The Basics
🎨 Data Designer Tutorial: The Basics
📚 What you'll learn
This notebook demonstrates the basics of Data Designer by generating a simple product review dataset.
📦 Import Data Designer
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data_designer.configprovides access to the configuration API. -
DataDesigneris the main interface for data generation.
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⚙️ Initialize the Data Designer interface
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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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🎛️ Define model configurations
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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).
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The "model provider" is the external service that hosts the model (see the model config docs for more details).
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By default, we use build.nvidia.com as the model provider.
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🏗️ Initialize the Data Designer Config Builder
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The Data Designer config defines the dataset schema and generation process.
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The config builder provides an intuitive interface for building this configuration.
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The list of model configs is provided to the builder at initialization.
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🎲 Getting started with sampler columns
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Sampler columns offer non-LLM based generation of synthetic data.
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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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Output
─────────────────────────────────────────── NeMo Data Designer Samplers ─────────────────────────────────────────── ┏━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┓ ┃ Type ┃ Parameter ┃ Data Type ┃ Required ┃ Constraints ┃ ┡━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━┩ │ bernoulli │ p │ number │ ✓ │ >= 0.0, <= 1.0 │ │ │ sampler_type │ string │ │ │ ├────────────────────┼──────────────────────────┼───────────────────────────────────┼──────────┼──────────────────┤ │ bernoulli_mixture │ p │ number │ ✓ │ >= 0.0, <= 1.0 │ │ │ dist_name │ string │ ✓ │ │ │ │ dist_params │ dict │ ✓ │ │ │ │ sampler_type │ string │ │ │ ├────────────────────┼──────────────────────────┼───────────────────────────────────┼──────────┼──────────────────┤ │ binomial │ n │ integer │ ✓ │ │ │ │ p │ number │ ✓ │ >= 0.0, <= 1.0 │ │ │ sampler_type │ string │ │ │ ├────────────────────┼──────────────────────────┼───────────────────────────────────┼──────────┼──────────────────┤ │ category │ values │ string[] | integer[] | number[] │ ✓ │ len > 1 │ │ │ weights │ number[] | null │ │ │ │ │ sampler_type │ string │ │ │ ├────────────────────┼──────────────────────────┼───────────────────────────────────┼──────────┼──────────────────┤ │ datetime │ start │ string │ ✓ │ │ │ │ end │ string │ ✓ │ │ │ │ unit │ string │ │ │ │ │ sampler_type │ string │ │ │ ├────────────────────┼──────────────────────────┼───────────────────────────────────┼──────────┼──────────────────┤ │ gaussian │ mean │ number │ ✓ │ │ │ │ stddev │ number │ ✓ │ │ │ │ decimal_places │ integer | null │ │ │ │ │ sampler_type │ string │ │ │ ├────────────────────┼──────────────────────────┼───────────────────────────────────┼──────────┼──────────────────┤ │ person │ locale │ string │ │ │ │ │ sex │ string | null │ │ │ │ │ city │ string | string[] | null │ │ │ │ │ age_range │ integer[] │ │ len > 2, len < 2 │ │ │ select_field_values │ object | null │ │ │ │ │ with_synthetic_personas │ boolean │ │ │ │ │ sampler_type │ string │ │ │ ├────────────────────┼──────────────────────────┼───────────────────────────────────┼──────────┼──────────────────┤ │ person_from_faker │ locale │ string │ │ │ │ │ sex │ string | null │ │ │ │ │ city │ string | string[] | null │ │ │ │ │ age_range │ integer[] │ │ len > 2, len < 2 │ │ │ sampler_type │ string │ │ │ ├────────────────────┼──────────────────────────┼───────────────────────────────────┼──────────┼──────────────────┤ │ poisson │ mean │ number │ ✓ │ │ │ │ sampler_type │ string │ │ │ ├────────────────────┼──────────────────────────┼───────────────────────────────────┼──────────┼──────────────────┤ │ scipy │ dist_name │ string │ ✓ │ │ │ │ dist_params │ dict │ ✓ │ │ │ │ decimal_places │ integer | null │ │ │ │ │ sampler_type │ string │ │ │ ├────────────────────┼──────────────────────────┼───────────────────────────────────┼──────────┼──────────────────┤ │ subcategory │ category │ string │ ✓ │ │ │ │ values │ dict │ ✓ │ │ │ │ sampler_type │ string │ │ │ ├────────────────────┼──────────────────────────┼───────────────────────────────────┼──────────┼──────────────────┤ │ timedelta │ dt_min │ integer │ ✓ │ >= 0 │ │ │ dt_max │ integer │ ✓ │ > 0 │ │ │ reference_column_name │ string │ ✓ │ │ │ │ unit │ string │ │ │ │ │ sampler_type │ string │ │ │ ├────────────────────┼──────────────────────────┼───────────────────────────────────┼──────────┼──────────────────┤ │ uniform │ low │ number │ ✓ │ │ │ │ high │ number │ ✓ │ │ │ │ decimal_places │ integer | null │ │ │ │ │ sampler_type │ string │ │ │ ├────────────────────┼──────────────────────────┼───────────────────────────────────┼──────────┼──────────────────┤ │ uuid │ prefix │ string | null │ │ │ │ │ short_form │ boolean │ │ │ │ │ uppercase │ boolean │ │ │ │ │ sampler_type │ string │ │ │ └────────────────────┴──────────────────────────┴───────────────────────────────────┴──────────┴──────────────────┘
Let's start designing our product review dataset by adding product category and subcategory columns.
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Output
[19:55:07] [INFO] ✅ Validation passed
Next, let's add samplers to generate data related to the customer and their review.
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Output
[19:55:07] [INFO] ✅ Validation passed
🦜 LLM-generated columns
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The real power of Data Designer comes from leveraging LLMs to generate text, code, and structured data.
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When prompting the LLM, we can use Jinja templating to reference other columns in the dataset.
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As we see below, nested json fields can be accessed using dot notation.
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Output
[19:55:07] [INFO] ✅ Validation passed
🔁 Iteration is key – preview the dataset!
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Use the
previewmethod to generate a sample of records quickly. -
Inspect the results for quality and format issues.
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Adjust column configurations, prompts, or parameters as needed.
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Re-run the preview until satisfied.
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Output
[19:55:07] [INFO] 🖼️ Preview generation in progress
[19:55:07] [INFO] |-- 🔒 Jinja rendering engine: secure
[19:55:07] [INFO] ✅ Validation passed
[19:55:07] [INFO] ⛓️ Sorting column configs into a Directed Acyclic Graph
[19:55:07] [INFO] Skipping model health checks because DATA_DESIGNER_SKIP_MODEL_HEALTH_CHECKS=1
[19:55:07] [INFO] ⚡ Using async task-queue preview
[19:55:07] [INFO] 📝 llm-text model config for column 'product_name'
[19:55:07] [INFO] |-- model: 'nvidia/nemotron-3.5-lightning-30b-a3b'
[19:55:07] [INFO] |-- model alias: 'nemotron-lightning'
[19:55:07] [INFO] |-- model provider: 'nvidia'
[19:55:07] [INFO] |-- inference parameters:
[19:55:07] [INFO] | |-- generation_type=chat-completion
[19:55:07] [INFO] | |-- max_parallel_requests=4
[19:55:07] [INFO] | |-- extra_body={'chat_template_kwargs': {'enable_thinking': False}}[19:55:07] [INFO] | |-- temperature=1.00
[19:55:07] [INFO] | |-- top_p=0.95
[19:55:07] [INFO] | |-- max_tokens=2048
[19:55:07] [INFO] 📝 llm-text model config for column 'customer_review'
[19:55:07] [INFO] |-- model: 'nvidia/nemotron-3.5-lightning-30b-a3b'
[19:55:07] [INFO] |-- model alias: 'nemotron-lightning'
[19:55:07] [INFO] |-- model provider: 'nvidia'
[19:55:07] [INFO] |-- inference parameters:
[19:55:07] [INFO] | |-- generation_type=chat-completion
[19:55:07] [INFO] | |-- max_parallel_requests=4
[19:55:07] [INFO] | |-- extra_body={'chat_template_kwargs': {'enable_thinking': False}}[19:55:07] [INFO] | |-- temperature=1.00
[19:55:07] [INFO] | |-- top_p=0.95
[19:55:07] [INFO] | |-- max_tokens=2048
[19:55:07] [INFO] ⚡️ Async generation: 2 column(s) (column 'product_name', column 'customer_review'), 4 tasks across 1 row group(s)
[19:55:07] [INFO] 🚀 (1/1) Dispatching with 2 records
[19:55:07] [INFO] 🎲 (1/1) Preparing samplers to generate 2 records across 6 columns
[19:55:11] [INFO] 📊 Progress [4.2s]:
[19:55:11] [INFO] |-- ☀️ column 'product_name': 2/2 (100%) 0.5 rec/s
[19:55:11] [INFO] |-- 🐔 column 'customer_review': 2/2 (100%) 0.5 rec/s
[19:55:11] [INFO] ✅ Async generation complete [4.2s]: 4 ok, 0 failed across 2 column(s)
[19:55:11] [INFO] 📊 Model usage summary:
[19:55:11] [INFO] |-- model: nvidia/nemotron-3.5-lightning-30b-a3b
[19:55:11] [INFO] |-- tokens: input=356, output=139, reasoning=0, total=495, tps=117
[19:55:11] [INFO] |-- requests: success=4, failed=0, total=4, rpm=57
[19:55:11] [INFO] 📐 Measuring dataset column statistics:
[19:55:11] [INFO] |-- 🎲 column: 'product_category'
[19:55:11] [INFO] |-- 🎲 column: 'product_subcategory'
[19:55:11] [INFO] |-- 🎲 column: 'target_age_range'
[19:55:11] [INFO] |-- 🎲 column: 'customer'
[19:55:11] [INFO] |-- 🎲 column: 'number_of_stars'
[19:55:11] [INFO] |-- 🎲 column: 'review_style'
[19:55:11] [INFO] |-- 📝 column: 'product_name'
[19:55:11] [INFO] |-- 📝 column: 'customer_review'
[19:55:11] [INFO] 🙌 Preview complete!
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Output
[index: 0]
Generated Columns ┏━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ ┃ Name ┃ Value ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ │ product_category │ Books │ ├─────────────────────────┼──────────────────────────────────────────────────────────────────────────────────┤ │ product_subcategory │ Non-Fiction │ ├─────────────────────────┼──────────────────────────────────────────────────────────────────────────────────┤ │ target_age_range │ 65+ │ ├─────────────────────────┼──────────────────────────────────────────────────────────────────────────────────┤ │ customer │ { │ │ │ 'uuid': '98898411-29f9-4650-90e6-7a272fba1045', │ │ │ 'locale': 'en_US', │ │ │ 'first_name': 'Robert', │ │ │ 'last_name': 'Decker', │ │ │ 'middle_name': None, │ │ │ 'sex': 'Male', │ │ │ 'street_number': '0378', │ │ │ 'street_name': 'Robert Summit', │ │ │ 'city': 'Lake Jamesmouth', │ │ │ 'state': 'South Carolina', │ │ │ 'postcode': '02831', │ │ │ 'age': 49, │ │ │ 'birth_date': '1977-02-14', │ │ │ 'country': 'South Africa', │ │ │ 'marital_status': 'married_present', │ │ │ 'education_level': 'doctorate', │ │ │ 'unit': '', │ │ │ 'occupation': 'Corporate investment banker', │ │ │ 'phone_number': '952.991.8873x881', │ │ │ 'bachelors_field': 'business' │ │ │ } │ ├─────────────────────────┼──────────────────────────────────────────────────────────────────────────────────┤ │ number_of_stars │ 2 │ ├─────────────────────────┼──────────────────────────────────────────────────────────────────────────────────┤ │ review_style │ structured with bullet points │ ├─────────────────────────┼──────────────────────────────────────────────────────────────────────────────────┤ │ product_name │ Golden Chapters: A Life In Stories │ ├─────────────────────────┼──────────────────────────────────────────────────────────────────────────────────┤ │ customer_review │ • The concept of a life told in stories sounded promising and unique. │ │ │ • Unfortunately, the narrative flow felt fragmented and disjointed. │ │ │ • I struggled to connect with the characters or the overarching plot. │ │ │ • The execution felt rushed, leaving many questions unanswered. │ │ │ • For the price, I expected a more polished and cohesive experience. │ │ │ • It left me feeling underwhelmed and longing for more depth. │ └─────────────────────────┴──────────────────────────────────────────────────────────────────────────────────┘
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Output
| product_category | product_subcategory | target_age_range | customer | number_of_stars | review_style | product_name | customer_review | |
|---|---|---|---|---|---|---|---|---|
| 0 | Books | Non-Fiction | 65+ | {'uuid': '98898411-29f9-4650-90e6-7a272fba1045... | 2 | structured with bullet points | Golden Chapters: A Life In Stories | • The concept of a life told in stories sounde... |
| 1 | Electronics | Smartphones | 18-25 | {'uuid': '568f1dc4-2d77-46c9-86b7-eabf2563b1c1... | 2 | brief | EchoBand | This band is okay, but not worth the price. Fo... |
📊 Analyze the generated data
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Data Designer automatically generates a basic statistical analysis of the generated data.
