๐ฆ Import Data Designerยถ
data_designer.configprovides access to the configuration API.DataDesigneris the main interface for data generation.
Inย [1]:
Copied!
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.
Inย [2]:
Copied!
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.
Inย [3]:
Copied!
# 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.
Inย [4]:
Copied!
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:
Inย [5]:
Copied!
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.
Inย [6]:
Copied!
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)
[12:12:19] [INFO] โ Validation passed
Next, let's add samplers to generate data related to the customer and their review.
Inย [7]:
Copied!
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)
[12:12:19] [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.
Inย [8]:
Copied!
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)
[12:12:19] [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)
[12:12:19] [INFO] ๐๏ธ Preview generation in progress
[12:12:19] [INFO] โ Validation passed
[12:12:19] [INFO] โ๏ธ Sorting column configs into a Directed Acyclic Graph
[12:12:19] [INFO] ๐ฉบ Running health checks for models...
[12:12:19] [INFO] |-- ๐ Checking 'nvidia/nemotron-3-nano-30b-a3b' in provider named 'nvidia' for model alias 'nemotron-nano-v3'...
[12:12:20] [INFO] |-- โ Passed!
[12:12:20] [INFO] ๐ฒ Preparing samplers to generate 2 records across 6 columns
[12:12:22] [INFO] ๐ llm-text model config for column 'product_name'
[12:12:22] [INFO] |-- model: 'nvidia/nemotron-3-nano-30b-a3b'
[12:12:22] [INFO] |-- model alias: 'nemotron-nano-v3'
[12:12:22] [INFO] |-- model provider: 'nvidia'
[12:12:22] [INFO] |-- inference parameters:
[12:12:22] [INFO] | |-- generation_type=chat-completion
[12:12:22] [INFO] | |-- max_parallel_requests=4
[12:12:22] [INFO] | |-- extra_body={'chat_template_kwargs': {'enable_thinking': False}}
[12:12:22] [INFO] | |-- temperature=1.00
[12:12:22] [INFO] | |-- top_p=1.00
[12:12:22] [INFO] | |-- max_tokens=2048
[12:12:22] [INFO] โก๏ธ Processing llm-text column 'product_name' with 4 concurrent workers
[12:12:22] [INFO] โฑ๏ธ llm-text column 'product_name' will report progress after each record
[12:12:22] [INFO] |-- ๐ llm-text column 'product_name' progress: 1/2 (50%) complete, 1 ok, 0 failed, 3.52 rec/s, eta 0.3s
[12:12:22] [INFO] |-- ๐ llm-text column 'product_name' progress: 2/2 (100%) complete, 2 ok, 0 failed, 6.04 rec/s, eta 0.0s
[12:12:22] [INFO] ๐ llm-text model config for column 'customer_review'
[12:12:22] [INFO] |-- model: 'nvidia/nemotron-3-nano-30b-a3b'
[12:12:22] [INFO] |-- model alias: 'nemotron-nano-v3'
[12:12:22] [INFO] |-- model provider: 'nvidia'
[12:12:22] [INFO] |-- inference parameters:
[12:12:22] [INFO] | |-- generation_type=chat-completion
[12:12:22] [INFO] | |-- max_parallel_requests=4
[12:12:22] [INFO] | |-- extra_body={'chat_template_kwargs': {'enable_thinking': False}}
[12:12:22] [INFO] | |-- temperature=1.00
[12:12:22] [INFO] | |-- top_p=1.00
[12:12:22] [INFO] | |-- max_tokens=2048
[12:12:22] [INFO] โก๏ธ Processing llm-text column 'customer_review' with 4 concurrent workers
[12:12:22] [INFO] โฑ๏ธ llm-text column 'customer_review' will report progress after each record
[12:12:24] [INFO] |-- ๐ llm-text column 'customer_review' progress: 1/2 (50%) complete, 1 ok, 0 failed, 0.61 rec/s, eta 1.6s
[12:12:24] [INFO] |-- ๐ llm-text column 'customer_review' progress: 2/2 (100%) complete, 2 ok, 0 failed, 0.93 rec/s, eta 0.0s
[12:12:25] [INFO] ๐ Model usage summary:
[12:12:25] [INFO] |-- model: nvidia/nemotron-3-nano-30b-a3b
[12:12:25] [INFO] |-- tokens: input=360, output=767, total=1127, tps=244
[12:12:25] [INFO] |-- requests: success=4, failed=0, total=4, rpm=52
[12:12:25] [INFO] ๐ Measuring dataset column statistics:
[12:12:25] [INFO] |-- ๐ฒ column: 'product_category'
[12:12:25] [INFO] |-- ๐ฒ column: 'product_subcategory'
[12:12:25] [INFO] |-- ๐ฒ column: 'target_age_range'
[12:12:25] [INFO] |-- ๐ฒ column: 'customer'
[12:12:25] [INFO] |-- ๐ฒ column: 'number_of_stars'
[12:12:25] [INFO] |-- ๐ฒ column: 'review_style'
[12:12:25] [INFO] |-- ๐ column: 'product_name'
[12:12:25] [INFO] |-- ๐ column: 'customer_review'
[12:12:25] [INFO] ๐พ 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()
Generated Columns โโโโโโโโโโโโโโโโโโโโโโโณโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ โ Name โ Value โ โกโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฉ โ product_category โ Home & Kitchen โ โโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ product_subcategory โ Appliances โ โโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ target_age_range โ 18-25 โ โโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ customer โ { โ โ โ 'uuid': '66d6d3f9-1c74-4461-9178-4db8f43a1fb8', โ โ โ 'locale': 'en_US', โ โ โ 'first_name': 'Ashley', โ โ โ 'last_name': 'Robles', โ โ โ 'middle_name': None, โ โ โ 'sex': 'Female', โ โ โ 'street_number': '472', โ โ โ 'street_name': 'Mays Dam', โ โ โ 'city': 'Dylanport', โ โ โ 'state': 'New York', โ โ โ 'postcode': '50833', โ โ โ 'age': 31, โ โ โ 'birth_date': '1994-04-16', โ โ โ 'country': 'Faroe Islands', โ โ โ 'marital_status': 'married_present', โ โ โ 'education_level': 'associates', โ โ โ 'unit': '', โ โ โ 'occupation': 'Medical sales representative', โ โ โ 'phone_number': '400.505.6335', โ โ โ 'bachelors_field': 'no_degree' โ โ โ } โ โโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ number_of_stars โ 5 โ โโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ review_style โ rambling โ โโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ product_name โ AuraGlow Induction Bowl โ โโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ customer_review โ Iโve been using the AuraGlow Induction Bowl for a solid month now and honestly, I โ โ โ canโt stop whispering โI love itโ to my goldfish every night. I mean, the first time โ โ โ I plugged it inโwell, it was more like I gave it a hopeful pat and said โHey, try โ โ โ not to be terrible,โ and then it turned on with this soft, buttery glow that made my โ โ โ kitchen feel like a tiny, peaceful galaxy. Itโs like the bowl decided to become a โ โ โ miniature sunrise for my countertop. โ โ โ โ โ โ I was skeptical at first: โWill it really stay lit for more than a week? Will it โ โ โ make weird noises like my old toaster?โ But nope, itโs as quiet as a library on a โ โ โ Sunday morning. The color transitions are smoother than my last relationship, โ โ โ shifting from a calming teal to a cozy amber with just a whisper of hand movement. โ โ โ And the best part? It actually *does* respond to motionโso I can sway my hand like a โ โ โ rainโdancing wizard and watch the bowl light up in perfect sync. I swear, my cat โ โ โ tried to catch the light and ended up looking like an overexcited firefly. (Heโs now โ โ โ demanding a dedicated โlightโcatchingโ career.) โ โ โ โ โ โ Oh, and the battery life? Letโs just say it outlasts my weekly attempts at learning โ โ โ a new language on Duolingo. It still feels like the first day we met, even after โ โ โ countless lateโnight snack raids. Iโve even started naming the bowl โSir Glowyโ โ โ โ because it deserves a royal titleโespecially when it dazzles my guests and makes โ โ โ them think Iโm some kind of avantโgarde, techโsavvy interior decorator. Honestly, โ โ โ Iโve turned my kitchen into a tiny, glowing sanctuary, and I canโt imagine ever โ โ โ replacing it. 5 stars? Absolutely, and a whole extra star for the sheer joy it โ โ โ brings to my daily coffee ritual. If youโre on the fence, just buy itโyour future โ โ โ self will thank you (and maybe your goldfish will finally approve). โ โโโโโโโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ [index: 0]
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 | Appliances | 18-25 | {'uuid': '66d6d3f9-1c74-4461-9178-4db8f43a1fb8... | 5 | rambling | AuraGlow Induction Bowl | Iโve been using the AuraGlow Induction Bowl fo... |
| 1 | Electronics | Cameras | 18-25 | {'uuid': '44079736-f61d-4804-b0f8-77641ad48bf2... | 5 | rambling | Beatrix Camera System | Well, I'll tell you what, I reckon I ain't nev... |
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 1 (50.0%) 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 1 (50.0%) โ uniform โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ review_style โ string โ 1 (50.0%) 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 2 (100.0%) โ 75.0 +/- 1.0 โ 5.0 +/- 1.4 โ โโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโค โ customer_review โ string โ 2 (100.0%) โ 69.5 +/- 1.5 โ 366.0 +/- 80.6 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
๐ Scale up!ยถ
Happy with your preview data?
Use the
createmethod to submit larger Data Designer generation jobs.
Inย [13]:
Copied!
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")
[12:12:25] [INFO] ๐จ Creating Data Designer dataset
[12:12:25] [INFO] โ Validation passed
[12:12:25] [INFO] โ๏ธ Sorting column configs into a Directed Acyclic Graph
[12:12:25] [INFO] ๐ฉบ Running health checks for models...
[12:12:25] [INFO] |-- ๐ Checking 'nvidia/nemotron-3-nano-30b-a3b' in provider named 'nvidia' for model alias 'nemotron-nano-v3'...
[12:12:26] [INFO] |-- โ Passed!
[12:12:26] [INFO] โณ Processing batch 1 of 1
[12:12:26] [INFO] ๐ฒ Preparing samplers to generate 10 records across 6 columns
[12:12:26] [INFO] ๐ llm-text model config for column 'product_name'
[12:12:26] [INFO] |-- model: 'nvidia/nemotron-3-nano-30b-a3b'
[12:12:26] [INFO] |-- model alias: 'nemotron-nano-v3'
[12:12:26] [INFO] |-- model provider: 'nvidia'
[12:12:26] [INFO] |-- inference parameters:
[12:12:26] [INFO] | |-- generation_type=chat-completion
[12:12:26] [INFO] | |-- max_parallel_requests=4
[12:12:26] [INFO] | |-- extra_body={'chat_template_kwargs': {'enable_thinking': False}}
[12:12:26] [INFO] | |-- temperature=1.00
[12:12:26] [INFO] | |-- top_p=1.00
[12:12:26] [INFO] | |-- max_tokens=2048
[12:12:26] [INFO] โก๏ธ Processing llm-text column 'product_name' with 4 concurrent workers
[12:12:26] [INFO] โฑ๏ธ llm-text column 'product_name' will report progress after each record
[12:12:26] [INFO] |-- ๐ด llm-text column 'product_name' progress: 1/10 (10%) complete, 1 ok, 0 failed, 3.77 rec/s, eta 2.4s
[12:12:26] [INFO] |-- ๐ด llm-text column 'product_name' progress: 2/10 (20%) complete, 2 ok, 0 failed, 6.00 rec/s, eta 1.3s
[12:12:26] [INFO] |-- ๐ฅฑ llm-text column 'product_name' progress: 3/10 (30%) complete, 3 ok, 0 failed, 8.40 rec/s, eta 0.8s
[12:12:26] [INFO] |-- ๐ฅฑ llm-text column 'product_name' progress: 4/10 (40%) complete, 4 ok, 0 failed, 11.15 rec/s, eta 0.5s
[12:12:26] [INFO] |-- ๐ llm-text column 'product_name' progress: 5/10 (50%) complete, 5 ok, 0 failed, 9.36 rec/s, eta 0.5s
[12:12:26] [INFO] |-- ๐ llm-text column 'product_name' progress: 6/10 (60%) complete, 6 ok, 0 failed, 10.21 rec/s, eta 0.4s
[12:12:26] [INFO] |-- ๐ llm-text column 'product_name' progress: 7/10 (70%) complete, 7 ok, 0 failed, 11.74 rec/s, eta 0.3s
[12:12:26] [INFO] |-- ๐ llm-text column 'product_name' progress: 8/10 (80%) complete, 8 ok, 0 failed, 12.78 rec/s, eta 0.2s
[12:12:27] [INFO] |-- ๐ llm-text column 'product_name' progress: 9/10 (90%) complete, 9 ok, 0 failed, 11.60 rec/s, eta 0.1s
[12:12:27] [INFO] |-- ๐คฉ llm-text column 'product_name' progress: 10/10 (100%) complete, 10 ok, 0 failed, 11.99 rec/s, eta 0.0s
[12:12:27] [INFO] ๐ llm-text model config for column 'customer_review'
[12:12:27] [INFO] |-- model: 'nvidia/nemotron-3-nano-30b-a3b'
[12:12:27] [INFO] |-- model alias: 'nemotron-nano-v3'
[12:12:27] [INFO] |-- model provider: 'nvidia'
[12:12:27] [INFO] |-- inference parameters:
[12:12:27] [INFO] | |-- generation_type=chat-completion
[12:12:27] [INFO] | |-- max_parallel_requests=4
[12:12:27] [INFO] | |-- extra_body={'chat_template_kwargs': {'enable_thinking': False}}
[12:12:27] [INFO] | |-- temperature=1.00
[12:12:27] [INFO] | |-- top_p=1.00
[12:12:27] [INFO] | |-- max_tokens=2048
[12:12:27] [INFO] โก๏ธ Processing llm-text column 'customer_review' with 4 concurrent workers
[12:12:27] [INFO] โฑ๏ธ llm-text column 'customer_review' will report progress after each record
[12:12:27] [INFO] |-- ๐ง๏ธ llm-text column 'customer_review' progress: 1/10 (10%) complete, 1 ok, 0 failed, 2.37 rec/s, eta 3.8s
[12:12:27] [INFO] |-- ๐ง๏ธ llm-text column 'customer_review' progress: 2/10 (20%) complete, 2 ok, 0 failed, 2.68 rec/s, eta 3.0s
[12:12:28] [INFO] |-- ๐ฆ๏ธ llm-text column 'customer_review' progress: 3/10 (30%) complete, 3 ok, 0 failed, 3.63 rec/s, eta 1.9s
[12:12:28] [INFO] |-- ๐ฆ๏ธ llm-text column 'customer_review' progress: 4/10 (40%) complete, 4 ok, 0 failed, 4.22 rec/s, eta 1.4s
[12:12:28] [INFO] |-- โ llm-text column 'customer_review' progress: 5/10 (50%) complete, 5 ok, 0 failed, 4.61 rec/s, eta 1.1s
[12:12:28] [INFO] |-- โ llm-text column 'customer_review' progress: 6/10 (60%) complete, 6 ok, 0 failed, 4.66 rec/s, eta 0.9s
[12:12:28] [INFO] |-- โ llm-text column 'customer_review' progress: 7/10 (70%) complete, 7 ok, 0 failed, 4.37 rec/s, eta 0.7s
[12:12:28] [INFO] |-- ๐ค๏ธ llm-text column 'customer_review' progress: 8/10 (80%) complete, 8 ok, 0 failed, 4.97 rec/s, eta 0.4s
[12:12:29] [INFO] |-- ๐ค๏ธ llm-text column 'customer_review' progress: 9/10 (90%) complete, 9 ok, 0 failed, 4.51 rec/s, eta 0.2s
[12:12:29] [INFO] |-- โ๏ธ llm-text column 'customer_review' progress: 10/10 (100%) complete, 10 ok, 0 failed, 3.70 rec/s, eta 0.0s
[12:12:30] [INFO] ๐ Model usage summary:
[12:12:30] [INFO] |-- model: nvidia/nemotron-3-nano-30b-a3b
[12:12:30] [INFO] |-- tokens: input=1779, output=1862, total=3641, tps=960
[12:12:30] [INFO] |-- requests: success=20, failed=0, total=20, rpm=316
[12:12:30] [INFO] ๐ Measuring dataset column statistics:
[12:12:30] [INFO] |-- ๐ฒ column: 'product_category'
[12:12:30] [INFO] |-- ๐ฒ column: 'product_subcategory'
[12:12:30] [INFO] |-- ๐ฒ column: 'target_age_range'
[12:12:30] [INFO] |-- ๐ฒ column: 'customer'
[12:12:30] [INFO] |-- ๐ฒ column: 'number_of_stars'
[12:12:30] [INFO] |-- ๐ฒ column: 'review_style'
[12:12:30] [INFO] |-- ๐ column: 'product_name'
[12:12:30] [INFO] |-- ๐ column: 'customer_review'
Inย [14]:
Copied!
# 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 | Home & Kitchen | Cookware | 65+ | {'age': 53, 'bachelors_field': 'no_degree', 'b... | 3 | brief | Hearthstone Heritage Cast Iron Dutch Oven | By Karen from West Kelly, CA โข Age 53 โข 3/5 St... |
| 1 | Home Office | Chairs | 18-25 | {'age': 28, 'bachelors_field': 'arts_humanitie... | 3 | brief | ErgoPulse Chair | Got the ErgoPulse Chair in a few days from Ohi... |
| 2 | Clothing | Activewear | 18-25 | {'age': 70, 'bachelors_field': 'no_degree', 'b... | 2 | rambling | SweatChase Neon Pulse Wear | I am a 70 year old lady from OโNeillton, Louis... |
| 3 | Electronics | Smartphones | 35-50 | {'age': 28, 'bachelors_field': 'stem_related',... | 2 | structured with bullet points | ZenithX ProCore | **Product:** ZenithX ProCore **Rating:** โ โ โ... |
| 4 | Electronics | Headphones | 65+ | {'age': 51, 'bachelors_field': 'no_degree', 'b... | 5 | brief | VintageSound Harmony Wireless Earbuds | Melissa from East Annmouth, TX: 5 stars. Vinta... |
Inย [15]:
Copied!
# 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 5 (50.0%) โ category โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ product_subcategory โ string โ 9 (90.0%) โ subcategory โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ target_age_range โ string โ 4 (40.0%) 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 4 (40.0%) 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 74.0 +/- 0.5 โ 5.5 +/- 1.2 โ โโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโค โ customer_review โ string โ 10 (100.0%) โ 70.5 +/- 1.6 โ 148.0 +/- 143.6 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: