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
[16:02:00] [INFO] ✅ Validation passed
Next, let's add samplers to generate data related to the customer and their review.
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Output
[16:02:00] [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
[16:02:00] [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
[16:02:00] [INFO] 📸 Preview generation in progress
[16:02:00] [INFO] |-- 🔒 Jinja rendering engine: secure
[16:02:00] [INFO] ✅ Validation passed
[16:02:00] [INFO] ⛓️ Sorting column configs into a Directed Acyclic Graph
[16:02:00] [INFO] Skipping model health checks because DATA_DESIGNER_SKIP_MODEL_HEALTH_CHECKS=1
[16:02:00] [INFO] ⚡ Using async task-queue preview
[16:02:00] [INFO] 📝 llm-text model config for column 'product_name'
[16:02:00] [INFO] |-- model: 'nvidia/nemotron-3.5-lightning-30b-a3b'
[16:02:00] [INFO] |-- model alias: 'nemotron-lightning'
[16:02:00] [INFO] |-- model provider: 'nvidia'
[16:02:00] [INFO] |-- inference parameters:
[16:02:00] [INFO] | |-- generation_type=chat-completion
[16:02:00] [INFO] | |-- max_parallel_requests=4
[16:02:00] [INFO] | |-- extra_body={'chat_template_kwargs': {'enable_thinking': False}}[16:02:00] [INFO] | |-- temperature=1.00
[16:02:00] [INFO] | |-- top_p=0.95
[16:02:00] [INFO] | |-- max_tokens=2048
[16:02:00] [INFO] 📝 llm-text model config for column 'customer_review'
[16:02:00] [INFO] |-- model: 'nvidia/nemotron-3.5-lightning-30b-a3b'
[16:02:00] [INFO] |-- model alias: 'nemotron-lightning'
[16:02:00] [INFO] |-- model provider: 'nvidia'
[16:02:00] [INFO] |-- inference parameters:
[16:02:00] [INFO] | |-- generation_type=chat-completion
[16:02:00] [INFO] | |-- max_parallel_requests=4
[16:02:00] [INFO] | |-- extra_body={'chat_template_kwargs': {'enable_thinking': False}}[16:02:00] [INFO] | |-- temperature=1.00
[16:02:00] [INFO] | |-- top_p=0.95
[16:02:00] [INFO] | |-- max_tokens=2048
[16:02:00] [INFO] ⚡️ Async generation: 2 column(s) (column 'product_name', column 'customer_review'), 4 tasks across 1 row group(s)
[16:02:00] [INFO] 🚀 (1/1) Dispatching with 2 records
[16:02:00] [INFO] 🎲 (1/1) Preparing samplers to generate 2 records across 6 columns
[16:02:08] [INFO] 📊 Progress [8.1s]:
[16:02:08] [INFO] |-- 🤩 column 'product_name': 2/2 (100%) 0.2 rec/s
[16:02:08] [INFO] |-- 🌗 column 'customer_review': 1/2 (50%) 0.1 rec/s
[16:02:10] [INFO] 📊 Progress [9.9s]:
[16:02:10] [INFO] |-- 🤩 column 'product_name': 2/2 (100%) 0.2 rec/s
[16:02:10] [INFO] |-- 🌕 column 'customer_review': 2/2 (100%) 0.2 rec/s
[16:02:10] [INFO] ✅ Async generation complete [9.9s]: 4 ok, 0 failed across 2 column(s)
[16:02:10] [INFO] 📊 Model usage summary:
[16:02:10] [INFO] |-- model: nvidia/nemotron-3.5-lightning-30b-a3b
[16:02:10] [INFO] |-- tokens: input=354, output=150, reasoning=0, total=504, tps=50
[16:02:10] [INFO] |-- requests: success=4, failed=0, total=4, rpm=24
[16:02:10] [INFO] 📐 Measuring dataset column statistics:
[16:02:10] [INFO] |-- 🎲 column: 'product_category'
[16:02:10] [INFO] |-- 🎲 column: 'product_subcategory'
[16:02:10] [INFO] |-- 🎲 column: 'target_age_range'
[16:02:10] [INFO] |-- 🎲 column: 'customer'
[16:02:10] [INFO] |-- 🎲 column: 'number_of_stars'
[16:02:10] [INFO] |-- 🎲 column: 'review_style'
[16:02:10] [INFO] |-- 📝 column: 'product_name'
[16:02:10] [INFO] |-- 📝 column: 'customer_review'
[16:02:10] [INFO] ✅ Preview complete!
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Output
[index: 0]
Generated Columns ┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ ┃ Name ┃ Value ┃ ┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ │ product_category │ Home Office │ ├─────────────────────┼──────────────────────────────────────────────────────────────────────────────────────┤ │ product_subcategory │ Lighting │ ├─────────────────────┼──────────────────────────────────────────────────────────────────────────────────────┤ │ target_age_range │ 18-25 │ ├─────────────────────┼──────────────────────────────────────────────────────────────────────────────────────┤ │ customer │ { │ │ │ 'uuid': '93b10199-2c03-4277-bc64-8f5192dcdf04', │ │ │ 'locale': 'en_US', │ │ │ 'first_name': 'Teresa', │ │ │ 'last_name': 'Smith', │ │ │ 'middle_name': None, │ │ │ 'sex': 'Female', │ │ │ 'street_number': '57930', │ │ │ 'street_name': 'Sullivan Expressway', │ │ │ 'city': 'New Maria', │ │ │ 'state': 'Oklahoma', │ │ │ 'postcode': '31532', │ │ │ 'age': 18, │ │ │ 'birth_date': '2007-11-20', │ │ │ 'country': 'Bahamas', │ │ │ 'marital_status': 'never_married', │ │ │ 'education_level': 'bachelors', │ │ │ 'unit': '', │ │ │ 'occupation': 'Restaurant manager', │ │ │ 'phone_number': '+1-726-903-2636', │ │ │ 'bachelors_field': 'education' │ │ │ } │ ├─────────────────────┼──────────────────────────────────────────────────────────────────────────────────────┤ │ number_of_stars │ 4 │ ├─────────────────────┼──────────────────────────────────────────────────────────────────────────────────────┤ │ review_style │ structured with bullet points │ ├─────────────────────┼──────────────────────────────────────────────────────────────────────────────────────┤ │ product_name │ PulseShift Work Light │ ├─────────────────────┼──────────────────────────────────────────────────────────────────────────────────────┤ │ customer_review │ • Bought the PulseShift Work Light last week and it has completely transformed my │ │ │ late-night study sessions. │ │ │ • The adjustable brightness settings are perfect; I can dim it just enough to not │ │ │ strain my eyes, but bright enough to see my notes clearly. │ │ │ • Battery life is impressive—I’ve used it for hours on a single charge and it’s │ │ │ still going strong. │ │ │ • The clip-on design is sturdy and holds onto my desk lamp without slipping, which │ │ │ is exactly what I needed. │ │ │ • Would definitely recommend it to fellow students; great value for the price and │ │ │ performance. │ └─────────────────────┴──────────────────────────────────────────────────────────────────────────────────────┘
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Output
| product_category | product_subcategory | target_age_range | customer | number_of_stars | review_style | product_name | customer_review | |
|---|---|---|---|---|---|---|---|---|
| 0 | Home Office | Lighting | 18-25 | {'uuid': '93b10199-2c03-4277-bc64-8f5192dcdf04... | 4 | structured with bullet points | PulseShift Work Light | • Bought the PulseShift Work Light last week a... |
| 1 | Home Office | Office Supplies | 50-65 | {'uuid': '5a3b78b0-adb8-4eac-8639-a230763c75a7... | 1 | brief | Blueprints Office Companion | Bought this hoping for useful office help, but... |
📊 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 │ 1 (50.0%) │ 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 │ 2 (100.0%) │ 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%) │ 74.0 +/- 0.0 │ 4.5 +/- 0.7 │ ├───────────────────────┼───────────────┼────────────────────────────┼───────────────────┼────────────────────────┤ │ customer_review │ string │ 2 (100.0%) │ 69.5 +/- 0.5 │ 67.0 +/- 62.2 │ └───────────────────────┴───────────────┴────────────────────────────┴───────────────────┴────────────────────────┘ ╭────────────────────────────────────────────────── 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
[16:02:10] [INFO] OpenTelemetry metrics available at http://127.0.0.1:9464/metrics
[16:02:10] [INFO] 🎨 Creating Data Designer dataset
[16:02:10] [INFO] |-- 🔒 Jinja rendering engine: secure
[16:02:10] [INFO] ✅ Validation passed
[16:02:10] [INFO] ⛓️ Sorting column configs into a Directed Acyclic Graph
[16:02:10] [INFO] Skipping model health checks because DATA_DESIGNER_SKIP_MODEL_HEALTH_CHECKS=1
[16:02:10] [INFO] ⚡ Using async task-queue builder
[16:02:10] [INFO] 📝 llm-text model config for column 'product_name'
[16:02:10] [INFO] |-- model: 'nvidia/nemotron-3.5-lightning-30b-a3b'
[16:02:10] [INFO] |-- model alias: 'nemotron-lightning'
[16:02:10] [INFO] |-- model provider: 'nvidia'
[16:02:10] [INFO] |-- inference parameters:
[16:02:10] [INFO] | |-- generation_type=chat-completion
[16:02:10] [INFO] | |-- max_parallel_requests=4
[16:02:10] [INFO] | |-- extra_body={'chat_template_kwargs': {'enable_thinking': False}}[16:02:10] [INFO] | |-- temperature=1.00
[16:02:10] [INFO] | |-- top_p=0.95
[16:02:10] [INFO] | |-- max_tokens=2048
[16:02:10] [INFO] 📝 llm-text model config for column 'customer_review'
[16:02:10] [INFO] |-- model: 'nvidia/nemotron-3.5-lightning-30b-a3b'
[16:02:10] [INFO] |-- model alias: 'nemotron-lightning'
[16:02:10] [INFO] |-- model provider: 'nvidia'
[16:02:10] [INFO] |-- inference parameters:
[16:02:10] [INFO] | |-- generation_type=chat-completion
[16:02:10] [INFO] | |-- max_parallel_requests=4
[16:02:10] [INFO] | |-- extra_body={'chat_template_kwargs': {'enable_thinking': False}}[16:02:10] [INFO] | |-- temperature=1.00
[16:02:10] [INFO] | |-- top_p=0.95
[16:02:10] [INFO] | |-- max_tokens=2048
[16:02:10] [INFO] ⚡️ Async generation: 2 column(s) (column 'product_name', column 'customer_review'), 20 tasks across 1 row group(s)
[16:02:10] [INFO] 🚀 (1/1) Dispatching with 10 records
[16:02:10] [INFO] 🎲 (1/1) Preparing samplers to generate 10 records across 6 columns
[16:02:11] [WARNING] Observed retryable model-task error: kind=rate_limit, http_status=429; the row task will be deferred.
[16:02:16] [INFO] 📊 Progress [6.1s]:
[16:02:16] [INFO] |-- 🌤️ column 'product_name': 9/10 (90%) 1.5 rec/s
[16:02:16] [INFO] |-- 🌦️ column 'customer_review': 4/10 (40%) 0.7 rec/s
[16:02:25] [INFO] 📊 Progress [14.2s]:
[16:02:25] [INFO] |-- 🌤️ column 'product_name': 9/10 (90%) 0.6 rec/s
[16:02:25] [INFO] |-- ⛅ column 'customer_review': 5/10 (50%) 0.4 rec/s
[16:02:30] [INFO] 📊 Progress [19.5s]:
[16:02:30] [INFO] |-- 🌤️ column 'product_name': 9/10 (90%) 0.5 rec/s
[16:02:30] [INFO] |-- ⛅ column 'customer_review': 7/10 (70%) 0.4 rec/s
[16:02:30] [INFO] 🔄 (1/1) Salvaging 3 deferred task(s)
[16:02:40] [INFO] 📊 Progress [29.6s]:
[16:02:40] [INFO] |-- 🌤️ column 'product_name': 9/10 (90%) 0.3 rec/s
[16:02:40] [INFO] |-- 🌤️ column 'customer_review': 8/10 (80%) 0.3 rec/s
[16:02:40] [INFO] 🔄 (1/1) Preserving 2 retryable task(s); records will keep retrying.
[16:02:48] [INFO] 📊 Progress [37.4s]:
[16:02:48] [INFO] |-- 🌤️ column 'product_name': 9/10 (90%) 0.2 rec/s
[16:02:48] [INFO] |-- 🌤️ column 'customer_review': 9/10 (90%) 0.2 rec/s
[16:02:48] [INFO] 🔄 (1/1) Preserving 1 retryable task(s); records will keep retrying.
[16:02:50] [WARNING] Provider showing degraded performance: 50% of last 20 task outcomes were retryable errors (rate_limit=10, success=10); cumulative outcomes: rate_limit=11, success=18; deferred_tasks=1. Run may take longer than expected; salvage will retry these.
[16:03:03] [INFO] 📊 Progress [52.2s]:
[16:03:03] [INFO] |-- ☀️ column 'product_name': 10/10 (100%) 0.2 rec/s
[16:03:03] [INFO] |-- 🌤️ column 'customer_review': 9/10 (90%) 0.2 rec/s
[16:03:07] [INFO] 📊 Progress [56.4s]:
[16:03:07] [INFO] |-- ☀️ column 'product_name': 10/10 (100%) 0.2 rec/s
[16:03:07] [INFO] |-- ☀️ column 'customer_review': 10/10 (100%) 0.2 rec/s
[16:03:07] [INFO] ✅ Async generation complete [56.4s]: 20 ok, 0 failed across 2 column(s)
[16:03:07] [INFO] 📊 Model usage summary:
[16:03:07] [INFO] |-- model: nvidia/nemotron-3.5-lightning-30b-a3b
[16:03:07] [INFO] |-- tokens: input=1773, output=2461, reasoning=0, total=4234, tps=74
[16:03:07] [INFO] |-- requests: success=20, failed=16, total=36, rpm=38
[16:03:07] [INFO] 📐 Measuring dataset column statistics:
[16:03:07] [INFO] |-- 🎲 column: 'product_category'
[16:03:07] [INFO] |-- 🎲 column: 'product_subcategory'
[16:03:07] [INFO] |-- 🎲 column: 'target_age_range'
[16:03:07] [INFO] |-- 🎲 column: 'customer'
[16:03:07] [INFO] |-- 🎲 column: 'number_of_stars'
[16:03:07] [INFO] |-- 🎲 column: 'review_style'
[16:03:07] [INFO] |-- 📝 column: 'product_name'
[16:03:07] [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 | Winter Coats | 35-50 | {'uuid': '28f4ef0c-0b65-41b4-913b-e7045093b80a... | 1 | rambling | Obsidian Overcoat | So I went and I bought this coat, the Obsidian... |
| 1 | Clothing | Men's Clothing | 25-35 | {'uuid': '62b901c4-db9e-4ca3-9d20-002a19be7a49... | 4 | rambling | Urban Nomad Jacket | Okay so I decided to pull the trigger on this ... |
| 2 | Clothing | Winter Coats | 35-50 | {'uuid': '27e5d786-af0f-429f-8295-9c11a9e426f0... | 2 | brief | The Aurora Shift Coat | The material feels cheap and flimsy. The fit i... |
| 3 | Home & Kitchen | Decor | 18-25 | {'uuid': 'a1bc206f-5024-4e85-8da0-48477925bddd... | 2 | detailed | MoodLights | I have to be honest, installing these MoodLigh... |
| 4 | Home & Kitchen | Organization | 50-65 | {'uuid': 'c12b104b-2db6-4d50-bf93-dec95a6575f8... | 4 | brief | Timeless Tidy Organizer | Purchased the Timeless Tidy Organizer recently... |
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 │ 4 (40.0%) │ category │ ├────────────────────────────────┼─────────────────┼─────────────────────────────────┼────────────────────────────┤ │ product_subcategory │ string │ 8 (80.0%) │ subcategory │ ├────────────────────────────────┼─────────────────┼─────────────────────────────────┼────────────────────────────┤ │ target_age_range │ string │ 4 (40.0%) │ category │ ├────────────────────────────────┼─────────────────┼─────────────────────────────────┼────────────────────────────┤ │ customer │ dict │ 10 (100.0%) │ person_from_faker │ ├────────────────────────────────┼─────────────────┼─────────────────────────────────┼────────────────────────────┤ │ number_of_stars │ int │ 4 (40.0%) │ uniform │ ├────────────────────────────────┼─────────────────┼─────────────────────────────────┼────────────────────────────┤ │ review_style │ string │ 4 (40.0%) │ 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%) │ 74.5 +/- 0.8 │ 4.0 +/- 0.7 │ ├───────────────────────┼───────────────┼────────────────────────────┼───────────────────┼────────────────────────┤ │ customer_review │ string │ 10 (100.0%) │ 68.5 +/- 1.7 │ 218.5 +/- 146.3 │ └───────────────────────┴───────────────┴────────────────────────────┴───────────────────┴────────────────────────┘ ╭────────────────────────────────────────────────── 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: