feat: add imdb and ag news dataset to datasets
Browse files- README.md +7 -0
- dataset/ag_news_test.csv +0 -0
- dataset/preprocess_datasets.ipynb +523 -0
- index.html +2 -0
- src/datasetLoader.js +105 -22
- src/utils.js +19 -1
README.md
CHANGED
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@@ -22,3 +22,10 @@ Furthermore, it investigates two different scheduling policies to send request t
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## Getting Started
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To run the experiments, open `index.html` in a web browser. Ensure that you have an API key for the OpenRouter service to run the cloud inference.
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You can then download the models and run them in the browser by leveraging the transformers.js library.
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## Getting Started
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| 23 |
To run the experiments, open `index.html` in a web browser. Ensure that you have an API key for the OpenRouter service to run the cloud inference.
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| 24 |
You can then download the models and run them in the browser by leveraging the transformers.js library.
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## Dataset preparation
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To prepare the dataset for the experiments, we follow these steps:
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- Download the dataset from kaggle or huggingface
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- Add row indexes to each entry in the dataset for easy reference
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- Save the prepared dataset in the `dataset/` directory
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dataset/ag_news_test.csv
ADDED
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The diff for this file is too large to render.
See raw diff
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dataset/preprocess_datasets.ipynb
ADDED
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@@ -0,0 +1,523 @@
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|
| 1 |
+
{
|
| 2 |
+
"cells": [
|
| 3 |
+
{
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| 4 |
+
"cell_type": "code",
|
| 5 |
+
"id": "initial_id",
|
| 6 |
+
"metadata": {
|
| 7 |
+
"collapsed": true,
|
| 8 |
+
"ExecuteTime": {
|
| 9 |
+
"end_time": "2025-12-25T10:54:53.050054Z",
|
| 10 |
+
"start_time": "2025-12-25T10:54:49.296474Z"
|
| 11 |
+
}
|
| 12 |
+
},
|
| 13 |
+
"source": "import pandas as pd",
|
| 14 |
+
"outputs": [],
|
| 15 |
+
"execution_count": 1
|
| 16 |
+
},
|
| 17 |
+
{
|
| 18 |
+
"metadata": {},
|
| 19 |
+
"cell_type": "markdown",
|
| 20 |
+
"source": "# Prepare the spam/ham email dataset",
|
| 21 |
+
"id": "fd1d0f3beb9aa893"
|
| 22 |
+
},
|
| 23 |
+
{
|
| 24 |
+
"metadata": {
|
| 25 |
+
"ExecuteTime": {
|
| 26 |
+
"end_time": "2025-12-21T16:20:15.450476Z",
|
| 27 |
+
"start_time": "2025-12-21T16:20:15.389394Z"
|
| 28 |
+
}
|
| 29 |
+
},
|
| 30 |
+
"cell_type": "code",
|
| 31 |
+
"source": "dataset = pd.read_csv('./emails.csv')",
|
| 32 |
+
"id": "f72a26d75f7a2588",
|
| 33 |
+
"outputs": [],
|
| 34 |
+
"execution_count": 11
|
| 35 |
+
},
|
| 36 |
+
{
|
| 37 |
+
"metadata": {
|
| 38 |
+
"ExecuteTime": {
|
| 39 |
+
"end_time": "2025-12-21T16:20:15.562018Z",
|
| 40 |
+
"start_time": "2025-12-21T16:20:15.550178Z"
|
| 41 |
+
}
|
| 42 |
+
},
|
| 43 |
+
"cell_type": "code",
|
| 44 |
+
"source": [
|
| 45 |
+
"# add column with row id\n",
|
| 46 |
+
"dataset['ID'] = range(1, len(dataset) + 1)"
|
| 47 |
+
],
|
| 48 |
+
"id": "e6a938ba1a501431",
|
| 49 |
+
"outputs": [],
|
| 50 |
+
"execution_count": 12
|
| 51 |
+
},
|
| 52 |
+
{
|
| 53 |
+
"metadata": {
|
| 54 |
+
"ExecuteTime": {
|
| 55 |
+
"end_time": "2025-12-21T16:20:15.714419Z",
|
| 56 |
+
"start_time": "2025-12-21T16:20:15.710137Z"
|
| 57 |
+
}
|
| 58 |
+
},
|
| 59 |
+
"cell_type": "code",
|
| 60 |
+
"source": [
|
| 61 |
+
"# add the column to the first position\n",
|
| 62 |
+
"cols = dataset.columns.tolist()\n",
|
| 63 |
+
"cols = cols[-1:] + cols[:-1]\n",
|
| 64 |
+
"dataset = dataset[cols]"
|
| 65 |
+
],
|
| 66 |
+
"id": "9d56f16fc000b2e2",
|
| 67 |
+
"outputs": [],
|
| 68 |
+
"execution_count": 13
|
| 69 |
+
},
|
| 70 |
+
{
|
| 71 |
+
"metadata": {
|
| 72 |
+
"ExecuteTime": {
|
| 73 |
+
"end_time": "2025-12-21T16:20:15.896626Z",
|
| 74 |
+
"start_time": "2025-12-21T16:20:15.883918Z"
|
| 75 |
+
}
|
| 76 |
+
},
|
| 77 |
+
"cell_type": "code",
|
| 78 |
+
"source": "dataset.head()",
|
| 79 |
+
"id": "c853a292fe6d4d01",
|
| 80 |
+
"outputs": [
|
| 81 |
+
{
|
| 82 |
+
"data": {
|
| 83 |
+
"text/plain": [
|
| 84 |
+
" ID Text Spam\n",
|
| 85 |
+
"0 1 Subject: naturally irresistible your corporate... 1\n",
|
| 86 |
+
"1 2 Subject: the stock trading gunslinger fanny i... 1\n",
|
| 87 |
+
"2 3 Subject: unbelievable new homes made easy im ... 1\n",
|
| 88 |
+
"3 4 Subject: 4 color printing special request add... 1\n",
|
| 89 |
+
"4 5 Subject: do not have money , get software cds ... 1"
|
| 90 |
+
],
|
| 91 |
+
"text/html": [
|
| 92 |
+
"<div>\n",
|
| 93 |
+
"<style scoped>\n",
|
| 94 |
+
" .dataframe tbody tr th:only-of-type {\n",
|
| 95 |
+
" vertical-align: middle;\n",
|
| 96 |
+
" }\n",
|
| 97 |
+
"\n",
|
| 98 |
+
" .dataframe tbody tr th {\n",
|
| 99 |
+
" vertical-align: top;\n",
|
| 100 |
+
" }\n",
|
| 101 |
+
"\n",
|
| 102 |
+
" .dataframe thead th {\n",
|
| 103 |
+
" text-align: right;\n",
|
| 104 |
+
" }\n",
|
| 105 |
+
"</style>\n",
|
| 106 |
+
"<table border=\"1\" class=\"dataframe\">\n",
|
| 107 |
+
" <thead>\n",
|
| 108 |
+
" <tr style=\"text-align: right;\">\n",
|
| 109 |
+
" <th></th>\n",
|
| 110 |
+
" <th>ID</th>\n",
|
| 111 |
+
" <th>Text</th>\n",
|
| 112 |
+
" <th>Spam</th>\n",
|
| 113 |
+
" </tr>\n",
|
| 114 |
+
" </thead>\n",
|
| 115 |
+
" <tbody>\n",
|
| 116 |
+
" <tr>\n",
|
| 117 |
+
" <th>0</th>\n",
|
| 118 |
+
" <td>1</td>\n",
|
| 119 |
+
" <td>Subject: naturally irresistible your corporate...</td>\n",
|
| 120 |
+
" <td>1</td>\n",
|
| 121 |
+
" </tr>\n",
|
| 122 |
+
" <tr>\n",
|
| 123 |
+
" <th>1</th>\n",
|
| 124 |
+
" <td>2</td>\n",
|
| 125 |
+
" <td>Subject: the stock trading gunslinger fanny i...</td>\n",
|
| 126 |
+
" <td>1</td>\n",
|
| 127 |
+
" </tr>\n",
|
| 128 |
+
" <tr>\n",
|
| 129 |
+
" <th>2</th>\n",
|
| 130 |
+
" <td>3</td>\n",
|
| 131 |
+
" <td>Subject: unbelievable new homes made easy im ...</td>\n",
|
| 132 |
+
" <td>1</td>\n",
|
| 133 |
+
" </tr>\n",
|
| 134 |
+
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" <th>3</th>\n",
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" <td>1</td>\n",
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"</div>"
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]
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"execution_count": 14,
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"execution_count": 14
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"metadata": {
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"# store csv in dataset folder\n",
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| 168 |
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],
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"cell_type": "markdown",
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"source": "# Prepare the AG news dataset",
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},
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{
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"cell_type": "code",
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"source": "dataset = pd.read_csv('./ag_news_test.csv')",
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"id": "6396678647ac4f8",
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| 190 |
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"outputs": [],
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"execution_count": 2
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{
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"metadata": {
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"cell_type": "code",
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"source": [
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"# add column with row id\n",
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"dataset['ID'] = range(1, len(dataset) + 1)"
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"id": "445a51aa7a9d6de9",
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"outputs": [],
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"execution_count": 4
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"metadata": {
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"cell_type": "code",
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"source": "dataset.head()",
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"id": "3c95d325ace568ef",
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"outputs": [
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{
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| 239 |
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"data": {
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"text/plain": [
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| 241 |
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" ID Class Index Title \\\n",
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| 242 |
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"0 1 3 Fears for T N pension after talks \n",
|
| 243 |
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"1 2 4 The Race is On: Second Private Team Sets Launc... \n",
|
| 244 |
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"2 3 4 Ky. Company Wins Grant to Study Peptides (AP) \n",
|
| 245 |
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"3 4 4 Prediction Unit Helps Forecast Wildfires (AP) \n",
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"4 5 4 Calif. Aims to Limit Farm-Related Smog (AP) \n",
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"\n",
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" Description \n",
|
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"1 SPACE.com - TORONTO, Canada -- A second\\team o... \n",
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" <td>Prediction Unit Helps Forecast Wildfires (AP)</td>\n",
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" <th>4</th>\n",
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" <td>5</td>\n",
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" <td>Calif. Aims to Limit Farm-Related Smog (AP)</td>\n",
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| 319 |
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| 325 |
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| 328 |
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| 329 |
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|
| 330 |
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|
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|
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|
| 339 |
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| 340 |
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| 344 |
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{
|
| 345 |
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"metadata": {},
|
| 346 |
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"cell_type": "markdown",
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| 347 |
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"source": "# Prepare the IMDB reviews dataset",
|
| 348 |
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"id": "d6d26cc073ed9c20"
|
| 349 |
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},
|
| 350 |
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{
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| 355 |
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|
| 356 |
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|
| 357 |
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"cell_type": "code",
|
| 358 |
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"source": "dataset = pd.read_csv('./imdb_dataset.csv')",
|
| 359 |
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"id": "d428b1226c135eeb",
|
| 360 |
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"outputs": [],
|
| 361 |
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"execution_count": 7
|
| 362 |
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},
|
| 363 |
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{
|
| 364 |
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"metadata": {
|
| 365 |
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"ExecuteTime": {
|
| 366 |
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"end_time": "2025-12-25T10:57:21.530556Z",
|
| 367 |
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"start_time": "2025-12-25T10:57:21.516704Z"
|
| 368 |
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|
| 369 |
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},
|
| 370 |
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"cell_type": "code",
|
| 371 |
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"source": [
|
| 372 |
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"# add column with row id\n",
|
| 373 |
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"dataset['ID'] = range(1, len(dataset) + 1)"
|
| 374 |
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|
| 375 |
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"id": "fcd59bb4a4faaee9",
|
| 376 |
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|
| 377 |
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"execution_count": 8
|
| 378 |
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|
| 379 |
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|
| 380 |
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"metadata": {
|
| 381 |
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|
| 382 |
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| 383 |
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| 384 |
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| 385 |
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|
| 387 |
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|
| 388 |
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|
| 389 |
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|
| 390 |
+
"cols = cols[-1:] + cols[:-1]\n",
|
| 391 |
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"dataset = dataset[cols]"
|
| 392 |
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"id": "4f484cca1f2663f3",
|
| 394 |
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|
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|
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|
| 408 |
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|
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" ID review sentiment\n",
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|
| 413 |
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|
| 414 |
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"2 3 I thought this was a wonderful way to spend ti... positive\n",
|
| 415 |
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"3 4 Basically there's a family where a little boy ... negative\n",
|
| 416 |
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"4 5 Petter Mattei's \"Love in the Time of Money\" is... positive"
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|
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| 440 |
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| 448 |
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| 449 |
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|
| 450 |
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| 451 |
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| 452 |
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| 453 |
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|
| 454 |
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|
| 455 |
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|
| 456 |
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| 457 |
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|
| 458 |
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| 459 |
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| 460 |
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| 466 |
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| 467 |
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| 473 |
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| 474 |
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|
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+
"name": "python",
|
| 516 |
+
"nbconvert_exporter": "python",
|
| 517 |
+
"pygments_lexer": "ipython2",
|
| 518 |
+
"version": "2.7.6"
|
| 519 |
+
}
|
| 520 |
+
},
|
| 521 |
+
"nbformat": 4,
|
| 522 |
+
"nbformat_minor": 5
|
| 523 |
+
}
|
index.html
CHANGED
|
@@ -125,6 +125,8 @@
|
|
| 125 |
class="mt-1 w-full px-3 py-2 rounded-lg border border-gray-300 focus:ring-2 focus:ring-blue-500 focus:outline-none">
|
| 126 |
<option value="boolq_validation">BoolQ</option>
|
| 127 |
<option value="spam_ham_dataset">Spam</option>
|
|
|
|
|
|
|
| 128 |
</select>
|
| 129 |
</label>
|
| 130 |
|
|
|
|
| 125 |
class="mt-1 w-full px-3 py-2 rounded-lg border border-gray-300 focus:ring-2 focus:ring-blue-500 focus:outline-none">
|
| 126 |
<option value="boolq_validation">BoolQ</option>
|
| 127 |
<option value="spam_ham_dataset">Spam</option>
|
| 128 |
+
<option value="imdb_dataset">IMDB</option>
|
| 129 |
+
<option value="ag_news_test">AG News</option>
|
| 130 |
</select>
|
| 131 |
</label>
|
| 132 |
|
src/datasetLoader.js
CHANGED
|
@@ -36,29 +36,24 @@ export class DatasetLoader {
|
|
| 36 |
this._dataset = lines
|
| 37 |
.filter(l => l.trim().length > 0)
|
| 38 |
.map(line => {
|
| 39 |
-
let id, answer, full_prompt
|
| 40 |
|
| 41 |
-
// load different datasets based on name
|
| 42 |
-
|
| 43 |
-
|
| 44 |
-
|
| 45 |
-
|
| 46 |
-
|
| 47 |
-
|
| 48 |
-
|
| 49 |
-
|
| 50 |
-
|
| 51 |
-
|
| 52 |
-
|
| 53 |
-
|
| 54 |
-
|
| 55 |
-
|
| 56 |
-
|
| 57 |
-
// set the prompt
|
| 58 |
-
full_prompt = `Task: Determine whether the following message is spam or not.
|
| 59 |
-
Instructions: Answer with ONLY the word "true" or "false". Do not provide any explanation or additional text.
|
| 60 |
-
Message: ${text}
|
| 61 |
-
Answer:`;
|
| 62 |
}
|
| 63 |
|
| 64 |
return {id: id, prompt: full_prompt, groundTruth: answer};
|
|
@@ -73,6 +68,94 @@ export class DatasetLoader {
|
|
| 73 |
});
|
| 74 |
}
|
| 75 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 76 |
/**
|
| 77 |
* Parse a single CSV line into fields, handling quoted fields with commas
|
| 78 |
*
|
|
|
|
| 36 |
this._dataset = lines
|
| 37 |
.filter(l => l.trim().length > 0)
|
| 38 |
.map(line => {
|
| 39 |
+
let id, answer, full_prompt;
|
| 40 |
|
| 41 |
+
// load different datasets based on the dataset name
|
| 42 |
+
switch (name) {
|
| 43 |
+
case 'boolq_validation':
|
| 44 |
+
({id, full_prompt, answer} = this._loadBoolQLine(line));
|
| 45 |
+
break;
|
| 46 |
+
case 'spam_ham_dataset':
|
| 47 |
+
({id, full_prompt, answer} = this._loadSpamHamLine(line));
|
| 48 |
+
break;
|
| 49 |
+
case 'imdb_dataset':
|
| 50 |
+
({id, full_prompt, answer} = this._loadIMDBLine(line));
|
| 51 |
+
break;
|
| 52 |
+
case 'ag_news_test':
|
| 53 |
+
({id, full_prompt, answer} = this._loadAGNewsLine(line));
|
| 54 |
+
break;
|
| 55 |
+
default:
|
| 56 |
+
throw new Error(`DatasetLoader: Unsupported dataset name '${name}'`);
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 57 |
}
|
| 58 |
|
| 59 |
return {id: id, prompt: full_prompt, groundTruth: answer};
|
|
|
|
| 68 |
});
|
| 69 |
}
|
| 70 |
|
| 71 |
+
|
| 72 |
+
/**
|
| 73 |
+
* Load a single line from the BoolQ dataset and prepare the prompt
|
| 74 |
+
*
|
| 75 |
+
* @param line - A single line from the BoolQ CSV dataset
|
| 76 |
+
* @returns {{full_prompt: string, answer: *, id: *}}
|
| 77 |
+
* @private
|
| 78 |
+
*/
|
| 79 |
+
_loadBoolQLine(line) {
|
| 80 |
+
// parse line into fields handling quoted commas
|
| 81 |
+
const [id, question, answer, context] = this._parseCSVLine(line);
|
| 82 |
+
|
| 83 |
+
// set the prompt
|
| 84 |
+
const full_prompt = `Question: ${question}
|
| 85 |
+
Context: ${context}
|
| 86 |
+
Instructions: Answer with ONLY the word "true" or "false". Do not provide any explanation or additional text.
|
| 87 |
+
Answer:`;
|
| 88 |
+
|
| 89 |
+
return {id, full_prompt, answer}
|
| 90 |
+
}
|
| 91 |
+
|
| 92 |
+
|
| 93 |
+
/**
|
| 94 |
+
* Load a single line from the SpamHam dataset and prepare the prompt
|
| 95 |
+
*
|
| 96 |
+
* @param line - A single line from the SpamHam CSV dataset
|
| 97 |
+
* @returns {{full_prompt: string, answer: (string), id: *}}
|
| 98 |
+
* @private
|
| 99 |
+
*/
|
| 100 |
+
_loadSpamHamLine(line) {
|
| 101 |
+
let [id, text, answer] = this._parseCSVLine(line);
|
| 102 |
+
|
| 103 |
+
// convert answer to string boolean
|
| 104 |
+
answer = (answer.toLowerCase() === 'spam') ? 'true' : 'false';
|
| 105 |
+
|
| 106 |
+
// set the prompt
|
| 107 |
+
const full_prompt = `Task: Determine whether the following message is spam or not.
|
| 108 |
+
Instructions: Answer with ONLY the word "true" or "false". Do not provide any explanation or additional text.
|
| 109 |
+
Message: ${text}
|
| 110 |
+
Answer:`;
|
| 111 |
+
|
| 112 |
+
return {id, full_prompt, answer}
|
| 113 |
+
}
|
| 114 |
+
|
| 115 |
+
|
| 116 |
+
/**
|
| 117 |
+
* Load a single line from the IMDB dataset and prepare the prompt
|
| 118 |
+
*
|
| 119 |
+
* @param line - A single line from the IMDB CSV dataset
|
| 120 |
+
* @returns {{full_prompt: string, answer: *, id: *}}
|
| 121 |
+
* @private
|
| 122 |
+
*/
|
| 123 |
+
_loadIMDBLine(line) {
|
| 124 |
+
let [id, review, answer] = this._parseCSVLine(line);
|
| 125 |
+
|
| 126 |
+
// set the prompt
|
| 127 |
+
const full_prompt = `Task: Determine whether the sentiment of the following review is positive or negative.
|
| 128 |
+
Instructions: Answer with ONLY the word "positive" or "negative". Do not provide any explanation or additional text.
|
| 129 |
+
Review: ${review}
|
| 130 |
+
Sentiment:`;
|
| 131 |
+
|
| 132 |
+
return {id, full_prompt, answer}
|
| 133 |
+
}
|
| 134 |
+
|
| 135 |
+
|
| 136 |
+
/**
|
| 137 |
+
* Load a single line from the AG News dataset and prepare the prompt
|
| 138 |
+
*
|
| 139 |
+
* @param line - A single line from the AG News CSV dataset
|
| 140 |
+
* @returns {{full_prompt: string, answer: *, id: *}}
|
| 141 |
+
* @private
|
| 142 |
+
*/
|
| 143 |
+
_loadAGNewsLine(line) {
|
| 144 |
+
let [id, answer, title, description] = this._parseCSVLine(line);
|
| 145 |
+
|
| 146 |
+
// set the prompt
|
| 147 |
+
const full_prompt = `Task: Determine whether the following news article belong to world, sports, business or Sci/Tech category.
|
| 148 |
+
Categories: World (1), Sports (2), Business (3), Sci/Tech (4).
|
| 149 |
+
Instructions: Answer with ONLY the id (1,2,3 or 4) of the class. Do not provide any explanation or additional text.
|
| 150 |
+
News Title: ${title}
|
| 151 |
+
News Description: ${description}
|
| 152 |
+
`;
|
| 153 |
+
|
| 154 |
+
return {id, full_prompt, answer}
|
| 155 |
+
}
|
| 156 |
+
|
| 157 |
+
|
| 158 |
+
|
| 159 |
/**
|
| 160 |
* Parse a single CSV line into fields, handling quoted fields with commas
|
| 161 |
*
|
src/utils.js
CHANGED
|
@@ -33,7 +33,7 @@ export function logTo(el, evt) {
|
|
| 33 |
<td>${evt.totalLatency?.toFixed(2) || evt.latency?.toFixed(2) || 0}ms</td>
|
| 34 |
<td>${evt.queueingTime?.toFixed(2) || 0}ms</td>
|
| 35 |
<td>${evt.inferenceTime?.toFixed(2) || evt.latency?.toFixed(2) || 0}ms</td>
|
| 36 |
-
<td title="${evt.job.prompt}">${evt.job.prompt.substring(0, 30)}...</td>
|
| 37 |
<td title="${evt.response || ''}">${(evt.response || '').substring(0, 30)}</td>
|
| 38 |
<td>${evt.evalRes.exactMatch}</td>
|
| 39 |
`;
|
|
@@ -41,6 +41,24 @@ export function logTo(el, evt) {
|
|
| 41 |
el.scrollTop = el.scrollHeight;
|
| 42 |
}
|
| 43 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 44 |
|
| 45 |
/**
|
| 46 |
* Approximates the number of words in a given text string
|
|
|
|
| 33 |
<td>${evt.totalLatency?.toFixed(2) || evt.latency?.toFixed(2) || 0}ms</td>
|
| 34 |
<td>${evt.queueingTime?.toFixed(2) || 0}ms</td>
|
| 35 |
<td>${evt.inferenceTime?.toFixed(2) || evt.latency?.toFixed(2) || 0}ms</td>
|
| 36 |
+
<td title="${escapeHtml(evt.job.prompt)}">${escapeHtml(evt.job.prompt.substring(0, 30))}...</td>
|
| 37 |
<td title="${evt.response || ''}">${(evt.response || '').substring(0, 30)}</td>
|
| 38 |
<td>${evt.evalRes.exactMatch}</td>
|
| 39 |
`;
|
|
|
|
| 41 |
el.scrollTop = el.scrollHeight;
|
| 42 |
}
|
| 43 |
|
| 44 |
+
/**
|
| 45 |
+
* Escapes HTML special characters in a string to prevent HTML injection
|
| 46 |
+
*
|
| 47 |
+
* @param str - Input string
|
| 48 |
+
* @returns {string} - Escaped string
|
| 49 |
+
*/
|
| 50 |
+
function escapeHtml(str) {
|
| 51 |
+
return str.replace(/[&<>"']/g, (char) => {
|
| 52 |
+
const escapeMap = {
|
| 53 |
+
'&': '&',
|
| 54 |
+
'<': '<',
|
| 55 |
+
'>': '>',
|
| 56 |
+
'"': '"',
|
| 57 |
+
"'": ''',
|
| 58 |
+
};
|
| 59 |
+
return escapeMap[char];
|
| 60 |
+
});
|
| 61 |
+
}
|
| 62 |
|
| 63 |
/**
|
| 64 |
* Approximates the number of words in a given text string
|