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)
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DataDesignerConfigBuilder()
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)
[16:30:29] [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)
[16:30:29] [INFO] ๐ญ Preview generation in progress
[16:30:29] [INFO] โ Validation passed
[16:30:29] [INFO] โ๏ธ Sorting column configs into a Directed Acyclic Graph
[16:30:29] [INFO] ๐ฉบ Running health checks for models...
[16:30:29] [INFO] |-- ๐ Checking 'nvidia/nemotron-3-nano-30b-a3b' in provider named 'nvidia' for model alias 'nemotron-nano-v3'...
[16:30:30] [INFO] |-- โ Passed!
[16:30:30] [INFO] ๐ฑ Sampling 2 records from seed dataset
[16:30:30] [INFO] |-- seed dataset size: 820 records
[16:30:30] [INFO] |-- sampling strategy: ordered
[16:30:30] [INFO] ๐ฒ Preparing samplers to generate 2 records across 5 columns
[16:30:30] [INFO] (๐พ + ๐พ) Concatenating 2 datasets
[16:30:30] [INFO] ๐งฉ Generating column `first_name` from expression
[16:30:30] [INFO] ๐งฉ Generating column `last_name` from expression
[16:30:30] [INFO] ๐งฉ Generating column `dob` from expression
[16:30:30] [INFO] ๐งฉ Generating column `physician` from expression
[16:30:30] [INFO] ๐ llm-text model config for column 'physician_notes'
[16:30:30] [INFO] |-- model: 'nvidia/nemotron-3-nano-30b-a3b'
[16:30:30] [INFO] |-- model alias: 'nemotron-nano-v3'
[16:30:30] [INFO] |-- model provider: 'nvidia'
[16:30:30] [INFO] |-- inference parameters:
[16:30:30] [INFO] | |-- generation_type=chat-completion
[16:30:30] [INFO] | |-- max_parallel_requests=4
[16:30:30] [INFO] | |-- extra_body={'chat_template_kwargs': {'enable_thinking': False}}
[16:30:30] [INFO] | |-- temperature=1.00
[16:30:30] [INFO] | |-- top_p=1.00
[16:30:30] [INFO] | |-- max_tokens=2048
[16:30:30] [INFO] โก๏ธ Processing llm-text column 'physician_notes' with 4 concurrent workers
[16:30:30] [INFO] โฑ๏ธ llm-text column 'physician_notes' will report progress after each record
[16:30:36] [INFO] |-- ๐ llm-text column 'physician_notes' progress: 1/2 (50%) complete, 1 ok, 0 failed, 0.16 rec/s, eta 6.3s
[16:30:40] [INFO] |-- ๐คฉ llm-text column 'physician_notes' progress: 2/2 (100%) complete, 2 ok, 0 failed, 0.19 rec/s, eta 0.0s
[16:30:40] [INFO] ๐ Model usage summary:
[16:30:40] [INFO] |-- model: nvidia/nemotron-3-nano-30b-a3b
[16:30:40] [INFO] |-- tokens: input=292, output=1713, total=2005, tps=189
[16:30:40] [INFO] |-- requests: success=2, failed=0, total=2, rpm=11
[16:30:40] [INFO] ๐ Measuring dataset column statistics:
[16:30:40] [INFO] |-- ๐ฒ column: 'patient_sampler'
[16:30:40] [INFO] |-- ๐ฒ column: 'doctor_sampler'
[16:30:40] [INFO] |-- ๐ฒ column: 'patient_id'
[16:30:40] [INFO] |-- ๐งฉ column: 'first_name'
[16:30:40] [INFO] |-- ๐งฉ column: 'last_name'
[16:30:40] [INFO] |-- ๐งฉ column: 'dob'
[16:30:40] [INFO] |-- ๐ฒ column: 'symptom_onset_date'
[16:30:40] [INFO] |-- ๐ฒ column: 'date_of_visit'
[16:30:40] [INFO] |-- ๐งฉ column: 'physician'
[16:30:40] [INFO] |-- ๐ column: 'physician_notes'
[16:30:40] [INFO] โ 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()
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': '15ed186a-76c3-4bdc-88ff-18413e2dee00', โ โ โ 'locale': 'en_US', โ โ โ 'first_name': 'Anthony', โ โ โ 'last_name': 'Ramirez', โ โ โ 'middle_name': None, โ โ โ 'sex': 'Male', โ โ โ 'street_number': '11583', โ โ โ 'street_name': 'Brian Landing', โ โ โ 'city': 'Port Cynthia', โ โ โ 'state': 'Minnesota', โ โ โ 'postcode': '58357', โ โ โ 'age': 92, โ โ โ 'birth_date': '1933-12-28', โ โ โ 'country': 'Finland', โ โ โ 'marital_status': 'never_married', โ โ โ 'education_level': 'associates', โ โ โ 'unit': '', โ โ โ 'occupation': 'Production assistant, radio', โ โ โ 'phone_number': '394.576.8511', โ โ โ 'bachelors_field': 'no_degree' โ โ โ } โ โโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ doctor_sampler โ { โ โ โ 'uuid': '0c48d254-7997-4683-962e-b10fa5d9e4f4', โ โ โ 'locale': 'en_US', โ โ โ 'first_name': 'Deborah', โ โ โ 'last_name': 'Carter', โ โ โ 'middle_name': None, โ โ โ 'sex': 'Female', โ โ โ 'street_number': '3605', โ โ โ 'street_name': 'Payne Flats', โ โ โ 'city': 'North Richard', โ โ โ 'state': 'South Dakota', โ โ โ 'postcode': '58079', โ โ โ 'age': 20, โ โ โ 'birth_date': '2005-05-22', โ โ โ 'country': 'Northern Mariana Islands', โ โ โ 'marital_status': 'separated', โ โ โ 'education_level': 'secondary_education', โ โ โ 'unit': '', โ โ โ 'occupation': 'Psychiatric nurse', โ โ โ 'phone_number': '+1-378-344-2941x3415', โ โ โ 'bachelors_field': 'no_degree' โ โ โ } โ โโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ patient_id โ PT-468CB487 โ โโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ symptom_onset_date โ 2024-01-01 โ โโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ date_of_visit โ 2024-01-24 โ โโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ first_name โ Anthony โ โโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ last_name โ Ramirez โ โโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ dob โ 1933-12-28 โ โโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ physician โ Dr. Carter โ โโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค โ physician_notes โ **SCHEDULE** โ โ โ - 2024-01-24 09:00 - Anthony Ramirez (New patient intake for chronic cervical โ โ โ spondylosis; follow-up) โ โ โ - 2024-01-24 09:30 - F. Chen (Sprained wrist) โ โ โ - 2024-01-24 10:00 - M. Lopez (HTN follow-up) โ โ โ - 2024-01-24 10:30 - R. Patel (Diabetes check) โ โ โ - 2024-01-24 11:00 - **ANTHONY RAMIREZ** (Follow-up: cervical spondylosis with new โ โ โ neuro symptoms) โ โ โ โ โ โ **VISIT NOTES** โ โ โ **Patient:** Anthony Ramirez โ โ โ **DOB:** 1985-03-15 โ โ โ **Date:** 2024-01-24 โ โ โ **Chief Complaint:** Neck/back pain, worsening weakness, balance issues, chronic โ โ โ cough, 3-week decline. โ โ โ โ โ โ **HPI:** โ โ โ - 72M with 3-yr history of cervical spondylosis (diagnosed 2021). โ โ โ - **New symptoms (since 2023-12-30):** โ โ โ - Progressive neck/back pain (worsening, now 7/10 vs. prior 4/10). โ โ โ - **Neuro:** "Weakness in hands" (difficulty buttoning shirt), unsteady gait ("need โ โ โ to hold walls"), clumsiness (spilled coffee). โ โ โ - **Systemic:** Chronic dry cough (2 months, no fever, no sputum), no chest pain โ โ โ shortness of breath. โ โ โ - **Denies:** Trauma, fever, bowel/bladder changes, headache, vision changes. โ โ โ - **Past:** Baseline spondylosis stable for 2+ years; pain managed with PT/exercise. โ โ โ - **Triggers:** Pain worse with movement; improves with rest. โ โ โ โ โ โ **ROS (pertinent):** โ โ โ - **Neuro:** Weakness in hands, balance issues, "clumsiness." โ โ โ - **GI:** None (cough non-productive, no nausea). โ โ โ - **Respiratory:** Chronic cough (2 months), no hemoptysis. โ โ โ - **Priorities:** Neuro symptoms > pain > cough. โ โ โ โ โ โ **Physical Exam:** โ โ โ - **General:** Alert, no acute distress. โ โ โ - **Neck:** Limited ROM (flexion/extension), no costovertebral tenderness. โ โ โ - **Neuro:** โ โ โ - Strength: 4/5 in hands (vs. 5/5 previously), 5/5 legs. โ โ โ - Reflexes: Trace biceps/brachioradialis. โ โ โ - Coordination: Finger-nose test **positive** (intention tremor), gait **unsteady** โ โ โ (required wall support). โ โ โ - Sensation: Decreased light touch in bilateral hands. โ โ โ - **Other:** No lymphadenopathy, no skin changes. โ โ โ โ โ โ **Assessment & Plan:** โ โ โ 1. **New Neuro Symptoms:** โ โ โ - **Elevated concern for myelopathy** (cervical spondylosis causing cord โ โ โ compression). โ โ โ - **Next Steps:** โ โ โ - **EMG/NCS** (nerve conduction study) to evaluate peripheral nerve involvement. โ โ โ - **MRI C-spine** (non-contrast, upright if possible) **prioritized**โ*urgent โ โ โ referral to Neurosurgery*. โ โ โ - **Avoid** NSAIDs/physical therapy (risk of exacerbating cord compression). โ โ โ 2. **Chronic Pain:** โ โ โ - Continue PT exercises **only** as prescribed (no new strain). โ โ โ - **Medication:** Acetaminophen PRN; *avoid opioids* (risk of masking neurological โ โ โ decline). โ โ โ 3. **Chronic Cough:** โ โ โ - Rule out GERD (trial of PPI *only if* symptoms persist post-Neurology workup). โ โ โ - **Action:** Hold prescribing until neuro workup complete. โ โ โ 4. **Follow-Up:** โ โ โ - **Urgently:** Neurosurgery consult within 72 hours. โ โ โ - **Next routine:** 2024-02-01 (labs: CBC, Vitamin B12). โ โ โ โ โ โ **Note:** Patient anxious but engaged. Emphasized *urgency* of neuro workup โ โ โ (stress-related symptoms worsening). **Deferred** "cough management" until neuro โ โ โ clearance. โ โ โ โ โ โ **Order:** โ โ โ - MRI C-spine (urgent) โ โ โ - EMG/NCS (order sent to lab) โ โ โ - Neurosurgery consult (priority) โ โ โ - *Hold* current pain regimen (transition to acetaminophen only) โ โ โ โ โ โ **Provider Signature:** D. Carter, MD โ โ โ **Time:** 09:45 AM โ โ โ โ โ โ --- โ โ โ *Note: This note reflects a high-priority, time-sensitive neuro referral. Delayed โ โ โ workup = risk of permanent cord damage.* โ โโโโโโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ [index: 0]
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': '15ed186a-76c3-4bdc-88ff-18413e2dee00... | {'uuid': '0c48d254-7997-4683-962e-b10fa5d9e4f4... | PT-468CB487 | 2024-01-01 | 2024-01-24 | Anthony | Ramirez | 1933-12-28 | Dr. Carter | **SCHEDULE** \n- 2024-01-24 09:00 - Anthony R... |
| 1 | impetigo | I have a rash on my face that is getting worse... | {'uuid': 'dfe3d22a-5132-4c6c-9f7b-38d437a2f373... | {'uuid': 'f7aadbf5-fc6a-4cba-a843-d282518742c4... | PT-0080DFB2 | 2024-07-23 | 2024-08-03 | Matthew | Buck | 1963-08-05 | Dr. Murphy | MURPHY, DR. J. \n2024-08-03 | 09:15 AM | INT.... |
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 122.0 +/- 4.0 โ 770.5 +/- 352.9 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")
[16:30:40] [INFO] ๐จ Creating Data Designer dataset
[16:30:40] [INFO] โ Validation passed
[16:30:40] [INFO] โ๏ธ Sorting column configs into a Directed Acyclic Graph
[16:30:40] [INFO] ๐ฉบ Running health checks for models...
[16:30:40] [INFO] |-- ๐ Checking 'nvidia/nemotron-3-nano-30b-a3b' in provider named 'nvidia' for model alias 'nemotron-nano-v3'...
[16:30:41] [INFO] |-- โ Passed!
[16:30:41] [INFO] โณ Processing batch 1 of 1
[16:30:41] [INFO] ๐ฑ Sampling 10 records from seed dataset
[16:30:41] [INFO] |-- seed dataset size: 820 records
[16:30:41] [INFO] |-- sampling strategy: ordered
[16:30:41] [INFO] ๐ฒ Preparing samplers to generate 10 records across 5 columns
[16:30:41] [INFO] (๐พ + ๐พ) Concatenating 2 datasets
[16:30:41] [INFO] ๐งฉ Generating column `first_name` from expression
[16:30:41] [INFO] ๐งฉ Generating column `last_name` from expression
[16:30:41] [INFO] ๐งฉ Generating column `dob` from expression
[16:30:41] [INFO] ๐งฉ Generating column `physician` from expression
[16:30:41] [INFO] ๐ llm-text model config for column 'physician_notes'
[16:30:41] [INFO] |-- model: 'nvidia/nemotron-3-nano-30b-a3b'
[16:30:41] [INFO] |-- model alias: 'nemotron-nano-v3'
[16:30:41] [INFO] |-- model provider: 'nvidia'
[16:30:41] [INFO] |-- inference parameters:
[16:30:41] [INFO] | |-- generation_type=chat-completion
[16:30:41] [INFO] | |-- max_parallel_requests=4
[16:30:41] [INFO] | |-- extra_body={'chat_template_kwargs': {'enable_thinking': False}}
[16:30:41] [INFO] | |-- temperature=1.00
[16:30:41] [INFO] | |-- top_p=1.00
[16:30:41] [INFO] | |-- max_tokens=2048
[16:30:41] [INFO] โก๏ธ Processing llm-text column 'physician_notes' with 4 concurrent workers
[16:30:41] [INFO] โฑ๏ธ llm-text column 'physician_notes' will report progress after each record
[16:30:47] [INFO] |-- ๐ง๏ธ llm-text column 'physician_notes' progress: 1/10 (10%) complete, 1 ok, 0 failed, 0.16 rec/s, eta 55.5s
[16:30:49] [INFO] |-- ๐ง๏ธ llm-text column 'physician_notes' progress: 2/10 (20%) complete, 2 ok, 0 failed, 0.23 rec/s, eta 34.3s
[16:30:50] [INFO] |-- ๐ฆ๏ธ llm-text column 'physician_notes' progress: 3/10 (30%) complete, 3 ok, 0 failed, 0.34 rec/s, eta 20.5s
[16:30:53] [INFO] |-- ๐ฆ๏ธ llm-text column 'physician_notes' progress: 4/10 (40%) complete, 4 ok, 0 failed, 0.32 rec/s, eta 18.9s
[16:30:55] [INFO] |-- โ llm-text column 'physician_notes' progress: 5/10 (50%) complete, 5 ok, 0 failed, 0.36 rec/s, eta 13.8s
[16:30:57] [INFO] |-- โ llm-text column 'physician_notes' progress: 6/10 (60%) complete, 6 ok, 0 failed, 0.37 rec/s, eta 10.8s
[16:31:02] [INFO] |-- โ llm-text column 'physician_notes' progress: 7/10 (70%) complete, 7 ok, 0 failed, 0.34 rec/s, eta 8.9s
[16:31:03] [INFO] |-- ๐ค๏ธ llm-text column 'physician_notes' progress: 8/10 (80%) complete, 8 ok, 0 failed, 0.36 rec/s, eta 5.5s
[16:31:03] [INFO] |-- ๐ค๏ธ llm-text column 'physician_notes' progress: 9/10 (90%) complete, 9 ok, 0 failed, 0.41 rec/s, eta 2.5s
