hynky HF staff commited on
Commit
bb5d313
1 Parent(s): f3b0151

consistent colors + edu plot

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assets/data/plots/edu_ablations/agg_score.json CHANGED
@@ -1 +1 @@
1
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  <h2>📚 FineWeb-Edu</h2>
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- <figure style="text-align: center;">
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- <img src="assets/images/dataset_comparisons_agg_fw_edu.png"/>
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- <figcaption style="font-style: italic; margin-top: 10px;">📚 FineWeb-Edu outperforms 🍷 FineWeb and all other open web datasets on our group of evaluation tasks.</figcaption>
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608
  <p><a href="https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu">📚 FineWeb-Edu</a> is an additional development of FineWeb that we are excited to introduce in this tech report and openly release. 📚 FineWeb-Edu is based on a new approach that has recently emerged for filtering LLM training datasets: using synthetic data to develop classifiers for identifying educational content. This technique was notably used in the trainings of Llama 3<d-cite bibtex-key="llama3modelcard"></d-cite> and Phi3<d-cite bibtex-key="abdin2024phi"></d-cite>, but its large-scale impact on web data filtering has, in our opinion, thur far not been publicly explored to its full potential.</p>
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  <p>The popular Phi3 models were trained on 3.3 and 4.8 trillion tokens, with the paper<d-cite bibtex-key="abdin2024phi"></d-cite> stating:</p>
610
  <blockquote>Our training data consists of heavily filtered publicly available web data (according to the 'educational level') from various open internet sources, as well as synthetic LLM-generated data.</blockquote>
 
601
 
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  <h2>📚 FineWeb-Edu</h2>
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  <p><a href="https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu">📚 FineWeb-Edu</a> is an additional development of FineWeb that we are excited to introduce in this tech report and openly release. 📚 FineWeb-Edu is based on a new approach that has recently emerged for filtering LLM training datasets: using synthetic data to develop classifiers for identifying educational content. This technique was notably used in the trainings of Llama 3<d-cite bibtex-key="llama3modelcard"></d-cite> and Phi3<d-cite bibtex-key="abdin2024phi"></d-cite>, but its large-scale impact on web data filtering has, in our opinion, thur far not been publicly explored to its full potential.</p>
613
  <p>The popular Phi3 models were trained on 3.3 and 4.8 trillion tokens, with the paper<d-cite bibtex-key="abdin2024phi"></d-cite> stating:</p>
614
  <blockquote>Our training data consists of heavily filtered publicly available web data (according to the 'educational level') from various open internet sources, as well as synthetic LLM-generated data.</blockquote>
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  "cell_type": "code",
1004
- "execution_count": 45,
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1005
  "metadata": {},
1006
  "outputs": [],
1007
  "source": [
@@ -1047,7 +1150,7 @@
1047
  " \"data\": datas,\n",
1048
  " \"layout\": {\n",
1049
  " \"title\": {\n",
1050
- " \"text\": \"FineWeb-Edu ablations\"\n",
1051
  " },\n",
1052
  " }\n",
1053
  " }, f)\n",
@@ -1058,7 +1161,8 @@
1058
  " \"files\": files,\n",
1059
  " \"settings\": {\n",
1060
  " \"defaultMetric\": \"agg_score\",\n",
1061
- " \"slider\":{\"min\":0,\"max\":30,\"default\":5}\n",
 
1062
  " }\n",
1063
  " }, f)"
1064
  ]
 
9
  },
10
  {
11
  "cell_type": "code",
12
+ "execution_count": 56,
13
  "metadata": {},
14
  "outputs": [],
15
  "source": [
 
26
  },
27
  {
28
  "cell_type": "code",
29
+ "execution_count": 57,
30
  "metadata": {},
31
  "outputs": [],
32
  "source": [
 
560
  },
561
  {
562
  "cell_type": "code",
563
+ "execution_count": 58,
564
  "metadata": {},
565
  "outputs": [
566
  {
 
750
  "4 0.506 0.4890 0.4820 0.302189 0.317653 "
751
  ]
752
  },
753
+ "execution_count": 58,
754
  "metadata": {},
755
  "output_type": "execute_result"
756
  }
 
1001
  },
1002
  {
1003
  "cell_type": "code",
1004
+ "execution_count": 63,
1005
+ "metadata": {},
1006
+ "outputs": [
1007
+ {
1008
+ "data": {
1009
+ "text/html": [
1010
+ "<div>\n",
1011
+ "<style scoped>\n",
1012
+ " .dataframe tbody tr th:only-of-type {\n",
1013
+ " vertical-align: middle;\n",
1014
+ " }\n",
1015
+ "\n",
1016
+ " .dataframe tbody tr th {\n",
1017
+ " vertical-align: top;\n",
1018
+ " }\n",
1019
+ "\n",
1020
+ " .dataframe thead th {\n",
1021
+ " text-align: right;\n",
1022
+ " }\n",
1023
+ "</style>\n",
1024
+ "<table border=\"1\" class=\"dataframe\">\n",
1025
+ " <thead>\n",
1026
+ " <tr style=\"text-align: right;\">\n",
1027
+ " <th></th>\n",
1028
+ " <th>steps</th>\n",
1029
+ " </tr>\n",
1030
+ " <tr>\n",
1031
+ " <th>runname</th>\n",
1032
+ " <th></th>\n",
1033
+ " </tr>\n",
1034
+ " </thead>\n",
1035
+ " <tbody>\n",
1036
+ " <tr>\n",
1037
+ " <th>C4</th>\n",
1038
+ " <td>168</td>\n",
1039
+ " </tr>\n",
1040
+ " <tr>\n",
1041
+ " <th>Dolma</th>\n",
1042
+ " <td>168</td>\n",
1043
+ " </tr>\n",
1044
+ " <tr>\n",
1045
+ " <th>FineWeb</th>\n",
1046
+ " <td>168</td>\n",
1047
+ " </tr>\n",
1048
+ " <tr>\n",
1049
+ " <th>FineWeb-Edu</th>\n",
1050
+ " <td>82</td>\n",
1051
+ " </tr>\n",
1052
+ " <tr>\n",
1053
+ " <th>RedPajama2</th>\n",
1054
+ " <td>168</td>\n",
1055
+ " </tr>\n",
1056
+ " <tr>\n",
1057
+ " <th>RefinedWeb</th>\n",
1058
+ " <td>168</td>\n",
1059
+ " </tr>\n",
1060
+ " <tr>\n",
1061
+ " <th>SlimPajama</th>\n",
1062
+ " <td>168</td>\n",
1063
+ " </tr>\n",
1064
+ " <tr>\n",
1065
+ " <th>The Pile</th>\n",
1066
+ " <td>168</td>\n",
1067
+ " </tr>\n",
1068
+ " </tbody>\n",
1069
+ "</table>\n",
1070
+ "</div>"
1071
+ ],
1072
+ "text/plain": [
1073
+ " steps\n",
1074
+ "runname \n",
1075
+ "C4 168\n",
1076
+ "Dolma 168\n",
1077
+ "FineWeb 168\n",
1078
+ "FineWeb-Edu 82\n",
1079
+ "RedPajama2 168\n",
1080
+ "RefinedWeb 168\n",
1081
+ "SlimPajama 168\n",
1082
+ "The Pile 168"
1083
+ ]
1084
+ },
1085
+ "execution_count": 63,
1086
+ "metadata": {},
1087
+ "output_type": "execute_result"
1088
+ }
1089
+ ],
1090
+ "source": [
1091
+ "df_full.groupby(\"runname\").agg({\"steps\": \"count\"})"
1092
+ ]
1093
+ },
1094
+ {
1095
+ "cell_type": "code",
1096
+ "execution_count": 68,
1097
+ "metadata": {},
1098
+ "outputs": [],
1099
+ "source": [
1100
+ "fineweb_edu_steps = df_full[df_full[\"runname\"] == \"FineWeb-Edu\"][\"steps\"].unique()\n",
1101
+ "# Only selects steps that are in the fineweb_edu_steps \n",
1102
+ "df_full = df_full[df_full[\"steps\"].isin(fineweb_edu_steps)]"
1103
+ ]
1104
+ },
1105
+ {
1106
+ "cell_type": "code",
1107
+ "execution_count": 71,
1108
  "metadata": {},
1109
  "outputs": [],
1110
  "source": [
 
1150
  " \"data\": datas,\n",
1151
  " \"layout\": {\n",
1152
  " \"title\": {\n",
1153
+ " \"text\": \"Dataset ablations\"\n",
1154
  " },\n",
1155
  " }\n",
1156
  " }, f)\n",
 
1161
  " \"files\": files,\n",
1162
  " \"settings\": {\n",
1163
  " \"defaultMetric\": \"agg_score\",\n",
1164
+ " \"slider\":{\"min\":0,\"max\":30,\"default\":5},\n",
1165
+ " \"caption\": \"📚 FineWeb-Edu outperforms 🍷 FineWeb and all other open web datasets on our group of evaluation tasks.\"\n",
1166
  " }\n",
1167
  " }, f)"
1168
  ]
src/colors.mjs CHANGED
@@ -25,7 +25,7 @@
25
  // [154, 231, 201], // Aqua Green
26
  // ]
27
 
28
- const COLORS = [
29
  ["235", "102", "59"], // Burnt Orange
30
  ["46", "145", "229"], // Sky Blue
31
  ["225", "95", "153"], // Soft Magenta
 
25
  // [154, 231, 201], // Aqua Green
26
  // ]
27
 
28
+ export const COLORS = [
29
  ["235", "102", "59"], // Burnt Orange
30
  ["46", "145", "229"], // Sky Blue
31
  ["225", "95", "153"], // Soft Magenta
src/index.html CHANGED
@@ -601,10 +601,14 @@
601
 
602
  <h2>📚 FineWeb-Edu</h2>
603
 
604
- <figure style="text-align: center;">
605
- <img src="assets/images/dataset_comparisons_agg_fw_edu.png"/>
606
- <figcaption style="font-style: italic; margin-top: 10px;">📚 FineWeb-Edu outperforms 🍷 FineWeb and all other open web datasets on our group of evaluation tasks.</figcaption>
607
- </figure>
 
 
 
 
608
  <p><a href="https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu">📚 FineWeb-Edu</a> is an additional development of FineWeb that we are excited to introduce in this tech report and openly release. 📚 FineWeb-Edu is based on a new approach that has recently emerged for filtering LLM training datasets: using synthetic data to develop classifiers for identifying educational content. This technique was notably used in the trainings of Llama 3<d-cite bibtex-key="llama3modelcard"></d-cite> and Phi3<d-cite bibtex-key="abdin2024phi"></d-cite>, but its large-scale impact on web data filtering has, in our opinion, thur far not been publicly explored to its full potential.</p>
609
  <p>The popular Phi3 models were trained on 3.3 and 4.8 trillion tokens, with the paper<d-cite bibtex-key="abdin2024phi"></d-cite> stating:</p>
610
  <blockquote>Our training data consists of heavily filtered publicly available web data (according to the 'educational level') from various open internet sources, as well as synthetic LLM-generated data.</blockquote>
 
601
 
602
  <h2>📚 FineWeb-Edu</h2>
603
 
604
+ <div class="main-plot-container">
605
+ <figure>
606
+ <img src="assets/images/edu_ablations.png">
607
+ <figcaption style="font-style: italic; margin-top: 10px;">📚 FineWeb-Edu outperforms 🍷 FineWeb and all other open web datasets on our group of evaluation tasks.</figcaption>
608
+ </figure>
609
+ <div id="plot-edu_ablations">
610
+ </div>
611
+ </div>
612
  <p><a href="https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu">📚 FineWeb-Edu</a> is an additional development of FineWeb that we are excited to introduce in this tech report and openly release. 📚 FineWeb-Edu is based on a new approach that has recently emerged for filtering LLM training datasets: using synthetic data to develop classifiers for identifying educational content. This technique was notably used in the trainings of Llama 3<d-cite bibtex-key="llama3modelcard"></d-cite> and Phi3<d-cite bibtex-key="abdin2024phi"></d-cite>, but its large-scale impact on web data filtering has, in our opinion, thur far not been publicly explored to its full potential.</p>
613
  <p>The popular Phi3 models were trained on 3.3 and 4.8 trillion tokens, with the paper<d-cite bibtex-key="abdin2024phi"></d-cite> stating:</p>
614
  <blockquote>Our training data consists of heavily filtered publicly available web data (according to the 'educational level') from various open internet sources, as well as synthetic LLM-generated data.</blockquote>
src/plotting.js CHANGED
@@ -1,4 +1,4 @@
1
- import { getColor } from "./colors.mjs"
2
  import Plotly from "plotly.js/dist/plotly-basic"
3
  import _ from "lodash"
4
 
@@ -136,6 +136,22 @@ const getAutoRange = (traces) => {
136
  return [minX * 0.95, maxX * 1.05];
137
  };
138
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
139
 
140
  const createAblationPlottingElements = (
141
  plotElement,
@@ -202,8 +218,15 @@ const createAblationPlottingElements = (
202
  sliderContainer.appendChild(sliderInputContainer);
203
  controls.appendChild(sliderContainer);
204
  }
 
 
 
 
 
 
 
205
 
206
- return { dropdown, slider, plot };
207
  };
208
 
209
  const rollingWindow = function (data, windowSize) {
@@ -258,12 +281,12 @@ export const init_ablation_plot = function () {
258
  // Shared plot
259
  Plotly.newPlot(plot, []);
260
 
 
 
 
261
  async function updatePlot(dropdown, slider) {
262
  const metricName = dropdown?.value ?? settings.defaultMetric;
263
  const sliderValue = parseInt(slider?.value ?? 0);
264
- console.log(plotName)
265
- console.log(metricName)
266
- console.log(indexMapping)
267
  const metricData = await fetch(
268
  `${DATA_FOLDER}/${plotName}/${indexMapping[metricName]["file"]}`
269
  ).then((response) => response.json());
@@ -272,15 +295,16 @@ export const init_ablation_plot = function () {
272
  const y = rollingWindow(traceData.y, sliderValue);
273
  const x = traceData.x.slice(0, y.length);
274
  const plotSettings = settings.type === "bar" ? BAR_SETTINGS : LINE_SETTINGS;
 
275
  const trace = _.merge({}, {
276
  x: x,
277
  y: y,
278
  name: traceData.label ?? DATASET_ID_TO_NAME[key] ?? key,
279
  marker: {
280
- color: getColor(index),
281
  },
282
  line: {
283
- color: getColor(index),
284
  },
285
  }, plotSettings);
286
  traces.push(trace);
 
1
+ import { getColor, COLORS } from "./colors.mjs"
2
  import Plotly from "plotly.js/dist/plotly-basic"
3
  import _ from "lodash"
4
 
 
136
  return [minX * 0.95, maxX * 1.05];
137
  };
138
 
139
+ const getColorForTrace = (traceName, colorsMapping, index) => {
140
+ // First check if the color already exists in colorsMaping and if so return it
141
+ const reusedColor = colorsMapping.get(traceName)
142
+ if (reusedColor) {
143
+ return reusedColor
144
+ }
145
+
146
+ let color = getColor(index)
147
+ while (colorsMapping.has(color) && index < COLORS.length) {
148
+ index += 1
149
+ color = getColor(index)
150
+ }
151
+ colorsMapping.set(traceName, color)
152
+ return color
153
+ }
154
+
155
 
156
  const createAblationPlottingElements = (
157
  plotElement,
 
218
  sliderContainer.appendChild(sliderInputContainer);
219
  controls.appendChild(sliderContainer);
220
  }
221
+ let caption = undefined
222
+ if (settings.caption) {
223
+ caption = document.createElement("figcaption");
224
+ caption.classList.add("plotly_caption");
225
+ caption.textContent = settings.caption;
226
+ plotElement.appendChild(caption);
227
+ }
228
 
229
+ return { dropdown, slider, plot, caption };
230
  };
231
 
232
  const rollingWindow = function (data, windowSize) {
 
281
  // Shared plot
282
  Plotly.newPlot(plot, []);
283
 
284
+ // This is to ensure that the colors are consistent acrros different metrics
285
+ const colorsMapping = new Map()
286
+
287
  async function updatePlot(dropdown, slider) {
288
  const metricName = dropdown?.value ?? settings.defaultMetric;
289
  const sliderValue = parseInt(slider?.value ?? 0);
 
 
 
290
  const metricData = await fetch(
291
  `${DATA_FOLDER}/${plotName}/${indexMapping[metricName]["file"]}`
292
  ).then((response) => response.json());
 
295
  const y = rollingWindow(traceData.y, sliderValue);
296
  const x = traceData.x.slice(0, y.length);
297
  const plotSettings = settings.type === "bar" ? BAR_SETTINGS : LINE_SETTINGS;
298
+ const traceColor = getColorForTrace(key, colorsMapping, index)
299
  const trace = _.merge({}, {
300
  x: x,
301
  y: y,
302
  name: traceData.label ?? DATASET_ID_TO_NAME[key] ?? key,
303
  marker: {
304
+ color: traceColor,
305
  },
306
  line: {
307
+ color: traceColor,
308
  },
309
  }, plotSettings);
310
  traces.push(trace);
src/style.css CHANGED
@@ -21,6 +21,11 @@
21
  padding: 0px;
22
  }
23
 
 
 
 
 
 
24
  .plotly_controls {
25
  display: flex;
26
  flex-wrap: wrap;
@@ -240,4 +245,5 @@ d-contents nav > div > a {
240
  d-contents nav > div > a:hover,
241
  d-contents nav > ul > li > a:hover {
242
  text-decoration: none;
243
- }
 
 
21
  padding: 0px;
22
  }
23
 
24
+ .plotly_caption {
25
+ font-style: italic;
26
+ margin-top: 10px;
27
+ }
28
+
29
  .plotly_controls {
30
  display: flex;
31
  flex-wrap: wrap;
 
245
  d-contents nav > div > a:hover,
246
  d-contents nav > ul > li > a:hover {
247
  text-decoration: none;
248
+ }
249
+