๐ฆ Import Data Designerยถ
data_designer.configprovides access to the configuration API.DataDesigneris the main interface for data generation.
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import data_designer.config as dd
from data_designer.interface import DataDesigner
import data_designer.config as dd
from data_designer.interface import DataDesigner
โ๏ธ Initialize the Data Designer interfaceยถ
DataDesigneris the main object responsible for managing the data generation process.When initialized without arguments, the default model providers are used.
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data_designer = DataDesigner()
data_designer = DataDesigner()
๐๏ธ Define model configurationsยถ
Each
ModelConfigdefines a model that can be used during the generation process.The "model alias" is used to reference the model in the Data Designer config (as we will see below).
The "model provider" is the external service that hosts the model (see the model config docs for more details).
By default, we use build.nvidia.com as the model provider.
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# This name is set in the model provider configuration.
MODEL_PROVIDER = "nvidia"
# The model ID is from build.nvidia.com.
MODEL_ID = "nvidia/nemotron-3-nano-30b-a3b"
# We choose this alias to be descriptive for our use case.
MODEL_ALIAS = "nemotron-nano-v3"
model_configs = [
dd.ModelConfig(
alias=MODEL_ALIAS,
model=MODEL_ID,
provider=MODEL_PROVIDER,
inference_parameters=dd.ChatCompletionInferenceParams(
temperature=1.0,
top_p=1.0,
max_tokens=2048,
extra_body={"chat_template_kwargs": {"enable_thinking": False}},
),
)
]
# This name is set in the model provider configuration.
MODEL_PROVIDER = "nvidia"
# The model ID is from build.nvidia.com.
MODEL_ID = "nvidia/nemotron-3-nano-30b-a3b"
# We choose this alias to be descriptive for our use case.
MODEL_ALIAS = "nemotron-nano-v3"
model_configs = [
dd.ModelConfig(
alias=MODEL_ALIAS,
model=MODEL_ID,
provider=MODEL_PROVIDER,
inference_parameters=dd.ChatCompletionInferenceParams(
temperature=1.0,
top_p=1.0,
max_tokens=2048,
extra_body={"chat_template_kwargs": {"enable_thinking": False}},
),
)
]
๐๏ธ Initialize the Data Designer Config Builderยถ
The Data Designer config defines the dataset schema and generation process.
The config builder provides an intuitive interface for building this configuration.
The list of model configs is provided to the builder at initialization.
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config_builder = dd.DataDesignerConfigBuilder(model_configs=model_configs)
config_builder = dd.DataDesignerConfigBuilder(model_configs=model_configs)
๐ฒ Getting started with sampler columnsยถ
Sampler columns offer non-LLM based generation of synthetic data.
They are particularly useful for steering the diversity of the generated data, as we demonstrate below.
You can view available samplers using the config builder's info property:
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config_builder.info.display("samplers")
config_builder.info.display("samplers")
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ NeMo Data Designer Samplers โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ โโโโโโโโโโโโโโโโโโโโโโณโโโโโโโโโโโโโโโโโโโโโโโโโโโณโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโณโโโโโโโโโโโณโโโโโโโโโโโโโโโโโโโ โ Type โ Parameter โ Data Type โ Required โ Constraints โ โกโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฉ โ bernoulli โ p โ number โ โ โ >= 0.0, <= 1.0 โ โ โ sampler_type โ string โ โ โ โโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโค โ bernoulli_mixture โ p โ number โ โ โ >= 0.0, <= 1.0 โ โ โ dist_name โ string โ โ โ โ โ โ dist_params โ dict โ โ โ โ โ โ sampler_type โ string โ โ โ โโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโค โ binomial โ n โ integer โ โ โ โ โ โ p โ number โ โ โ >= 0.0, <= 1.0 โ โ โ sampler_type โ string โ โ โ โโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโค โ category โ values โ string[] | integer[] | number[] โ โ โ len > 1 โ โ โ weights โ number[] | null โ โ โ โ โ sampler_type โ string โ โ โ โโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโค โ datetime โ start โ string โ โ โ โ โ โ end โ string โ โ โ โ โ โ unit โ string โ โ โ โ โ sampler_type โ string โ โ โ โโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโค โ gaussian โ mean โ number โ โ โ โ โ โ stddev โ number โ โ โ โ โ โ decimal_places โ integer | null โ โ โ โ โ sampler_type โ string โ โ โ โโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโค โ person โ locale โ string โ โ โ โ โ sex โ string | null โ โ โ โ โ city โ string | string[] | null โ โ โ โ โ age_range โ integer[] โ โ len > 2, len < 2 โ โ โ select_field_values โ object | null โ โ โ โ โ with_synthetic_personas โ boolean โ โ โ โ โ sampler_type โ string โ โ โ โโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโค โ person_from_faker โ locale โ string โ โ โ โ โ sex โ string | null โ โ โ โ โ city โ string | string[] | null โ โ โ โ โ age_range โ integer[] โ โ len > 2, len < 2 โ โ โ sampler_type โ string โ โ โ โโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโค โ poisson โ mean โ number โ โ โ โ โ โ sampler_type โ string โ โ โ โโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโค โ scipy โ dist_name โ string โ โ โ โ โ โ dist_params โ dict โ โ โ โ โ โ decimal_places โ integer | null โ โ โ โ โ sampler_type โ string โ โ โ โโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโค โ subcategory โ category โ string โ โ โ โ โ โ values โ dict โ โ โ โ โ โ sampler_type โ string โ โ โ โโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโค โ timedelta โ dt_min โ integer โ โ โ >= 0 โ โ โ dt_max โ integer โ โ โ > 0 โ โ โ reference_column_name โ string โ โ โ โ โ โ unit โ string โ โ โ โ โ sampler_type โ string โ โ โ โโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโค โ uniform โ low โ number โ โ โ โ โ โ high โ number โ โ โ โ โ โ decimal_places โ integer | null โ โ โ โ โ sampler_type โ string โ โ โ โโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโค โ uuid โ prefix โ string | null โ โ โ โ โ short_form โ boolean โ โ โ โ โ uppercase โ boolean โ โ โ โ โ sampler_type โ string โ โ โ โโโโโโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโดโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโโ
Let's start designing our product review dataset by adding product category and subcategory columns.
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config_builder.add_column(
dd.SamplerColumnConfig(
name="product_category",
sampler_type=dd.SamplerType.CATEGORY,
params=dd.CategorySamplerParams(
values=[
"Electronics",
"Clothing",
"Home & Kitchen",
"Books",
"Home Office",
],
),
)
)
config_builder.add_column(
dd.SamplerColumnConfig(
name="product_subcategory",
sampler_type=dd.SamplerType.SUBCATEGORY,
params=dd.SubcategorySamplerParams(
category="product_category",
values={
"Electronics": [
"Smartphones",
"Laptops",
"Headphones",
"Cameras",
"Accessories",
],
"Clothing": [
"Men's Clothing",
"Women's Clothing",
"Winter Coats",
"Activewear",
"Accessories",
],
"Home & Kitchen": [
"Appliances",
"Cookware",
"Furniture",
"Decor",
"Organization",
],
"Books": [
"Fiction",
"Non-Fiction",
"Self-Help",
"Textbooks",
"Classics",
],
"Home Office": [
"Desks",
"Chairs",
"Storage",
"Office Supplies",
"Lighting",
],
},
),
)
)
config_builder.add_column(
dd.SamplerColumnConfig(
name="target_age_range",
sampler_type=dd.SamplerType.CATEGORY,
params=dd.CategorySamplerParams(values=["18-25", "25-35", "35-50", "50-65", "65+"]),
)
)
# Optionally validate that the columns are configured correctly.
data_designer.validate(config_builder)
config_builder.add_column(
dd.SamplerColumnConfig(
name="product_category",
sampler_type=dd.SamplerType.CATEGORY,
params=dd.CategorySamplerParams(
values=[
"Electronics",
"Clothing",
"Home & Kitchen",
"Books",
"Home Office",
],
),
)
)
config_builder.add_column(
dd.SamplerColumnConfig(
name="product_subcategory",
sampler_type=dd.SamplerType.SUBCATEGORY,
params=dd.SubcategorySamplerParams(
category="product_category",
values={
"Electronics": [
"Smartphones",
"Laptops",
"Headphones",
"Cameras",
"Accessories",
],
"Clothing": [
"Men's Clothing",
"Women's Clothing",
"Winter Coats",
"Activewear",
"Accessories",
],
"Home & Kitchen": [
"Appliances",
"Cookware",
"Furniture",
"Decor",
"Organization",
],
"Books": [
"Fiction",
"Non-Fiction",
"Self-Help",
"Textbooks",
"Classics",
],
"Home Office": [
"Desks",
"Chairs",
"Storage",
"Office Supplies",
"Lighting",
],
},
),
)
)
config_builder.add_column(
dd.SamplerColumnConfig(
name="target_age_range",
sampler_type=dd.SamplerType.CATEGORY,
params=dd.CategorySamplerParams(values=["18-25", "25-35", "35-50", "50-65", "65+"]),
)
)
# Optionally validate that the columns are configured correctly.
data_designer.validate(config_builder)
[12:13:35] [INFO] โ Validation passed
Next, let's add samplers to generate data related to the customer and their review.
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config_builder.add_column(
dd.SamplerColumnConfig(
name="customer",
sampler_type=dd.SamplerType.PERSON_FROM_FAKER,
params=dd.PersonFromFakerSamplerParams(age_range=[18, 70], locale="en_US"),
)
)
config_builder.add_column(
dd.SamplerColumnConfig(
name="number_of_stars",
sampler_type=dd.SamplerType.UNIFORM,
params=dd.UniformSamplerParams(low=1, high=5),
convert_to="int", # Convert the sampled float to an integer.
)
)
config_builder.add_column(
dd.SamplerColumnConfig(
name="review_style",
sampler_type=dd.SamplerType.CATEGORY,
params=dd.CategorySamplerParams(
values=["rambling", "brief", "detailed", "structured with bullet points"],
weights=[1, 2, 2, 1],
),
)
)
data_designer.validate(config_builder)
config_builder.add_column(
dd.SamplerColumnConfig(
name="customer",
sampler_type=dd.SamplerType.PERSON_FROM_FAKER,
params=dd.PersonFromFakerSamplerParams(age_range=[18, 70], locale="en_US"),
)
)
config_builder.add_column(
dd.SamplerColumnConfig(
name="number_of_stars",
sampler_type=dd.SamplerType.UNIFORM,
params=dd.UniformSamplerParams(low=1, high=5),
convert_to="int", # Convert the sampled float to an integer.
)
)
config_builder.add_column(
dd.SamplerColumnConfig(
name="review_style",
sampler_type=dd.SamplerType.CATEGORY,
params=dd.CategorySamplerParams(
values=["rambling", "brief", "detailed", "structured with bullet points"],
weights=[1, 2, 2, 1],
),
)
)
data_designer.validate(config_builder)
[12:13:35] [INFO] โ Validation passed
๐ฆ LLM-generated columnsยถ
The real power of Data Designer comes from leveraging LLMs to generate text, code, and structured data.
When prompting the LLM, we can use Jinja templating to reference other columns in the dataset.
As we see below, nested json fields can be accessed using dot notation.
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config_builder.add_column(
dd.LLMTextColumnConfig(
name="product_name",
prompt=(
"You are a helpful assistant that generates product names. DO NOT add quotes around the product name.\n\n"
"Come up with a creative product name for a product in the '{{ product_category }}' category, focusing "
"on products related to '{{ product_subcategory }}'. The target age range of the ideal customer is "
"{{ target_age_range }} years old. Respond with only the product name, no other text."
),
model_alias=MODEL_ALIAS,
)
)
config_builder.add_column(
dd.LLMTextColumnConfig(
name="customer_review",
prompt=(
"You are a customer named {{ customer.first_name }} from {{ customer.city }}, {{ customer.state }}. "
"You are {{ customer.age }} years old and recently purchased a product called {{ product_name }}. "
"Write a review of this product, which you gave a rating of {{ number_of_stars }} stars. "
"The style of the review should be '{{ review_style }}'. "
"Respond with only the review, no other text."
),
model_alias=MODEL_ALIAS,
)
)
data_designer.validate(config_builder)
config_builder.add_column(
dd.LLMTextColumnConfig(
name="product_name",
prompt=(
"You are a helpful assistant that generates product names. DO NOT add quotes around the product name.\n\n"
"Come up with a creative product name for a product in the '{{ product_category }}' category, focusing "
"on products related to '{{ product_subcategory }}'. The target age range of the ideal customer is "
"{{ target_age_range }} years old. Respond with only the product name, no other text."
),
model_alias=MODEL_ALIAS,
)
)
config_builder.add_column(
dd.LLMTextColumnConfig(
name="customer_review",
prompt=(
"You are a customer named {{ customer.first_name }} from {{ customer.city }}, {{ customer.state }}. "
"You are {{ customer.age }} years old and recently purchased a product called {{ product_name }}. "
"Write a review of this product, which you gave a rating of {{ number_of_stars }} stars. "
"The style of the review should be '{{ review_style }}'. "
"Respond with only the review, no other text."
),
model_alias=MODEL_ALIAS,
)
)
data_designer.validate(config_builder)
[12:13:35] [INFO] โ Validation passed
๐ Iteration is key โย preview the dataset!ยถ
Use the
previewmethod to generate a sample of records quickly.Inspect the results for quality and format issues.
Adjust column configurations, prompts, or parameters as needed.
Re-run the preview until satisfied.
Inย [9]:
Copied!
preview = data_designer.preview(config_builder, num_records=2)
preview = data_designer.preview(config_builder, num_records=2)
[12:13:35] [INFO] ๐ผ๏ธ Preview generation in progress
[12:13:35] [INFO] โ Validation passed
[12:13:36] [INFO] โ๏ธ Sorting column configs into a Directed Acyclic Graph
[12:13:36] [INFO] ๐ฉบ Running health checks for models...
[12:13:36] [INFO] |-- ๐ Checking 'nvidia/nemotron-3-nano-30b-a3b' in provider named 'nvidia' for model alias 'nemotron-nano-v3'...
[12:13:37] [INFO] |-- โ Passed!
[12:13:37] [INFO] ๐ฒ Preparing samplers to generate 2 records across 6 columns
[12:13:38] [INFO] ๐ llm-text model config for column 'product_name'
[12:13:38] [INFO] |-- model: 'nvidia/nemotron-3-nano-30b-a3b'
[12:13:38] [INFO] |-- model alias: 'nemotron-nano-v3'
[12:13:38] [INFO] |-- model provider: 'nvidia'
[12:13:38] [INFO] |-- inference parameters:
[12:13:38] [INFO] | |-- generation_type=chat-completion
[12:13:38] [INFO] | |-- max_parallel_requests=4
[12:13:38] [INFO] | |-- extra_body={'chat_template_kwargs': {'enable_thinking': False}}
[12:13:38] [INFO] | |-- temperature=1.00
[12:13:38] [INFO] | |-- top_p=1.00
[12:13:38] [INFO] | |-- max_tokens=2048
[12:13:38] [INFO] โก๏ธ Processing llm-text column 'product_name' with 4 concurrent workers
[12:13:38] [INFO] โฑ๏ธ llm-text column 'product_name' will report progress after each record
[12:13:39] [INFO] |-- ๐ llm-text column 'product_name' progress: 1/2 (50%) complete, 1 ok, 0 failed, 2.07 rec/s, eta 0.5s
[12:13:40] [INFO] |-- ๐คฉ llm-text column 'product_name' progress: 2/2 (100%) complete, 2 ok, 0 failed, 1.74 rec/s, eta 0.0s
[12:13:40] [INFO] ๐ llm-text model config for column 'customer_review'
[12:13:40] [INFO] |-- model: 'nvidia/nemotron-3-nano-30b-a3b'
[12:13:40] [INFO] |-- model alias: 'nemotron-nano-v3'
[12:13:40] [INFO] |-- model provider: 'nvidia'
[12:13:40] [INFO] |-- inference parameters:
[12:13:40] [INFO] | |-- generation_type=chat-completion
[12:13:40] [INFO] | |-- max_parallel_requests=4
[12:13:40] [INFO] | |-- extra_body={'chat_template_kwargs': {'enable_thinking': False}}
[12:13:40] [INFO] | |-- temperature=1.00
[12:13:40] [INFO] | |-- top_p=1.00
[12:13:40] [INFO] | |-- max_tokens=2048
[12:13:40] [INFO] โก๏ธ Processing llm-text column 'customer_review' with 4 concurrent workers
[12:13:40] [INFO] โฑ๏ธ llm-text column 'customer_review' will report progress after each record
[12:13:40] [INFO] |-- ๐ฅ llm-text column 'customer_review' progress: 1/2 (50%) complete, 1 ok, 0 failed, 2.49 rec/s, eta 0.4s
[12:13:44] [INFO] |-- ๐ llm-text column 'customer_review' progress: 2/2 (100%) complete, 2 ok, 0 failed, 0.43 rec/s, eta 0.0s
[12:13:44] [INFO] ๐ Model usage summary:
[12:13:44] [INFO] |-- model: nvidia/nemotron-3-nano-30b-a3b
[12:13:44] [INFO] |-- tokens: input=528, output=1334, total=1862, tps=242
[12:13:44] [INFO] |-- requests: success=4, failed=0, total=4, rpm=31
[12:13:44] [INFO] ๐ Measuring dataset column statistics:
[12:13:44] [INFO] |-- ๐ฒ column: 'product_category'
[12:13:44] [INFO] |-- ๐ฒ column: 'product_subcategory'
[12:13:44] [INFO] |-- ๐ฒ column: 'target_age_range'
[12:13:44] [INFO] |-- ๐ฒ column: 'customer'
[12:13:44] [INFO] |-- ๐ฒ column: 'number_of_stars'
[12:13:44] [INFO] |-- ๐ฒ column: 'review_style'
[12:13:44] [INFO] |-- ๐ column: 'product_name'
[12:13:45] [INFO] |-- ๐ column: 'customer_review'
[12:13:45] [INFO] โ๏ธ Preview complete!
Inย [10]:
Copied!
# Run this cell multiple times to cycle through the 2 preview records.
preview.display_sample_record()
# Run this cell multiple times to cycle through the 2 preview records.
preview.display_sample_record()
[index: 0]
Generated Columns โโโโโโโโโโโโโโโโโโโโโโโณโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ โ Name โ Value โ โกโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฉ โ product_category โ Home & Kitchen โ โโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ product_subcategory โ Organization โ โโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ target_age_range โ 25-35 โ โโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ customer โ { โ โ โ 'uuid': '6d3ad1e3-7321-46d0-9861-b843fe603a6f', โ โ โ 'locale': 'en_US', โ โ โ 'first_name': 'Ann', โ โ โ 'last_name': 'Campbell', โ โ โ 'middle_name': None, โ โ โ 'sex': 'Female', โ โ โ 'street_number': '359', โ โ โ 'street_name': 'Torres Plains', โ โ โ 'city': 'Jeffreytown', โ โ โ 'state': 'New Mexico', โ โ โ 'postcode': '40158', โ โ โ 'age': 39, โ โ โ 'birth_date': '1986-04-06', โ โ โ 'country': 'Mauritius', โ โ โ 'marital_status': 'separated', โ โ โ 'education_level': 'secondary_education', โ โ โ 'unit': '', โ โ โ 'occupation': 'Surveyor, planning and development', โ โ โ 'phone_number': '+1-579-216-7582x9880', โ โ โ 'bachelors_field': 'no_degree' โ โ โ } โ โโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ number_of_stars โ 5 โ โโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ review_style โ detailed โ โโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ product_name โ Nestify HomeLift Organizer โ โโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ customer_review โ **Nestify HomeLift Organizer โ 5โStar Review** โ โ โ โ โ โ Iโm Ann, 39, from Jeffreytown, New Mexico, and Iโve just installed the Nestify โ โ โ HomeLift Organizer in my master bathroom for the past three weeks. After some โ โ โ initial skepticism (Iโve tried a few โorganizerโ products that promised the moon but โ โ โ delivered cramped, flimsy trays), Iโm thrilled to report that this product โ โ โ absolutely