[16:31:09] [INFO] |-- โ๏ธ llm-text column 'physician_notes' progress: 10/10 (100%) complete, 10 ok, 0 failed, 0.36 rec/s, eta 0.0s
[16:31:09] [INFO] ๐ Model usage summary:
[16:31:09] [INFO] |-- model: nvidia/nemotron-3-nano-30b-a3b
[16:31:09] [INFO] |-- tokens: input=1430, output=8597, total=10027, tps=357
[16:31:09] [INFO] |-- requests: success=10, failed=0, total=10, rpm=21
[16:31:09] [INFO] ๐ Measuring dataset column statistics:
[16:31:09] [INFO] |-- ๐ฒ column: 'patient_sampler'
[16:31:09] [INFO] |-- ๐ฒ column: 'doctor_sampler'
[16:31:09] [INFO] |-- ๐ฒ column: 'patient_id'
[16:31:09] [INFO] |-- ๐งฉ column: 'first_name'
[16:31:09] [INFO] |-- ๐งฉ column: 'last_name'
[16:31:09] [INFO] |-- ๐งฉ column: 'dob'
[16:31:09] [INFO] |-- ๐ฒ column: 'symptom_onset_date'
[16:31:09] [INFO] |-- ๐ฒ column: 'date_of_visit'
[16:31:09] [INFO] |-- ๐งฉ column: 'physician'
[16:31:09] [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': 100, 'bachelors_field': 'stem_related'... | {'age': 36, 'bachelors_field': 'no_degree', 'b... | PT-467824AD | 2024-06-21 | 2024-07-11 | Christopher | Powell | 1925-07-28 | Dr. Galloway | **2024-07-11 | 9:15 AM | CHRISTOPHER POWELL, M... |
| 1 | impetigo | I have a rash on my face that is getting worse... | {'age': 74, 'bachelors_field': 'no_degree', 'b... | {'age': 112, 'bachelors_field': 'no_degree', '... | PT-FB7E8578 | 2024-09-04 | 2024-09-25 | Katherine | Pearson | 1951-12-27 | Dr. Lewis | **Patient:** Katherine Pearson **DOB:** 04/1... |
| 2 | urinary tract infection | I have been urinating blood. I sometimes feel ... | {'age': 88, 'bachelors_field': 'no_degree', 'b... | {'age': 73, 'bachelors_field': 'stem', 'birth_... | PT-362E398D | 2024-10-30 | 2024-11-10 | Jesse | Carter | 1937-10-23 | Dr. Barrett | **Visit: 2024-11-10 | Patient: Jesse Carter** ... |
| 3 | arthritis | I have been having trouble with my muscles and... | {'age': 106, 'bachelors_field': 'arts_humaniti... | {'age': 104, 'bachelors_field': 'business', 'b... | PT-4E1C673A | 2024-04-29 | 2024-05-27 | Raymond | Barnett | 1920-01-12 | Dr. Rodriguez | **Visit Note - 2024-05-27 | Dr. D. Rodriguez**... |
| 4 | dengue | I have been feeling really sick. My body hurts... | {'age': 99, 'bachelors_field': 'no_degree', 'b... | {'age': 46, 'bachelors_field': 'stem_related',... | PT-3DB2FE47 | 2024-09-12 | 2024-10-07 | Derek | Welch | 1927-01-18 | Dr. Pena | **Patient:** Derek Welch **DOB:** 03/14/1987... |
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 10 (100.0%) 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 117.5 +/- 5.8 โ 800.5 +/- 267.7 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 10 (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
โญ๏ธ Next Stepsยถ
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