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33 {
44 "cell_type" : " markdown"
5- "id" : " 45518a9f "
5+ "id" : " 538dab5d "
66 "metadata" : {},
77 "source" : [
88 " # 🎨 Data Designer Tutorial: The Basics\n "
1414 },
1515 {
1616 "cell_type" : " markdown"
17- "id" : " 5fdc5dc3 "
17+ "id" : " 8f148ac1 "
1818 "metadata" : {},
1919 "source" : [
2020 " ### 📦 Import Data Designer\n "
2626 },
2727 {
2828 "cell_type" : " markdown"
29- "id" : " dbcd0f47 "
29+ "id" : " 85a4f14c "
3030 "metadata" : {},
3131 "source" : [
3232 " ### ⚡ Colab Setup\n "
3737 {
3838 "cell_type" : " code"
3939 "execution_count" : null ,
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4141 "metadata" : {},
4242 "outputs" : [],
4343 "source" : [
4848 {
4949 "cell_type" : " code"
5050 "execution_count" : null ,
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5252 "metadata" : {},
5353 "outputs" : [],
5454 "source" : [
6666 {
6767 "cell_type" : " code"
6868 "execution_count" : null ,
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7070 "metadata" : {},
7171 "outputs" : [],
7272 "source" : [
7676 },
7777 {
7878 "cell_type" : " markdown"
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8080 "metadata" : {},
8181 "source" : [
8282 " ### ⚙️ Initialize the Data Designer interface\n "
8989 {
9090 "cell_type" : " code"
9191 "execution_count" : null ,
92- "id" : " 0db0e06f "
92+ "id" : " 64cd40d2 "
9393 "metadata" : {},
9494 "outputs" : [],
9595 "source" : [
9898 },
9999 {
100100 "cell_type" : " markdown"
101- "id" : " a7faf4d3 "
101+ "id" : " 25827639 "
102102 "metadata" : {},
103103 "source" : [
104104 " ### 🎛️ Define model configurations\n "
115115 {
116116 "cell_type" : " code"
117117 "execution_count" : null ,
118- "id" : " c8f7acb1 "
118+ "id" : " 6ccc7929 "
119119 "metadata" : {},
120120 "outputs" : [],
121121 "source" : [
145145 },
146146 {
147147 "cell_type" : " markdown"
148- "id" : " 8626355d "
148+ "id" : " ffeb8a6e "
149149 "metadata" : {},
150150 "source" : [
151151 " ### 🏗️ Initialize the Data Designer Config Builder\n "
160160 {
161161 "cell_type" : " code"
162162 "execution_count" : null ,
163- "id" : " a6acf698 "
163+ "id" : " 0f13f0dd "
164164 "metadata" : {},
165165 "outputs" : [],
166166 "source" : [
169169 },
170170 {
171171 "cell_type" : " markdown"
172- "id" : " ba922526 "
172+ "id" : " ba888091 "
173173 "metadata" : {},
174174 "source" : [
175175 " ## 🎲 Getting started with sampler columns\n "
186186 {
187187 "cell_type" : " code"
188188 "execution_count" : null ,
189- "id" : " c968224a "
189+ "id" : " 9d831f16 "
190190 "metadata" : {},
191191 "outputs" : [],
192192 "source" : [
195195 },
196196 {
197197 "cell_type" : " markdown"
198- "id" : " 1de6016f "
198+ "id" : " 6bf9f07e "
199199 "metadata" : {},
200200 "source" : [
201201 " Let's start designing our product review dataset by adding product category and subcategory columns.\n "
204204 {
205205 "cell_type" : " code"
206206 "execution_count" : null ,
207- "id" : " 84abc84e "
207+ "id" : " 47236c2a "
208208 "metadata" : {},
209209 "outputs" : [],
210210 "source" : [
285285 },
286286 {
287287 "cell_type" : " markdown"
288- "id" : " 36b85598 "
288+ "id" : " 5fef86e3 "
289289 "metadata" : {},
290290 "source" : [
291291 " Next, let's add samplers to generate data related to the customer and their review.\n "
294294 {
295295 "cell_type" : " code"
296296 "execution_count" : null ,
297- "id" : " 8023d6a8 "
297+ "id" : " 6f4282c6 "
298298 "metadata" : {},
299299 "outputs" : [],
300300 "source" : [
331331 },
332332 {
333333 "cell_type" : " markdown"
334- "id" : " 3e44a399 "
334+ "id" : " 09c6d0bd "
335335 "metadata" : {},
336336 "source" : [
337337 " ## 🦜 LLM-generated columns\n "
346346 {
347347 "cell_type" : " code"
348348 "execution_count" : null ,
349- "id" : " 9a2f8c4e "
349+ "id" : " b136c9c9 "
350350 "metadata" : {},
351351 "outputs" : [],
352352 "source" : [
382382 },
383383 {
384384 "cell_type" : " markdown"
385- "id" : " 6d51de88 "
385+ "id" : " 77b21c85 "
386386 "metadata" : {},
387387 "source" : [
388388 " ### 🔁 Iteration is key – preview the dataset!\n "
399399 {
400400 "cell_type" : " code"
401401 "execution_count" : null ,
402- "id" : " e6f56af6 "
402+ "id" : " 9e7d8e57 "
403403 "metadata" : {},
404404 "outputs" : [],
405405 "source" : [
409409 {
410410 "cell_type" : " code"
411411 "execution_count" : null ,
412- "id" : " b786c171 "
412+ "id" : " 35a3e198 "
413413 "metadata" : {},
414414 "outputs" : [],
415415 "source" : [
420420 {
421421 "cell_type" : " code"
422422 "execution_count" : null ,
423- "id" : " cb4ab4fa "
423+ "id" : " ac646977 "
424424 "metadata" : {},
425425 "outputs" : [],
426426 "source" : [
430430 },
431431 {
432432 "cell_type" : " markdown"
433- "id" : " e915db4c "
433+ "id" : " 135c82ff "
434434 "metadata" : {},
435435 "source" : [
436436 " ### 📊 Analyze the generated data\n "
443443 {
444444 "cell_type" : " code"
445445 "execution_count" : null ,
446- "id" : " 6c79d1f9 "
446+ "id" : " 8b0290e0 "
447447 "metadata" : {},
448448 "outputs" : [],
449449 "source" : [
453453 },
454454 {
455455 "cell_type" : " markdown"
456- "id" : " 83616444 "
456+ "id" : " f780d07f "
457457 "metadata" : {},
458458 "source" : [
459459 " ### 🆙 Scale up!\n "
466466 {
467467 "cell_type" : " code"
468468 "execution_count" : null ,
469- "id" : " 5c87397a "
469+ "id" : " e040f619 "
470470 "metadata" : {},
471471 "outputs" : [],
472472 "source" : [
476476 {
477477 "cell_type" : " code"
478478 "execution_count" : null ,
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480480 "metadata" : {},
481481 "outputs" : [],
482482 "source" : [
489489 {
490490 "cell_type" : " code"
491491 "execution_count" : null ,
492- "id" : " 8d4acda5 "
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493493 "metadata" : {},
494494 "outputs" : [],
495495 "source" : [
501501 },
502502 {
503503 "cell_type" : " markdown"
504- "id" : " 760f62f7 "
504+ "id" : " 59725e56 "
505505 "metadata" : {},
506506 "source" : [
507507 " ## ⏭️ Next Steps\n "
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