๐จ Data Designer Tutorial: Seeding Synthetic Data Generation with an External Datasetยถ
๐ What you'll learnยถ
In this notebook, we will demonstrate how to seed synthetic data generation in Data Designer with an external dataset.
If this is your first time using Data Designer, we recommend starting with the first notebook in this tutorial series.
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)
๐ฅ Prepare a seed datasetยถ
For this notebook, we'll create a synthetic dataset of patient notes.
We will seed the generation process with a symptom-to-diagnosis dataset.
We already have the dataset downloaded in the data directory of this repository.
๐ฑ Why use a seed dataset?
Seed datasets let you steer the generation process by providing context that is specific to your use case.
Seed datasets are also an excellent way to inject real-world diversity into your synthetic data.
During generation, prompt templates can reference any of the seed dataset fields.
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# Download sample dataset from Github
import urllib.request
url = "https://raw.githubusercontent.com/NVIDIA/GenerativeAIExamples/refs/heads/main/nemo/NeMo-Data-Designer/data/gretelai_symptom_to_diagnosis.csv"
local_filename, _ = urllib.request.urlretrieve(url, "gretelai_symptom_to_diagnosis.csv")
# Seed datasets are passed as reference objects to the config builder.
seed_source = dd.LocalFileSeedSource(path=local_filename)
config_builder.with_seed_dataset(seed_source)
# Download sample dataset from Github
import urllib.request
url = "https://raw.githubusercontent.com/NVIDIA/GenerativeAIExamples/refs/heads/main/nemo/NeMo-Data-Designer/data/gretelai_symptom_to_diagnosis.csv"
local_filename, _ = urllib.request.urlretrieve(url, "gretelai_symptom_to_diagnosis.csv")
# Seed datasets are passed as reference objects to the config builder.
seed_source = dd.LocalFileSeedSource(path=local_filename)
config_builder.with_seed_dataset(seed_source)
Out[5]:
DataDesignerConfigBuilder( seed_dataset: local seed )
๐จ Designing our synthetic patient notes datasetยถ
- The prompt template can reference fields from our seed dataset:
{{ diagnosis }}- the medical diagnosis from the seed data{{ patient_summary }}- the symptom description from the seed data
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config_builder.add_column(
dd.SamplerColumnConfig(
name="patient_sampler",
sampler_type=dd.SamplerType.PERSON_FROM_FAKER,
params=dd.PersonFromFakerSamplerParams(),
)
)
config_builder.add_column(
dd.SamplerColumnConfig(
name="doctor_sampler",
sampler_type=dd.SamplerType.PERSON_FROM_FAKER,
params=dd.PersonFromFakerSamplerParams(),
)
)
config_builder.add_column(
dd.SamplerColumnConfig(
name="patient_id",
sampler_type=dd.SamplerType.UUID,
params=dd.UUIDSamplerParams(
prefix="PT-",
short_form=True,
uppercase=True,
),
)
)
config_builder.add_column(dd.ExpressionColumnConfig(name="first_name", expr="{{ patient_sampler.first_name }}"))
config_builder.add_column(dd.ExpressionColumnConfig(name="last_name", expr="{{ patient_sampler.last_name }}"))
config_builder.add_column(dd.ExpressionColumnConfig(name="dob", expr="{{ patient_sampler.birth_date }}"))
config_builder.add_column(
dd.SamplerColumnConfig(
name="symptom_onset_date",
sampler_type=dd.SamplerType.DATETIME,
params=dd.DatetimeSamplerParams(start="2024-01-01", end="2024-12-31"),
)
)
config_builder.add_column(
dd.SamplerColumnConfig(
name="date_of_visit",
sampler_type=dd.SamplerType.TIMEDELTA,
params=dd.TimeDeltaSamplerParams(dt_min=1, dt_max=30, reference_column_name="symptom_onset_date"),
)
)
config_builder.add_column(dd.ExpressionColumnConfig(name="physician", expr="Dr. {{ doctor_sampler.last_name }}"))
config_builder.add_column(
dd.LLMTextColumnConfig(
name="physician_notes",
prompt="""\
You are a primary-care physician who just had an appointment with {{ first_name }} {{ last_name }},
who has been struggling with symptoms from {{ diagnosis }} since {{ symptom_onset_date }}.
The date of today's visit is {{ date_of_visit }}.
{{ patient_summary }}
Write careful notes about your visit with {{ first_name }},
as Dr. {{ doctor_sampler.first_name }} {{ doctor_sampler.last_name }}.
Format the notes as a busy doctor might.
Respond with only the notes, no other text.
""",
model_alias=MODEL_ALIAS,
)
)
data_designer.validate(config_builder)
config_builder.add_column(
dd.SamplerColumnConfig(
name="patient_sampler",
sampler_type=dd.SamplerType.PERSON_FROM_FAKER,
params=dd.PersonFromFakerSamplerParams(),
)
)
config_builder.add_column(
dd.SamplerColumnConfig(
name="doctor_sampler",
sampler_type=dd.SamplerType.PERSON_FROM_FAKER,
params=dd.PersonFromFakerSamplerParams(),
)
)
config_builder.add_column(
dd.SamplerColumnConfig(
name="patient_id",
sampler_type=dd.SamplerType.UUID,
params=dd.UUIDSamplerParams(
prefix="PT-",
short_form=True,
uppercase=True,
),
)
)
config_builder.add_column(dd.ExpressionColumnConfig(name="first_name", expr="{{ patient_sampler.first_name }}"))
config_builder.add_column(dd.ExpressionColumnConfig(name="last_name", expr="{{ patient_sampler.last_name }}"))
config_builder.add_column(dd.ExpressionColumnConfig(name="dob", expr="{{ patient_sampler.birth_date }}"))
config_builder.add_column(
dd.SamplerColumnConfig(
name="symptom_onset_date",
sampler_type=dd.SamplerType.DATETIME,
params=dd.DatetimeSamplerParams(start="2024-01-01", end="2024-12-31"),
)
)
config_builder.add_column(
dd.SamplerColumnConfig(
name="date_of_visit",
sampler_type=dd.SamplerType.TIMEDELTA,
params=dd.TimeDeltaSamplerParams(dt_min=1, dt_max=30, reference_column_name="symptom_onset_date"),
)
)
config_builder.add_column(dd.ExpressionColumnConfig(name="physician", expr="Dr. {{ doctor_sampler.last_name }}"))
config_builder.add_column(
dd.LLMTextColumnConfig(
name="physician_notes",
prompt="""\
You are a primary-care physician who just had an appointment with {{ first_name }} {{ last_name }},
who has been struggling with symptoms from {{ diagnosis }} since {{ symptom_onset_date }}.
The date of today's visit is {{ date_of_visit }}.
{{ patient_summary }}
Write careful notes about your visit with {{ first_name }},
as Dr. {{ doctor_sampler.first_name }} {{ doctor_sampler.last_name }}.
Format the notes as a busy doctor might.
Respond with only the notes, no other text.
""",
model_alias=MODEL_ALIAS,
)
)
data_designer.validate(config_builder)
[03:29:43] [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.
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preview = data_designer.preview(config_builder, num_records=2)
preview = data_designer.preview(config_builder, num_records=2)
[03:29:43] [INFO] ๐บ Preview generation in progress
[03:29:43] [INFO] |-- ๐ Jinja rendering engine: secure
[03:29:43] [INFO] โ Validation passed
[03:29:43] [INFO] โ๏ธ Sorting column configs into a Directed Acyclic Graph
[03:29:43] [INFO] ๐ฉบ Running health checks for models...
[03:29:43] [INFO] |-- ๐ Checking 'nvidia/nemotron-3-nano-30b-a3b' in provider named 'nvidia' for model alias 'nemotron-nano-v3'...
[03:29:43] [INFO] |-- โ Passed!
[03:29:43] [INFO] ๐ฑ Sampling 2 records from seed dataset
[03:29:43] [INFO] |-- seed dataset size: 820 records
[03:29:43] [INFO] |-- sampling strategy: ordered
[03:29:43] [INFO] ๐ฒ Preparing samplers to generate 2 records across 5 columns
[03:29:43] [INFO] (๐พ + ๐พ) Concatenating 2 datasets
[03:29:43] [INFO] ๐งฉ Generating column `first_name` from expression
[03:29:43] [INFO] ๐งฉ Generating column `last_name` from expression
[03:29:43] [INFO] ๐งฉ Generating column `dob` from expression
[03:29:43] [INFO] ๐งฉ Generating column `physician` from expression
[03:29:43] [INFO] ๐ llm-text model config for column 'physician_notes'
[03:29:43] [INFO] |-- model: 'nvidia/nemotron-3-nano-30b-a3b'
[03:29:43] [INFO] |-- model alias: 'nemotron-nano-v3'
[03:29:43] [INFO] |-- model provider: 'nvidia'
[03:29:43] [INFO] |-- inference parameters:
[03:29:43] [INFO] | |-- generation_type=chat-completion
[03:29:43] [INFO] | |-- max_parallel_requests=4
[03:29:43] [INFO] | |-- extra_body={'chat_template_kwargs': {'enable_thinking': False}}
[03:29:43] [INFO] | |-- temperature=1.00
[03:29:43] [INFO] | |-- top_p=1.00
[03:29:43] [INFO] | |-- max_tokens=2048
[03:29:43] [INFO] โก๏ธ Processing llm-text column 'physician_notes' with 4 concurrent workers
[03:29:43] [INFO] โฑ๏ธ llm-text column 'physician_notes' will report progress after each record
[03:29:51] [INFO] |-- ๐ llm-text column 'physician_notes' progress: 1/2 (50%) complete, 1 ok, 0 failed, 0.13 rec/s, eta 7.7s
[03:30:03] [INFO] |-- ๐คฉ llm-text column 'physician_notes' progress: 2/2 (100%) complete, 2 ok, 0 failed, 0.10 rec/s, eta 0.0s
[03:30:04] [INFO] ๐ Model usage summary:
[03:30:04] [INFO] |-- model: nvidia/nemotron-3-nano-30b-a3b
[03:30:04] [INFO] |-- tokens: input=327, output=2380, total=2707, tps=133
[03:30:04] [INFO] |-- requests: success=2, failed=0, total=2, rpm=5
[03:30:04] [INFO] ๐ Measuring dataset column statistics:
[03:30:04] [INFO] |-- ๐ฒ column: 'patient_sampler'
[03:30:04] [INFO] |-- ๐ฒ column: 'doctor_sampler'
[03:30:04] [INFO] |-- ๐ฒ column: 'patient_id'
[03:30:04] [INFO] |-- ๐งฉ column: 'first_name'
[03:30:04] [INFO] |-- ๐งฉ column: 'last_name'
[03:30:04] [INFO] |-- ๐งฉ column: 'dob'
[03:30:04] [INFO] |-- ๐ฒ column: 'symptom_onset_date'
[03:30:04] [INFO] |-- ๐ฒ column: 'date_of_visit'
[03:30:04] [INFO] |-- ๐งฉ column: 'physician'
[03:30:04] [INFO] |-- ๐ column: 'physician_notes'
[03:30:04] [INFO] ๐ Preview complete!
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# 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]
Seed Columns โโโโโโโโโโโโโโโโโโโณโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ โ Name โ Value โ โกโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฉ โ diagnosis โ cervical spondylosis โ โโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ patient_summary โ I've been having a lot of pain in my neck and back. I've also been having trouble with โ โ โ my balance and coordination. I've been coughing a lot and my limbs feel weak. โ โโโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ Generated Columns โโโโโโโโโโโโโโโโโโโโโโณโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ โ Name โ Value โ โกโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฉ โ patient_sampler โ { โ โ โ 'uuid': '179179a7-29de-46e4-8224-90d77ad5160a', โ โ โ 'locale': 'en_US', โ โ โ 'first_name': 'Jennifer', โ โ โ 'last_name': 'Wilson', โ โ โ 'middle_name': None, โ โ โ 'sex': 'Female', โ โ โ 'street_number': '95350', โ โ โ 'street_name': 'Parsons Island', โ โ โ 'city': 'Obrienfort', โ โ โ 'state': 'Arkansas', โ โ โ 'postcode': '18322', โ โ โ 'age': 104, โ โ โ 'birth_date': '1922-02-11', โ โ โ 'country': 'Latvia', โ โ โ 'marital_status': 'divorced', โ โ โ 'education_level': 'some_college', โ โ โ 'unit': '', โ โ โ 'occupation': 'Accountant, chartered certified', โ โ โ 'phone_number': '248.901.5130x24571', โ โ โ 'bachelors_field': 'no_degree' โ โ โ } โ โโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ doctor_sampler โ { โ โ โ 'uuid': '816894aa-62bd-4142-b7ea-ae4af34879cd', โ โ โ 'locale': 'en_US', โ โ โ 'first_name': 'Amanda', โ โ โ 'last_name': 'Kelly', โ โ โ 'middle_name': None, โ โ โ 'sex': 'Female', โ โ โ 'street_number': '8490', โ โ โ 'street_name': 'Monroe Cliffs', โ โ โ 'city': 'New Gail', โ โ โ 'state': 'Colorado', โ โ โ 'postcode': '63184', โ โ โ 'age': 73, โ โ โ 'birth_date': '1952-08-27', โ โ โ 'country': 'Libyan Arab Jamahiriya', โ โ โ 'marital_status': 'married_present', โ โ โ 'education_level': 'some_college', โ โ โ 'unit': '', โ โ โ 'occupation': 'Higher education careers adviser', โ โ โ 'phone_number': '(245)607-4590', โ โ โ 'bachelors_field': 'no_degree' โ โ โ } โ โโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ patient_id โ PT-0DA26720 โ โโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ symptom_onset_date โ 2024-03-28T00:00:00 โ โโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ date_of_visit โ 2024-04-02T00:00:00 โ โโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ physician_notes โ **Patient:** Jennifer Wilson โ โ โ **DOB:** [Not provided] โ โ โ **MRN:** [Not provided] โ โ โ **Date:** 2024-04-02 โ โ โ **Provider:** Dr. A. Kelly, MD โ โ โ โ โ โ **Chief Complaint:** "Neck and back pain, trouble with balance, coughing, weak limbs" โ โ โ โ โ โ **HPI:** โ โ โ - 36-year-old female with worsening cervical spondylosis since 03/28/2024. โ โ โ - Reports increasing neck/back pain, new gait instability, frequent falls, "unsteady โ โ โ on feet." โ โ โ - Concurrent cough (new onset, non-productive), worsened by posture. โ โ โ - Describes "weakness" in bilateral upper/lower extremities (difficulty lifting arms, โ โ โ climbing stairs). โ โ โ - Denies trauma, new neurologic deficits (e.g., numbness, vision changes), or โ โ โ bowel/bladder issues. โ โ โ - Notes cough associated with neck movement; denies fever, SOB, weight loss. โ โ โ - Concerned about "neurological decline" based on online research. โ โ โ โ โ โ **ROS:** โ โ โ - Neuro: Gait instability, weakness (no focal deficits), no seizures. โ โ โ - Musculoskeletal: Chronic neck/back pain (worsened by movement). โ โ โ - Respiratory: Cough (new), no hemoptysis. โ โ โ - Constitutional: Fatigue (reported), no fever. โ โ โ โ โ โ **Physical Exam:** โ โ โ - **Vitals:** BP 128/82, HR 76, RR 16, SpOโ 98% RA. โ โ โ - **Neck:** Limited ROM (flexion/extension painful), no masses. โ โ โ - **Neuro:** โ โ โ - Motor: 4/5 strength in UE/LUE, 4/5 in LE. โ โ โ - Reflexes: 2+ throughout. โ โ โ - Coordination: Positive Romberg (unable to stand unassisted). โ โ โ - Sensation: Intact to light touch/pinprick. โ โ โ - **Spine:** Mild tenderness paraspinal, no deformity. โ โ โ - **Cardiopulmonary:** Clear lungs, regular rate/rhythm. โ โ โ โ โ โ **Assessment:** โ โ โ 1. **Worsening cervical spondylosis with radiculopathy + gait instability** (newly โ โ โ progressive since 03/28). โ โ โ 2. **Cough likely unrelated** (viral? postural exacerbation), but noted as symptom. โ โ โ 3. **Functional decline** (balance impairment, weakness affecting ADLs). โ โ โ โ โ โ **Plan:** โ โ โ - **Imaging:** Urgent MRI C-spine (ordered today; results pending). โ โ โ - **Medication:** โ โ โ - Start low-dose **gabapentin 300mg TID** (for neuropathic pain). โ โ โ - Continue **gabapentin 600mg BID** (current regimen; monitor for sedation). โ โ โ - *Avoid NSAIDs* (GI risk; consider acetaminophen for pain). โ โ โ - **Referral:** Neurology consult (urgent, pending MRI). โ โ โ - **Safety:** โ โ โ - **Fall precautions** (home safety eval, assistive device referral). โ โ โ - **Physical therapy** (gait training, balance exercises; referral to PT). โ โ โ - **Follow-up:** โ โ โ - Neurology: Within 1 week (MRI results). โ โ โ - PCP: Re-evaluate in 4 weeks *after* MRI/neurology input. โ โ โ - **Patient Education:** โ โ โ - Emphasized *no* need for ER unless new bowel/bladder incontinence/paralysis. โ โ โ - Discussed risks of NSAIDs (GI/renal) with current meds (warfarin/SSRI). โ โ โ - Encouraged adherence to PT/fall precautions. โ โ โ โ โ โ **Provider Note:** โ โ โ *High suspicion for progressive cervical spondylosis with spinal cord involvement โ โ โ given gait instability + weakness. MRI critical to rule out myelopathy. Cough likely โ โ โ benign but monitor; will reassess if respiratory symptoms persist.* โ โ โ **Disposition:** Discharged with fall precautions, PT referral, and urgent imaging. โ โ โ โ โ โ --- โ โ โ *End of note* โ โโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ first_name โ Jennifer โ โโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ last_name โ Wilson โ โโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ dob โ 1922-02-11 โ โโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ physician โ Dr. Kelly โ โโโโโโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
Inย [9]:
Copied!
# The preview dataset is available as a pandas DataFrame.
preview.dataset
# The preview dataset is available as a pandas DataFrame.
preview.dataset
Out[9]:
| diagnosis | patient_summary | patient_sampler | doctor_sampler | patient_id | symptom_onset_date | date_of_visit | first_name | last_name | dob | physician | physician_notes | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | cervical spondylosis | I've been having a lot of pain in my neck and ... | {'uuid': '179179a7-29de-46e4-8224-90d77ad5160a... | {'uuid': '816894aa-62bd-4142-b7ea-ae4af34879cd... | PT-0DA26720 | 2024-03-28T00:00:00 | 2024-04-02T00:00:00 | Jennifer | Wilson | 1922-02-11 | Dr. Kelly | **Patient:** Jennifer Wilson \n**DOB:** [Not ... |
| 1 | impetigo | I have a rash on my face that is getting worse... | {'uuid': '8b6cea55-a175-46bb-8114-04b03615852b... | {'uuid': '719cee9f-17fe-45e2-95c7-cdc1eebcd0a9... | PT-E1F7F03C | 2024-12-14T00:00:00 | 2024-12-21T00:00:00 | Angela | Young | 2007-03-12 | Dr. Greene | **SOAP Note** \n**Patient:** Angela Young \n... |
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 [10]:
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 10 โ 100.0% โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ ๐ฒ Sampler Columns โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโณโโโโโโโโโโโโโโโโโโณโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโณโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ โ column name โ data type โ number unique values โ sampler type โ โกโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฉ โ patient_sampler โ dict โ 2 (100.0%) โ person_from_faker โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ doctor_sampler โ dict โ 2 (100.0%) โ person_from_faker โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ patient_id โ string โ 2 (100.0%) โ uuid โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ symptom_onset_date โ string โ 2 (100.0%) โ datetime โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ date_of_visit โ string โ 2 (100.0%) โ timedelta โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ 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 physician_notes โ string โ 2 (100.0%) โ 134.0 +/- 4.0 โ 751.5 +/- 145.0 โ โโโโโโโโโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโโโโโโโโ ๐งฉ Expression Columns โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโณโโโโโโโโโโโโโโโโโโโโโโโโโโโโณโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ โ column name โ data type โ number unique values โ โกโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฉ โ first_name โ string โ 2 (100.0%) โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ last_name โ string โ 2 (100.0%) โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ dob โ string โ 2 (100.0%) โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ physician โ string โ 2 (100.0%) โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ 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 [11]:
Copied!
results = data_designer.create(config_builder, num_records=10, dataset_name="tutorial-3")
results = data_designer.create(config_builder, num_records=10, dataset_name="tutorial-3")
[03:30:04] [INFO] ๐จ Creating Data Designer dataset
[03:30:04] [INFO] |-- ๐ Jinja rendering engine: secure
[03:30:04] [INFO] โ Validation passed
[03:30:04] [INFO] โ๏ธ Sorting column configs into a Directed Acyclic Graph
[03:30:04] [INFO] ๐ฉบ Running health checks for models...
[03:30:04] [INFO] |-- ๐ Checking 'nvidia/nemotron-3-nano-30b-a3b' in provider named 'nvidia' for model alias 'nemotron-nano-v3'...
[03:30:05] [INFO] |-- โ Passed!
[03:30:05] [INFO] โณ Processing batch 1 of 1
[03:30:05] [INFO] ๐ฑ Sampling 10 records from seed dataset
[03:30:05] [INFO] |-- seed dataset size: 820 records
[03:30:05] [INFO] |-- sampling strategy: ordered
[03:30:05] [INFO] ๐ฒ Preparing samplers to generate 10 records across 5 columns
[03:30:05] [INFO] (๐พ + ๐พ) Concatenating 2 datasets
[03:30:05] [INFO] ๐งฉ Generating column `first_name` from expression
[03:30:05] [INFO] ๐งฉ Generating column `last_name` from expression
[03:30:05] [INFO] ๐งฉ Generating column `dob` from expression
[03:30:05] [INFO] ๐งฉ Generating column `physician` from expression
[03:30:05] [INFO] ๐ llm-text model config for column 'physician_notes'
[03:30:05] [INFO] |-- model: 'nvidia/nemotron-3-nano-30b-a3b'
[03:30:05] [INFO] |-- model alias: 'nemotron-nano-v3'
[03:30:05] [INFO] |-- model provider: 'nvidia'
[03:30:05] [INFO] |-- inference parameters:
[03:30:05] [INFO] | |-- generation_type=chat-completion
[03:30:05] [INFO] | |-- max_parallel_requests=4
[03:30:05] [INFO] | |-- extra_body={'chat_template_kwargs': {'enable_thinking': False}}
[03:30:05] [INFO] | |-- temperature=1.00
[03:30:05] [INFO] | |-- top_p=1.00
[03:30:05] [INFO] | |-- max_tokens=2048
[03:30:05] [INFO] โก๏ธ Processing llm-text column 'physician_notes' with 4 concurrent workers
[03:30:05] [INFO] โฑ๏ธ llm-text column 'physician_notes' will report progress after each record
[03:30:14] [INFO] |-- ๐ llm-text column 'physician_notes' progress: 1/10 (10%) complete, 1 ok, 0 failed, 0.12 rec/s, eta 73.6s
[03:30:14] [INFO] |-- ๐ llm-text column 'physician_notes' progress: 2/10 (20%) complete, 2 ok, 0 failed, 0.24 rec/s, eta 33.2s
[03:30:14] [INFO] |-- ๐ llm-text column 'physician_notes' progress: 3/10 (30%) complete, 3 ok, 0 failed, 0.36 rec/s, eta 19.7s
[03:30:16] [INFO] |-- ๐ llm-text column 'physician_notes' progress: 4/10 (40%) complete, 4 ok, 0 failed, 0.36 rec/s, eta 16.5s
[03:30:18] [INFO] |-- ๐ llm-text column 'physician_notes' progress: 5/10 (50%) complete, 5 ok, 0 failed, 0.41 rec/s, eta 12.3s
[03:30:20] [INFO] |-- ๐ llm-text column 'physician_notes' progress: 6/10 (60%) complete, 6 ok, 0 failed, 0.41 rec/s, eta 9.8s
[03:30:21] [INFO] |-- ๐ llm-text column 'physician_notes' progress: 7/10 (70%) complete, 7 ok, 0 failed, 0.44 rec/s, eta 6.8s
[03:30:26] [INFO] |-- ๐ llm-text column 'physician_notes' progress: 8/10 (80%) complete, 8 ok, 0 failed, 0.39 rec/s, eta 5.2s
[03:30:27] [INFO] |-- ๐ llm-text column 'physician_notes' progress: 9/10 (90%) complete, 9 ok, 0 failed, 0.42 rec/s, eta 2.4s
[03:30:30] [INFO] |-- ๐ llm-text column 'physician_notes' progress: 10/10 (100%) complete, 10 ok, 0 failed, 0.40 rec/s, eta 0.0s
[03:30:31] [INFO] ๐ Model usage summary:
[03:30:31] [INFO] |-- model: nvidia/nemotron-3-nano-30b-a3b
[03:30:31] [INFO] |-- tokens: input=1615, output=7609, total=9224, tps=363
[03:30:31] [INFO] |-- requests: success=10, failed=0, total=10, rpm=23
[03:30:31] [INFO] ๐ Measuring dataset column statistics:
[03:30:31] [INFO] |-- ๐ฒ column: 'patient_sampler'
[03:30:31] [INFO] |-- ๐ฒ column: 'doctor_sampler'
[03:30:31] [INFO] |-- ๐ฒ column: 'patient_id'
[03:30:31] [INFO] |-- ๐งฉ column: 'first_name'
[03:30:31] [INFO] |-- ๐งฉ column: 'last_name'
[03:30:31] [INFO] |-- ๐งฉ column: 'dob'
[03:30:31] [INFO] |-- ๐ฒ column: 'symptom_onset_date'
[03:30:31] [INFO] |-- ๐ฒ column: 'date_of_visit'
[03:30:31] [INFO] |-- ๐งฉ column: 'physician'
[03:30:31] [INFO] |-- ๐ column: 'physician_notes'
Inย [12]:
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[12]:
| diagnosis | patient_summary | patient_sampler | doctor_sampler | patient_id | symptom_onset_date | date_of_visit | first_name | last_name | dob | physician | physician_notes | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | cervical spondylosis | I've been having a lot of pain in my neck and ... | {'age': 34, 'bachelors_field': 'no_degree', 'b... | {'age': 44, 'bachelors_field': 'education', 'b... | PT-F84EFE4C | 2024-11-12T00:00:00 | 2024-11-16T00:00:00 | Matthew | Cole | 1991-12-27 | Dr. Nielsen | Patient: Matthew Cole DOB: [Not provided] ... |
| 1 | impetigo | I have a rash on my face that is getting worse... | {'age': 67, 'bachelors_field': 'no_degree', 'b... | {'age': 48, 'bachelors_field': 'no_degree', 'b... | PT-60F394C5 | 2024-01-02T00:00:00 | 2024-01-18T00:00:00 | John | Brown | 1958-05-26 | Dr. Vasquez | **Patient:** John Brown **DOB:** [Not provid... |
| 2 | urinary tract infection | I have been urinating blood. I sometimes feel ... | {'age': 95, 'bachelors_field': 'no_degree', 'b... | {'age': 86, 'bachelors_field': 'no_degree', 'b... | PT-488FE7C5 | 2024-09-13T00:00:00 | 2024-09-29T00:00:00 | David | Johnson | 1930-07-07 | Dr. Paul | **Chief Complain:** Gross hematuria, dysuria, ... |
| 3 | arthritis | I have been having trouble with my muscles and... | {'age': 22, 'bachelors_field': 'no_degree', 'b... | {'age': 80, 'bachelors_field': 'arts_humanitie... | PT-4A7732D1 | 2024-03-17T00:00:00 | 2024-03-26T00:00:00 | Katelyn | Castro | 2004-03-04 | Dr. Goodwin | **S: DX:** 3/17/2024 โ Onset of neck/should... |
| 4 | dengue | I have been feeling really sick. My body hurts... | {'age': 80, 'bachelors_field': 'arts_humanitie... | {'age': 82, 'bachelors_field': 'no_degree', 'b... | PT-42927D65 | 2024-02-11T00:00:00 | 2024-02-17T00:00:00 | Richard | Griffin | 1945-04-27 | Dr. Soto | - 2024-02-17 00:00 | RG: 555-78-9210 - VM: 1... |
Inย [13]:
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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 10 โ 100.0% โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ ๐ฒ Sampler Columns โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโณโโโโโโโโโโโโโโโโโโณโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโณโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ โ column name โ data type โ number unique values โ sampler type โ โกโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฉ โ patient_sampler โ dict โ 10 (100.0%) โ person_from_faker โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ doctor_sampler โ dict โ 10 (100.0%) โ person_from_faker โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ patient_id โ string โ 10 (100.0%) โ uuid โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ symptom_onset_date โ string โ 8 (80.0%) โ datetime โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ date_of_visit โ string โ 10 (100.0%) โ timedelta โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ 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 physician_notes โ string โ 10 (100.0%) โ 130.5 +/- 6.1 โ 761.0 +/- 264.7 โ โโโโโโโโโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโโโโโโโโ ๐งฉ Expression Columns โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโณโโโโโโโโโโโโโโโโโโโโโโโโโโโโณโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ โ column name โ data type โ number unique values โ โกโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฉ โ first_name โ string โ 10 (100.0%) โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ last_name โ string โ 10 (100.0%) โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ dob โ string โ 10 (100.0%) โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ physician โ string โ 9 (90.0%) โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ 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
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