lives up to its claims. โ โ โ โ โ โ --- โ โ โ โ โ โ ### First Impressions โ โ โ The packaging is clean and ecoโfriendly: a recyclable cardboard box with a magnetic โ โ โ closure that feels sturdy enough to protect the product during shipping. Inside, โ โ โ everything is wrapped in biodegradable cornโstarch bagsโno plastic. The box itself โ โ โ is printed with clear, minimalist diagrams that show how the unit expands from a โ โ โ compact 12โฏรโฏ9โฏรโฏ6โฏin. to a generous 18โฏรโฏ12โฏรโฏ10โฏin. when fully opened. The visual โ โ โ instructions are so straightforward that I was able to set it up in under ten โ โ โ minutes, without consulting any manuals. โ โ โ โ โ โ --- โ โ โ โ โ โ ### Build Quality & Materials โ โ โ Nestify uses a hybrid of highโgrade ABS plastic and brushed aluminum accents, which โ โ โ gives the organizer a premium feel while staying lightweight. The corners are โ โ โ reinforced with a rubberized coating that prevents scratches on the bathroom tiles โ โ โ or countertop. The hinges are stainlessโsteel, silently smooth, and have a slight โ โ โ resistance that lets the panels stay exactly where you want them without wobbling. โ โ โ โ โ โ --- โ โ โ โ โ โ ### Functionality โ โ โ 1. **Four Expandable Compartments** โ The organizer expands from a shallow tray โ โ โ (ideal for toiletries like toothpaste, floss, and travel-size shampoo) to a deep, โ โ โ multiโsectioned chamber (perfect for larger bottles of body wash, razors, and even a โ โ โ spare set of spare towels). โ โ โ 2. **Adjustable Shelves** โ Two interior shelves are heightโadjustable with builtโin โ โ โ clickโlock tabs. This flexibility lets me store everything from cotton swabs to a โ โ โ stack of folded face cloths without them sliding around. โ โ โ 3. **WaterโResistant Lining** โ A thin silicone mat lines the bottom of each โ โ โ compartment, keeping moisture away from the plastic and preventing mildew buildup. โ โ โ Iโve noticed no water rings after showers, and the mat wipes clean with a damp cloth โ โ โ in seconds. โ โ โ 4. **Integrated Drawer** โ A hidden pullโout drawer slides out on smooth โ โ โ ballโbearing rollers, ideal for storing smaller items like makeup sponges, contact โ โ โ lens cases, or even a compact firstโaid kit. It opens with a soft, audible click โ โ โ that tells you itโs securely latched. โ โ โ โ โ โ All of this fits neatly on a standard bathroom vanity, and because the sides expand โ โ โ outward rather than upward, Iโm not forced to give up any precious countertop space. โ โ โ โ โ โ --- โ โ โ โ โ โ ### Everyday Use Experiences โ โ โ **Morning routine:** I place my toothpaste, toothbrush, and floss on the shallow โ โ โ tray; the silicone mat catches any stray water, keeping the tray dry. The drawer โ โ โ holds my electric toothbrush charger, so I never have to hunt for an outlet. โ โ โ โ โ โ **Shower prep:** The deep compartment holds a 24โoz bottle of body wash, a loofah, โ โ โ and an extra roll of body wipes. The adjustable shelf lets me slide the body wash โ โ โ front and back so that I can easily grab it without reaching over the other items. โ โ โ โ โ โ **Travel days:** When Iโm away for a weekend, I collapse the organizer back to its โ โ โ compact form, pop it into my luggage bag, and nestle the silicone liners inside my โ โ โ carryโon. The whole thing folds down to a flat rectangle that slides right under my โ โ โ suitcase. โ โ โ โ โ โ --- โ โ โ โ โ โ ### Durability & Maintenance โ โ โ After three weeks of daily useโmultiple showers, occasional splashes of hair dye, โ โ โ and a few accidental dropsโIโm pleased to report that there are no scratches, warps, โ โ โ or stains. The silicone liners are dishwasher safe, and a simple rinse with mild โ โ โ soap keeps everything looking brand new. The ABS plastic resists cracking even when โ โ โ I accidentally overโextend one of the expanding panels. โ โ โ โ โ โ --- โ โ โ โ โ โ ### Value for Money โ โ โ At $39.99, the Nestify Organizer lands in the midโrange price bracket for bathroom โ โ โ storage solutions. However, the thoughtful design, robust construction, and genuine โ โ โ versatility feel far more premium than the price tag suggests. Compared with buying โ โ โ separate shower caddies, wall hooks, and countertop trays, Nestify consolidates โ โ โ everything into a single, expandable unit, saving both space and money. โ โ โ โ โ โ --- โ โ โ โ โ โ ### Bottom Line โ โ โ If youโre someoneโlike meโwho enjoys a clean, organized bathroom without sacrificing โ โ โ style or space, the Nestify HomeLift Organizer is a gameโchanger. Its seamless โ โ โ expansion, thoughtful shelving, waterโresistant lining, and sturdy build make it โ โ โ more than just a storage box; itโs a semiโpermanent upgrade to everyday life. Iโve โ โ โ already recommended it to three friends, and Iโd purchase another one without โ โ โ hesitation. โ โ โ โ โ โ **Five stars all the way.** โ โโโโโโโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
Inย [11]:
Copied!
# The preview dataset is available as a pandas DataFrame.
preview.dataset
# The preview dataset is available as a pandas DataFrame.
preview.dataset
Out[11]:
| product_category | product_subcategory | target_age_range | customer | number_of_stars | review_style | product_name | customer_review | |
|---|---|---|---|---|---|---|---|---|
| 0 | Home & Kitchen | Organization | 25-35 | {'uuid': '6d3ad1e3-7321-46d0-9861-b843fe603a6f... | 5 | detailed | Nestify HomeLift Organizer | **Nestify HomeLift Organizer โ 5โStar Review**... |
| 1 | Clothing | Men's Clothing | 65+ | {'uuid': '6ee81db0-4a97-47ae-9eed-d84f2cfd19b2... | 2 | brief | Timeless Warmth Knit Cardigan \nOffers year-r... | Too loose in shoulders, pouch gets in way when... |
๐ Analyze the generated dataยถ
Data Designer automatically generates a basic statistical analysis of the generated data.
This analysis is available via the
analysisproperty of generation result objects.
Inย [12]:
Copied!
# Print the analysis as a table.
preview.analysis.to_report()
# Print the analysis as a table.
preview.analysis.to_report()
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ ๐จ Data Designer Dataset Profile โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ Dataset Overview โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโณโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโณโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ โ number of records โ number of columns โ percent complete records โ โกโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฉ โ 2 โ 8 โ 100.0% โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ ๐ฒ Sampler Columns โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโณโโโโโโโโโโโโโโโโโโณโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโณโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ โ column name โ data type โ number unique values โ sampler type โ โกโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฉ โ product_category โ string โ 2 (100.0%) โ category โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ product_subcategory โ string โ 2 (100.0%) โ subcategory โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ target_age_range โ string โ 2 (100.0%) โ category โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ customer โ dict โ 2 (100.0%) โ person_from_faker โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ number_of_stars โ int โ 2 (100.0%) โ uniform โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ review_style โ string โ 2 (100.0%) โ category โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ ๐ LLM-Text Columns โโโโโโโโโโโโโโโโโโโโโโโโณโโโโโโโโโโโโโโโโณโโโโโโโโโโโโโโโโโโโโโโโโโโโโโณโโโโโโโโโโโโโโโโโโโโโโณโโโโโโโโโโโโโโโโโโโโโโโโ โ โ โ โ prompt tokens โ completion tokens โ โ column name โ data type โ number unique values โ per record โ per record โ โกโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฉ โ product_name โ string โ 2 (100.0%) โ 74.0 +/- 0.0 โ 84.0 +/- 108.9 โ โโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโค โ customer_review โ string โ 2 (100.0%) โ 147.5 +/- 76.5 โ 537.0 +/- 726.9 โ โโโโโโโโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโโโโโโโ โญโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ Table Notes โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ โ โ โ 1. All token statistics are based on a sample of max(1000, len(dataset)) records. โ โ 2. Tokens are calculated using tiktoken's cl100k_base tokenizer. โ โ โ โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
๐ Scale up!ยถ
Happy with your preview data?
Use the
createmethod to submit larger Data Designer generation jobs.
Inย [13]:
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results = data_designer.create(config_builder, num_records=10, dataset_name="tutorial-1")
results = data_designer.create(config_builder, num_records=10, dataset_name="tutorial-1")
[12:13:45] [INFO] ๐จ Creating Data Designer dataset
[12:13:45] [INFO] โ Validation passed
[12:13:45] [INFO] โ๏ธ Sorting column configs into a Directed Acyclic Graph
[12:13:45] [INFO] ๐ฉบ Running health checks for models...
[12:13:45] [INFO] |-- ๐ Checking 'nvidia/nemotron-3-nano-30b-a3b' in provider named 'nvidia' for model alias 'nemotron-nano-v3'...
[12:13:45] [INFO] |-- โ Passed!
[12:13:45] [INFO] โณ Processing batch 1 of 1
[12:13:45] [INFO] ๐ฒ Preparing samplers to generate 10 records across 6 columns
[12:13:45] [INFO] ๐ llm-text model config for column 'product_name'
[12:13:45] [INFO] |-- model: 'nvidia/nemotron-3-nano-30b-a3b'
[12:13:45] [INFO] |-- model alias: 'nemotron-nano-v3'
[12:13:45] [INFO] |-- model provider: 'nvidia'
[12:13:45] [INFO] |-- inference parameters:
[12:13:45] [INFO] | |-- generation_type=chat-completion
[12:13:45] [INFO] | |-- max_parallel_requests=4
[12:13:45] [INFO] | |-- extra_body={'chat_template_kwargs': {'enable_thinking': False}}
[12:13:45] [INFO] | |-- temperature=1.00
[12:13:45] [INFO] | |-- top_p=1.00
[12:13:45] [INFO] | |-- max_tokens=2048
[12:13:45] [INFO] โก๏ธ Processing llm-text column 'product_name' with 4 concurrent workers
[12:13:45] [INFO] โฑ๏ธ llm-text column 'product_name' will report progress after each record
[12:13:46] [INFO] |-- ๐ฑ llm-text column 'product_name' progress: 1/10 (10%) complete, 1 ok, 0 failed, 3.04 rec/s, eta 3.0s
[12:13:46] [INFO] |-- ๐ฑ llm-text column 'product_name' progress: 2/10 (20%) complete, 2 ok, 0 failed, 5.44 rec/s, eta 1.5s
[12:13:46] [INFO] |-- ๐บ llm-text column 'product_name' progress: 3/10 (30%) complete, 3 ok, 0 failed, 7.83 rec/s, eta 0.9s
[12:13:46] [INFO] |-- ๐บ llm-text column 'product_name' progress: 4/10 (40%) complete, 4 ok, 0 failed, 10.05 rec/s, eta 0.6s
[12:13:46] [INFO] |-- ๐ธ llm-text column 'product_name' progress: 5/10 (50%) complete, 5 ok, 0 failed, 7.81 rec/s, eta 0.6s
[12:13:46] [INFO] |-- ๐ธ llm-text column 'product_name' progress: 6/10 (60%) complete, 6 ok, 0 failed, 9.05 rec/s, eta 0.4s
[12:13:46] [INFO] |-- ๐ธ llm-text column 'product_name' progress: 7/10 (70%) complete, 7 ok, 0 failed, 10.06 rec/s, eta 0.3s
[12:13:46] [INFO] |-- ๐ผ llm-text column 'product_name' progress: 8/10 (80%) complete, 8 ok, 0 failed, 10.47 rec/s, eta 0.2s
[12:13:46] [INFO] |-- ๐ผ llm-text column 'product_name' progress: 9/10 (90%) complete, 9 ok, 0 failed, 9.52 rec/s, eta 0.1s
[12:13:47] [INFO] |-- ๐ฆ llm-text column 'product_name' progress: 10/10 (100%) complete, 10 ok, 0 failed, 6.63 rec/s, eta 0.0s
[12:13:47] [INFO] ๐ llm-text model config for column 'customer_review'
[12:13:47] [INFO] |-- model: 'nvidia/nemotron-3-nano-30b-a3b'
[12:13:47] [INFO] |-- model alias: 'nemotron-nano-v3'
[12:13:47] [INFO] |-- model provider: 'nvidia'
[12:13:47] [INFO] |-- inference parameters:
[12:13:47] [INFO] | |-- generation_type=chat-completion
[12:13:47] [INFO] | |-- max_parallel_requests=4
[12:13:47] [INFO] | |-- extra_body={'chat_template_kwargs': {'enable_thinking': False}}
[12:13:47] [INFO] | |-- temperature=1.00
[12:13:47] [INFO] | |-- top_p=1.00
[12:13:47] [INFO] | |-- max_tokens=2048
[12:13:47] [INFO] โก๏ธ Processing llm-text column 'customer_review' with 4 concurrent workers
[12:13:47] [INFO] โฑ๏ธ llm-text column 'customer_review' will report progress after each record
[12:13:47] [INFO] |-- ๐ถ llm-text column 'customer_review' progress: 1/10 (10%) complete, 1 ok, 0 failed, 1.93 rec/s, eta 4.7s
[12:13:50] [INFO] |-- ๐ถ llm-text column 'customer_review' progress: 2/10 (20%) complete, 2 ok, 0 failed, 0.56 rec/s, eta 14.3s
[12:13:51] [INFO] |-- ๐ด llm-text column 'customer_review' progress: 3/10 (30%) complete, 3 ok, 0 failed, 0.81 rec/s, eta 8.7s
[12:13:51] [INFO] |-- ๐ด llm-text column 'customer_review' progress: 4/10 (40%) complete, 4 ok, 0 failed, 1.02 rec/s, eta 5.9s
[12:13:51] [INFO] |-- ๐ llm-text column 'customer_review' progress: 5/10 (50%) complete, 5 ok, 0 failed, 1.17 rec/s, eta 4.3s
[12:13:52] [INFO] |-- ๐ llm-text column 'customer_review' progress: 6/10 (60%) complete, 6 ok, 0 failed, 1.25 rec/s, eta 3.2s
[12:13:52] [INFO] |-- ๐ llm-text column 'customer_review' progress: 7/10 (70%) complete, 7 ok, 0 failed, 1.43 rec/s, eta 2.1s
[12:13:52] [INFO] |-- โ๏ธ llm-text column 'customer_review' progress: 8/10 (80%) complete, 8 ok, 0 failed, 1.54 rec/s, eta 1.3s
[12:13:52] [INFO] |-- โ๏ธ llm-text column 'customer_review' progress: 9/10 (90%) complete, 9 ok, 0 failed, 1.70 rec/s, eta 0.6s
[12:13:52] [INFO] |-- ๐ llm-text column 'customer_review' progress: 10/10 (100%) complete, 10 ok, 0 failed, 1.83 rec/s, eta 0.0s
[12:13:53] [INFO] ๐ Model usage summary:
[12:13:53] [INFO] |-- model: nvidia/nemotron-3-nano-30b-a3b
[12:13:53] [INFO] |-- tokens: input=1774, output=3515, total=5289, tps=726
[12:13:53] [INFO] |-- requests: success=20, failed=0, total=20, rpm=164
[12:13:53] [INFO] ๐ Measuring dataset column statistics:
[12:13:53] [INFO] |-- ๐ฒ column: 'product_category'
[12:13:53] [INFO] |-- ๐ฒ column: 'product_subcategory'
[12:13:53] [INFO] |-- ๐ฒ column: 'target_age_range'
[12:13:53] [INFO] |-- ๐ฒ column: 'customer'
[12:13:53] [INFO] |-- ๐ฒ column: 'number_of_stars'
[12:13:53] [INFO] |-- ๐ฒ column: 'review_style'
[12:13:53] [INFO] |-- ๐ column: 'product_name'
[12:13:53] [INFO] |-- ๐ column: 'customer_review'
Inย [14]:
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# Load the generated dataset as a pandas DataFrame.
dataset = results.load_dataset()
dataset.head()
# Load the generated dataset as a pandas DataFrame.
dataset = results.load_dataset()
dataset.head()
Out[14]:
| product_category | product_subcategory | target_age_range | customer | number_of_stars | review_style | product_name | customer_review | |
|---|---|---|---|---|---|---|---|---|
| 0 | Books | Classics | 25-35 | {'age': 54, 'bachelors_field': 'stem', 'birth_... | 5 | detailed | Ink & Ivory Classics Set | I was instantly captivated by the Ink & Ivory ... |
| 1 | Books | Classics | 65+ | {'age': 63, 'bachelors_field': 'education', 'b... | 4 | detailed | Timeless Tales Pocket Reader | **Timeless Tales Pocket Reader โ 4โStar Review... |
| 2 | Home & Kitchen | Furniture | 50-65 | {'age': 30, 'bachelors_field': 'no_degree', 'b... | 4 | detailed | SereneEase Recliner | As Christina from Barbershire, Montana, Iโve o... |
| 3 | Books | Fiction | 65+ | {'age': 23, 'bachelors_field': 'no_degree', 'b... | 2 | brief | The Golden Quill Collection | Iโm disappointed with The Golden Quill Collect... |
| 4 | Home & Kitchen | Appliances | 18-25 | {'age': 36, 'bachelors_field': 'business', 'bi... | 2 | detailed | NeonChef Portable Induction Cooktop | I was excited to try the NeonChef Portable Ind... |
Inย [15]:
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# Load the analysis results into memory.
analysis = results.load_analysis()
analysis.to_report()
# Load the analysis results into memory.
analysis = results.load_analysis()
analysis.to_report()
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ ๐จ Data Designer Dataset Profile โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ Dataset Overview โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโณโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโณโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ โ number of records โ number of columns โ percent complete records โ โกโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฉ โ 10 โ 8 โ 100.0% โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ ๐ฒ Sampler Columns โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโณโโโโโโโโโโโโโโโโโโณโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโณโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ โ column name โ data type โ number unique values โ sampler type โ โกโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฉ โ product_category โ string โ 4 (40.0%) โ category โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ product_subcategory โ string โ 9 (90.0%) โ subcategory โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ target_age_range โ string โ 5 (50.0%) โ category โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ customer โ dict โ 10 (100.0%) โ person_from_faker โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ number_of_stars โ int โ 4 (40.0%) โ uniform โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ review_style โ string โ 3 (30.0%) โ category โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ ๐ LLM-Text Columns โโโโโโโโโโโโโโโโโโโโโโโโโณโโโโโโโโโโโโโโโโณโโโโโโโโโโโโโโโโโโโโโโโโโโโโโณโโโโโโโโโโโโโโโโโโโโณโโโโโโโโโโโโโโโโโโโโโโโโโ โ โ โ โ prompt tokens โ completion tokens โ โ column name โ data type โ number unique values โ per record โ per record โ โกโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฉ โ product_name โ string โ 10 (100.0%) โ 73.5 +/- 1.2 โ 5.5 +/- 1.4 โ โโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโค โ customer_review โ string โ 10 (100.0%) โ 70.5 +/- 1.4 โ 263.0 +/- 285.3 โ โโโโโโโโโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโโโโโโโโ โญโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ Table Notes โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ โ โ โ 1. All token statistics are based on a sample of max(1000, len(dataset)) records. โ โ 2. Tokens are calculated using tiktoken's cl100k_base tokenizer. โ โ โ โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โญ๏ธ Next Stepsยถ
Now that you've seen the basics of Data Designer, check out the following notebooks to learn more about: