\n",
53
+ "from sklearn.metrics import accuracy_score\n",
54
+ "import random\n",
55
+ "\n",
56
+ "import sys\n",
57
+ "sys.path.append('../tasks')\n",
58
+ "\n",
59
+ "from utils.evaluation import AudioEvaluationRequest\n",
60
+ "from utils.emissions import tracker, clean_emissions_data, get_space_info\n",
61
+ "\n",
62
+ "\n",
63
+ "# Define the label mapping\n",
64
+ "LABEL_MAPPING = {\n",
65
+ " \"chainsaw\": 0,\n",
66
+ " \"environment\": 1\n",
67
+ "}"
68
+ ]
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+ },
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+ {
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+ "cell_type": "markdown",
72
+ "metadata": {
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+ "id": "RB3_Wg3oBF2B"
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+ },
75
+ "source": [
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+ "## Loading the datasets and splitting them"
77
+ ]
78
+ },
79
+ {
80
+ "cell_type": "code",
81
+ "execution_count": null,
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+ "metadata": {
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+ "colab": {
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+ "referenced_widgets": [
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+ "668da7bf85434e098b95c3ec447d78fe",
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+ "5b68d43359eb429395da8be7d4b15556",
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+ "140a304773914e9db8f698eabeb40298",
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+ "6d04e8ab1906400e8e0029949dc523a5"
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+ ]
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+ },
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+ "id": "TA2feB2HBF2B",
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+ "outputId": "a72743ae-c969-473a-c9be-8a76e1122b9d"
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+ },
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+ "outputs": [
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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": "668da7bf85434e098b95c3ec447d78fe",
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+ "version_major": 2,
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+ "version_minor": 0
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+ },
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+ "text/plain": [
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+ "README.md: 0%| | 0.00/5.18k [00:00<?, ?B/s]"
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+ ]
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+ },
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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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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ "c:\\Users\\theo.alvesdacosta\\AppData\\Local\\anaconda3\\Lib\\site-packages\\huggingface_hub\\file_download.py:139: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\\Users\\theo.alvesdacosta\\.cache\\huggingface\\hub\\datasets--QuotaClimat--frugalaichallenge-text-train. Caching files will still work but in a degraded version that might require more space on your disk. This warning can be disabled by setting the `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For more details, see https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations.\n",
114
+ "To support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In order to activate developer mode, see this article: https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development\n",
115
+ " warnings.warn(message)\n"
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+ ]
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+ },
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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": "5b68d43359eb429395da8be7d4b15556",
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+ "version_major": 2,
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+ "version_minor": 0
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+ },
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+ "text/plain": [
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+ "train.parquet: 0%| | 0.00/1.21M [00:00<?, ?B/s]"
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+ ]
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+ },
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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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+ "data": {
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+ "application/vnd.jupyter.widget-view+json": {
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+ "model_id": "140a304773914e9db8f698eabeb40298",
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+ "version_major": 2,
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+ "version_minor": 0
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+ },
139
+ "text/plain": [
140
+ "Generating train split: 0%| | 0/6091 [00:00<?, ? examples/s]"
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+ ]
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+ },
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+ "metadata": {},
144
+ "output_type": "display_data"
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+ },
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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": "6d04e8ab1906400e8e0029949dc523a5",
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+ "version_major": 2,
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+ "version_minor": 0
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+ },
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+ "text/plain": [
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+ "Map: 0%| | 0/6091 [00:00<?, ? examples/s]"
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+ ]
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+ },
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+ "metadata": {},
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+ "output_type": "display_data"
159
+ }
160
+ ],
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+ "source": [
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+ "request = AudioEvaluationRequest()\n",
163
+ "\n",
164
+ "# Load and prepare the dataset\n",
165
+ "dataset = load_dataset(request.dataset_name)\n",
166
+ "\n",
167
+ "# Split dataset\n",
168
+ "train_test = dataset[\"train\"]\n",
169
+ "test_dataset = dataset[\"test\"]"
170
+ ]
171
+ },
172
+ {
173
+ "cell_type": "markdown",
174
+ "metadata": {
175
+ "id": "mGvNFfpBBF2C"
176
+ },
177
+ "source": [
178
+ "## Random Baseline"
179
+ ]
180
+ },
181
+ {
182
+ "cell_type": "code",
183
+ "execution_count": null,
184
+ "metadata": {
185
+ "id": "ogi8skzqBF2E"
186
+ },
187
+ "outputs": [],
188
+ "source": [
189
+ "# Start tracking emissions\n",
190
+ "tracker.start()\n",
191
+ "tracker.start_task(\"inference\")"
192
+ ]
193
+ },
194
+ {
195
+ "cell_type": "code",
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+ "execution_count": null,
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+ "metadata": {
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+ "id": "_g9uaAC3BF2E",
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+ "outputId": "c4331390-f24f-46af-d9d1-e41273ee1ca3"
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+ },
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+ "outputs": [
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1189
+ " 7,\n",
1190
+ " 6,\n",
1191
+ " 7,\n",
1192
+ " 0,\n",
1193
+ " 2,\n",
1194
+ " 6,\n",
1195
+ " 3,\n",
1196
+ " 1,\n",
1197
+ " 5,\n",
1198
+ " 4,\n",
1199
+ " 2,\n",
1200
+ " 4,\n",
1201
+ " 6,\n",
1202
+ " 5,\n",
1203
+ " 2,\n",
1204
+ " 7,\n",
1205
+ " ...]"
1206
+ ]
1207
+ },
1208
+ "execution_count": 6,
1209
+ "metadata": {},
1210
+ "output_type": "execute_result"
1211
+ }
1212
+ ],
1213
+ "source": [
1214
+ "\n",
1215
+ "#--------------------------------------------------------------------------------------------\n",
1216
+ "# YOUR MODEL INFERENCE CODE HERE\n",
1217
+ "# Update the code below to replace the random baseline by your model inference within the inference pass where the energy consumption and emissions are tracked.\n",
1218
+ "#--------------------------------------------------------------------------------------------\n",
1219
+ "\n",
1220
+ "# Make random predictions (placeholder for actual model inference)\n",
1221
+ "true_labels = test_dataset[\"label\"]\n",
1222
+ "import torch\n",
1223
+ "from transformers import pipeline\n",
1224
+ "from sklearn import preprocessing\n",
1225
+ "#encoded_data_fine_tuned_model = train_test[\"train\"].map(preprocess_function, remove_columns=\"audio\", batched=True)\n",
1226
+ "\n",
1227
+ "from datasets import Dataset\n",
1228
+ "\n",
1229
+ "# Utilisation du pipeline directement sur le dataset\n",
1230
+ "classifier = pipeline(\"audio-classification\", model=\"CindyDelage/Challenge_HuggingFace_DFG_FrugalAI\", feature_extractor=feature_extractor)\n",
1231
+ "\n",
1232
+ "# Correctly access the audio data\n",
1233
+ "audio_data = [example[\"array\"] for example in dataset[\"test\"][\"audio\"]]\n",
1234
+ "\n",
1235
+ "# Prédiction sur tout le dataset\n",
1236
+ "results = classifier(audio_data, batch_size=8)\n",
1237
+ "\n",
1238
+ "predictions = []\n",
1239
+ "for result in results:\n",
1240
+ " # Check if result is a dictionary\n",
1241
+ " if isinstance(result, dict):\n",
1242
+ " # Get the label with the highest score\n",
1243
+ " predicted_label = result['label']\n",
1244
+ " else:\n",
1245
+ " # If result is not a dictionary, access it as a list\n",
1246
+ " predicted_label = result[0]['label'] # Assuming the dictionary is the first element\n",
1247
+ "\n",
1248
+ " # Assign 1 for \"environment\", 0 for \"chainsaw\"\n",
1249
+ " if predicted_label == 'environment':\n",
1250
+ " predictions.append(1)\n",
1251
+ " else:\n",
1252
+ " predictions.append(0)\n",
1253
+ "\n",
1254
+ "predictions\n",
1255
+ "\n",
1256
+ "#--------------------------------------------------------------------------------------------\n",
1257
+ "# YOUR MODEL INFERENCE STOPS HERE\n",
1258
+ "#--------------------------------------------------------------------------------------------"
1259
+ ]
1260
+ },
1261
+ {
1262
+ "cell_type": "code",
1263
+ "execution_count": null,
1264
+ "metadata": {
1265
+ "id": "XyF13FJeBF2E",
1266
+ "outputId": "258cf14c-1653-44dc-95b8-920df4dc5d32"
1267
+ },
1268
+ "outputs": [
1269
+ {
1270
+ "name": "stderr",
1271
+ "output_type": "stream",
1272
+ "text": [
1273
+ "[codecarbon WARNING @ 19:53:32] Background scheduler didn't run for a long period (47s), results might be inaccurate\n",
1274
+ "[codecarbon INFO @ 19:53:32] Energy consumed for RAM : 0.000156 kWh. RAM Power : 11.755242347717285 W\n",
1275
+ "[codecarbon INFO @ 19:53:32] Delta energy consumed for CPU with constant : 0.000564 kWh, power : 42.5 W\n",
1276
+ "[codecarbon INFO @ 19:53:32] Energy consumed for All CPU : 0.000564 kWh\n",
1277
+ "[codecarbon INFO @ 19:53:32] 0.000720 kWh of electricity used since the beginning.\n"
1278
+ ]
1279
+ },
1280
+ {
1281
+ "data": {
1282
+ "text/plain": [
1283
+ "EmissionsData(timestamp='2025-01-21T19:53:32', project_name='codecarbon', run_id='908f2e7e-4bb2-4991-a0f6-56bf8d7eda21', experiment_id='5b0fa12a-3dd7-45bb-9766-cc326314d9f1', duration=47.736408500000834, emissions=4.032368007471064e-05, emissions_rate=8.444466886328872e-07, cpu_power=42.5, gpu_power=0.0, ram_power=11.755242347717285, cpu_energy=0.0005636615353475565, gpu_energy=0, ram_energy=0.00015590305493261682, energy_consumed=0.0007195645902801733, country_name='France', country_iso_code='FRA', region='île-de-france', cloud_provider='', cloud_region='', os='Windows-11-10.0.22631-SP0', python_version='3.12.7', codecarbon_version='3.0.0_rc0', cpu_count=12, cpu_model='13th Gen Intel(R) Core(TM) i7-1365U', gpu_count=None, gpu_model=None, longitude=2.3494, latitude=48.8558, ram_total_size=31.347312927246094, tracking_mode='machine', on_cloud='N', pue=1.0)"
1284
+ ]
1285
+ },
1286
+ "execution_count": 8,
1287
+ "metadata": {},
1288
+ "output_type": "execute_result"
1289
+ }
1290
+ ],
1291
+ "source": [
1292
+ "# Stop tracking emissions\n",
1293
+ "emissions_data = tracker.stop_task()\n",
1294
+ "emissions_data"
1295
+ ]
1296
+ },
1297
+ {
1298
+ "cell_type": "code",
1299
+ "execution_count": null,
1300
+ "metadata": {
1301
+ "id": "wBVdK2TaBF2F",
1302
+ "outputId": "39596c2e-2734-4b37-922d-647d771efe0c"
1303
+ },
1304
+ "outputs": [
1305
+ {
1306
+ "data": {
1307
+ "text/plain": [
1308
+ "0.10090237899917966"
1309
+ ]
1310
+ },
1311
+ "execution_count": 9,
1312
+ "metadata": {},
1313
+ "output_type": "execute_result"
1314
+ }
1315
+ ],
1316
+ "source": [
1317
+ "# Calculate accuracy\n",
1318
+ "accuracy = accuracy_score(true_labels, predictions)\n",
1319
+ "accuracy"
1320
+ ]
1321
+ },
1322
+ {
1323
+ "cell_type": "code",
1324
+ "execution_count": null,
1325
+ "metadata": {
1326
+ "id": "eIjuJJrKBF2G",
1327
+ "outputId": "bf691025-6f9d-4e3d-9e1e-75f4bfbcd70c"
1328
+ },
1329
+ "outputs": [
1330
+ {
1331
+ "data": {
1332
+ "text/plain": [
1333
+ "{'submission_timestamp': '2025-01-21T19:53:46.639165',\n",
1334
+ " 'accuracy': 0.10090237899917966,\n",
1335
+ " 'energy_consumed_wh': 0.7195645902801733,\n",
1336
+ " 'emissions_gco2eq': 0.040323680074710634,\n",
1337
+ " 'emissions_data': {'run_id': '908f2e7e-4bb2-4991-a0f6-56bf8d7eda21',\n",
1338
+ " 'duration': 47.736408500000834,\n",
1339
+ " 'emissions': 4.032368007471064e-05,\n",
1340
+ " 'emissions_rate': 8.444466886328872e-07,\n",
1341
+ " 'cpu_power': 42.5,\n",
1342
+ " 'gpu_power': 0.0,\n",
1343
+ " 'ram_power': 11.755242347717285,\n",
1344
+ " 'cpu_energy': 0.0005636615353475565,\n",
1345
+ " 'gpu_energy': 0,\n",
1346
+ " 'ram_energy': 0.00015590305493261682,\n",
1347
+ " 'energy_consumed': 0.0007195645902801733,\n",
1348
+ " 'country_name': 'France',\n",
1349
+ " 'country_iso_code': 'FRA',\n",
1350
+ " 'region': 'île-de-france',\n",
1351
+ " 'cloud_provider': '',\n",
1352
+ " 'cloud_region': '',\n",
1353
+ " 'os': 'Windows-11-10.0.22631-SP0',\n",
1354
+ " 'python_version': '3.12.7',\n",
1355
+ " 'codecarbon_version': '3.0.0_rc0',\n",
1356
+ " 'cpu_count': 12,\n",
1357
+ " 'cpu_model': '13th Gen Intel(R) Core(TM) i7-1365U',\n",
1358
+ " 'gpu_count': None,\n",
1359
+ " 'gpu_model': None,\n",
1360
+ " 'ram_total_size': 31.347312927246094,\n",
1361
+ " 'tracking_mode': 'machine',\n",
1362
+ " 'on_cloud': 'N',\n",
1363
+ " 'pue': 1.0},\n",
1364
+ " 'dataset_config': {'dataset_name': 'QuotaClimat/frugalaichallenge-text-train',\n",
1365
+ " 'test_size': 0.2,\n",
1366
+ " 'test_seed': 42}}"
1367
+ ]
1368
+ },
1369
+ "execution_count": 10,
1370
+ "metadata": {},
1371
+ "output_type": "execute_result"
1372
+ }
1373
+ ],
1374
+ "source": [
1375
+ "# Prepare results dictionary\n",
1376
+ "results = {\n",
1377
+ " \"submission_timestamp\": datetime.now().isoformat(),\n",
1378
+ " \"accuracy\": float(accuracy),\n",
1379
+ " \"energy_consumed_wh\": emissions_data.energy_consumed * 1000,\n",
1380
+ " \"emissions_gco2eq\": emissions_data.emissions * 1000,\n",
1381
+ " \"emissions_data\": clean_emissions_data(emissions_data),\n",
1382
+ " \"dataset_config\": {\n",
1383
+ " \"dataset_name\": request.dataset_name,\n",
1384
+ " \"test_size\": request.test_size,\n",
1385
+ " \"test_seed\": request.test_seed\n",
1386
+ " }\n",
1387
+ "}\n",
1388
+ "\n",
1389
+ "results"
1390
+ ]
1391
+ }
1392
+ ],
1393
+ "metadata": {
1394
+ "kernelspec": {
1395
+ "display_name": "base",
1396
+ "language": "python",
1397
+ "name": "python3"
1398
+ },
1399
+ "language_info": {
1400
+ "codemirror_mode": {
1401
+ "name": "ipython",
1402
+ "version": 3
1403
+ },
1404
+ "file_extension": ".py",
1405
+ "mimetype": "text/x-python",
1406
+ "name": "python",
1407
+ "nbconvert_exporter": "python",
1408
+ "pygments_lexer": "ipython3",
1409
+ "version": "3.12.7"
1410
+ },
1411
+ "colab": {
1412
+ "provenance": []
1413
+ }
1414
+ },
1415
+ "nbformat": 4,
1416
+ "nbformat_minor": 0
1417
+ }