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This analysis is available via the
analysisproperty of generation result objects.
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Output
──────────────────────────────────────── 🎨 Data Designer Dataset Profile ───────────────────────────────────────── Dataset Overview ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ ┃ number of records ┃ number of columns ┃ percent complete records ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ │ 2 │ 8 │ 100.0% │ └─────────────────────────────────┴─────────────────────────────────┴─────────────────────────────────────────────┘ 🎲 Sampler Columns ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ ┃ column name ┃ data type ┃ number unique values ┃ sampler type ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ │ product_category │ string │ 2 (100.0%) │ category │ ├────────────────────────────────┼─────────────────┼─────────────────────────────────┼────────────────────────────┤ │ product_subcategory │ string │ 2 (100.0%) │ subcategory │ ├────────────────────────────────┼─────────────────┼─────────────────────────────────┼────────────────────────────┤ │ target_age_range │ string │ 2 (100.0%) │ category │ ├────────────────────────────────┼─────────────────┼─────────────────────────────────┼────────────────────────────┤ │ customer │ dict │ 2 (100.0%) │ person_from_faker │ ├────────────────────────────────┼─────────────────┼─────────────────────────────────┼────────────────────────────┤ │ number_of_stars │ int │ 1 (50.0%) │ uniform │ ├────────────────────────────────┼─────────────────┼─────────────────────────────────┼────────────────────────────┤ │ review_style │ string │ 2 (100.0%) │ category │ └────────────────────────────────┴─────────────────┴─────────────────────────────────┴────────────────────────────┘ 📝 LLM-Text Columns ┏━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓ ┃ ┃ ┃ ┃ prompt tokens ┃ completion tokens ┃ ┃ column name ┃ data type ┃ number unique values ┃ per record ┃ per record ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩ │ product_name │ string │ 2 (100.0%) │ 73.5 +/- 0.5 │ 4.5 +/- 3.5 │ ├───────────────────────┼───────────────┼────────────────────────────┼───────────────────┼────────────────────────┤ │ customer_review │ string │ 2 (100.0%) │ 71.0 +/- 4.0 │ 60.0 +/- 25.5 │ └───────────────────────┴───────────────┴────────────────────────────┴───────────────────┴────────────────────────┘ ╭────────────────────────────────────────────────── Table Notes ──────────────────────────────────────────────────╮ │ │ │ 1. All token statistics are based on a sample of max(1000, len(dataset)) records. │ │ 2. Tokens are calculated using tiktoken's cl100k_base tokenizer. │ │ │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
🆙 Scale up!
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Happy with your preview data?
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Use the
createmethod to submit larger Data Designer generation jobs.
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Output
[19:55:11] [INFO] OpenTelemetry metrics available at http://127.0.0.1:9464/metrics
[19:55:11] [INFO] 🎨 Creating Data Designer dataset
[19:55:11] [INFO] |-- 🔒 Jinja rendering engine: secure
[19:55:11] [INFO] ✅ Validation passed
[19:55:11] [INFO] ⛓️ Sorting column configs into a Directed Acyclic Graph
[19:55:11] [INFO] Skipping model health checks because DATA_DESIGNER_SKIP_MODEL_HEALTH_CHECKS=1
[19:55:11] [INFO] ⚡ Using async task-queue builder
[19:55:11] [INFO] 📝 llm-text model config for column 'product_name'
[19:55:11] [INFO] |-- model: 'nvidia/nemotron-3.5-lightning-30b-a3b'
[19:55:11] [INFO] |-- model alias: 'nemotron-lightning'
[19:55:11] [INFO] |-- model provider: 'nvidia'
[19:55:11] [INFO] |-- inference parameters:
[19:55:11] [INFO] | |-- generation_type=chat-completion
[19:55:11] [INFO] | |-- max_parallel_requests=4
[19:55:11] [INFO] | |-- extra_body={'chat_template_kwargs': {'enable_thinking': False}}[19:55:11] [INFO] | |-- temperature=1.00
[19:55:11] [INFO] | |-- top_p=0.95
[19:55:11] [INFO] | |-- max_tokens=2048
[19:55:11] [INFO] 📝 llm-text model config for column 'customer_review'
[19:55:11] [INFO] |-- model: 'nvidia/nemotron-3.5-lightning-30b-a3b'
[19:55:11] [INFO] |-- model alias: 'nemotron-lightning'
[19:55:11] [INFO] |-- model provider: 'nvidia'
[19:55:11] [INFO] |-- inference parameters:
[19:55:11] [INFO] | |-- generation_type=chat-completion
[19:55:11] [INFO] | |-- max_parallel_requests=4
[19:55:11] [INFO] | |-- extra_body={'chat_template_kwargs': {'enable_thinking': False}}[19:55:11] [INFO] | |-- temperature=1.00
[19:55:11] [INFO] | |-- top_p=0.95
[19:55:11] [INFO] | |-- max_tokens=2048
[19:55:11] [INFO] ⚡️ Async generation: 2 column(s) (column 'product_name', column 'customer_review'), 20 tasks across 1 row group(s)
[19:55:11] [INFO] 🚀 (1/1) Dispatching with 10 records
[19:55:11] [INFO] 🎲 (1/1) Preparing samplers to generate 10 records across 6 columns
[19:55:12] [WARNING] Observed retryable model-task error: kind=rate_limit, http_status=429; the row task will be deferred.
[19:55:17] [INFO] 📊 Progress [5.2s]:
[19:55:17] [INFO] |-- 😊 column 'product_name': 9/10 (90%) 1.7 rec/s
[19:55:17] [INFO] |-- 🌘 column 'customer_review': 3/10 (30%) 0.6 rec/s
[19:55:24] [INFO] 📊 Progress [12.1s]:
[19:55:24] [INFO] |-- 😊 column 'product_name': 9/10 (90%) 0.7 rec/s
[19:55:24] [INFO] |-- 🌗 column 'customer_review': 7/10 (70%) 0.6 rec/s
[19:55:30] [INFO] 📊 Progress [18.7s]:
[19:55:30] [INFO] |-- 😊 column 'product_name': 9/10 (90%) 0.5 rec/s
[19:55:30] [INFO] |-- 🌖 column 'customer_review': 9/10 (90%) 0.5 rec/s
[19:55:30] [INFO] 🔄 (1/1) Salvaging 1 deferred task(s)
[19:55:31] [INFO] 📊 Progress [19.8s]:
[19:55:31] [INFO] |-- 🤩 column 'product_name': 10/10 (100%) 0.5 rec/s
[19:55:31] [INFO] |-- 🌕 column 'customer_review': 10/10 (100%) 0.5 rec/s
[19:55:31] [INFO] ✅ Async generation complete [19.8s]: 20 ok, 0 failed across 2 column(s)
[19:55:31] [INFO] 📊 Model usage summary:
[19:55:31] [INFO] |-- model: nvidia/nemotron-3.5-lightning-30b-a3b
[19:55:31] [INFO] |-- tokens: input=1769, output=2020, reasoning=0, total=3789, tps=189
[19:55:31] [INFO] |-- requests: success=20, failed=1, total=21, rpm=62
[19:55:32] [INFO] 📐 Measuring dataset column statistics:
[19:55:32] [INFO] |-- 🎲 column: 'product_category'
[19:55:32] [INFO] |-- 🎲 column: 'product_subcategory'
[19:55:32] [INFO] |-- 🎲 column: 'target_age_range'
[19:55:32] [INFO] |-- 🎲 column: 'customer'
[19:55:32] [INFO] |-- 🎲 column: 'number_of_stars'
[19:55:32] [INFO] |-- 🎲 column: 'review_style'
[19:55:32] [INFO] |-- 📝 column: 'product_name'
[19:55:32] [INFO] |-- 📝 column: 'customer_review'
Python
Output
| product_category | product_subcategory | target_age_range | customer | number_of_stars | review_style | product_name | customer_review | |
|---|---|---|---|---|---|---|---|---|
| 0 | Clothing | Activewear | 65+ | {'uuid': 'bc5098a2-a313-4280-a5a5-109777da9ea0... | 4 | structured with bullet points | Silver-Stretch Mobility Tee | * **Price Point:** The price is very reasona... |
| 1 | Books | Non-Fiction | 65+ | {'uuid': '5322b627-b5c7-4938-a520-368f89ec5954... | 3 | brief | Golden Age Reflections: A Non-Fiction Anthology | The anthology had its moments, but it felt a b... |
| 2 | Home & Kitchen | Appliances | 50-65 | {'uuid': '5bae685f-a98a-4d66-8efe-d21045ff7fbc... | 1 | detailed | Heritage Hearth Enthusiast | I recently invested in the Heritage Hearth Ent... |
| 3 | Home Office | Storage | 35-50 | {'uuid': '2c80c497-35d6-41ca-92c4-0518a9867d20... | 1 | brief | Elara Locker | Absolutely terrible. The Elara Locker arrived ... |
| 4 | Electronics | Cameras | 50-65 | {'uuid': '41f7dab5-57b3-48d4-935a-d20105b03b84... | 4 | detailed | VintageCapture Elite | I recently had the pleasure of adding the Vint... |
Python
Output
──────────────────────────────────────── 🎨 Data Designer Dataset Profile ───────────────────────────────────────── Dataset Overview ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ ┃ number of records ┃ number of columns ┃ percent complete records ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ │ 10 │ 8 │ 100.0% │ └─────────────────────────────────┴─────────────────────────────────┴─────────────────────────────────────────────┘ 🎲 Sampler Columns ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ ┃ column name ┃ data type ┃ number unique values ┃ sampler type ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ │ product_category │ string │ 5 (50.0%) │ category │ ├────────────────────────────────┼─────────────────┼─────────────────────────────────┼────────────────────────────┤ │ product_subcategory │ string │ 10 (100.0%) │ subcategory │ ├────────────────────────────────┼─────────────────┼─────────────────────────────────┼────────────────────────────┤ │ target_age_range │ string │ 5 (50.0%) │ category │ ├────────────────────────────────┼─────────────────┼─────────────────────────────────┼────────────────────────────┤ │ customer │ dict │ 10 (100.0%) │ person_from_faker │ ├────────────────────────────────┼─────────────────┼─────────────────────────────────┼────────────────────────────┤ │ number_of_stars │ int │ 4 (40.0%) │ uniform │ ├────────────────────────────────┼─────────────────┼─────────────────────────────────┼────────────────────────────┤ │ review_style │ string │ 3 (30.0%) │ category │ └────────────────────────────────┴─────────────────┴─────────────────────────────────┴────────────────────────────┘ 📝 LLM-Text Columns ┏━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓ ┃ ┃ ┃ ┃ prompt tokens ┃ completion tokens ┃ ┃ column name ┃ data type ┃ number unique values ┃ per record ┃ per record ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩ │ product_name │ string │ 10 (100.0%) │ 73.5 +/- 1.0 │ 4.0 +/- 2.7 │ ├───────────────────────┼───────────────┼────────────────────────────┼───────────────────┼────────────────────────┤ │ customer_review │ string │ 10 (100.0%) │ 69.0 +/- 2.5 │ 103.0 +/- 173.4 │ └───────────────────────┴───────────────┴────────────────────────────┴───────────────────┴────────────────────────┘ ╭────────────────────────────────────────────────── Table Notes ──────────────────────────────────────────────────╮ │ │ │ 1. All token statistics are based on a sample of max(1000, len(dataset)) records. │ │ 2. Tokens are calculated using tiktoken's cl100k_base tokenizer. │ │ │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
⏭️ Next Steps
Now that you've seen the basics of Data Designer, check out the following notebooks to learn more about: