datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
insurance-charge-mlops-logsinsurance-charge-mlops-logsinsurance-charge-mlops-logsinsurance-charge-logsinsurance-charge-mlops-logsinsurance-charge-mlops-logsv2_insurance-charge-mlops-logsinsurance-charge-mlops-logsinsurance-charge-mlops-logs-v3aprm-sft-thoughts-snorkel-insurance-policy_best-adamw30-lp0
Act-PRM SFT thoughts — snorkel-insurance insurance
Act-PRM (Action Process Reward Models) infers the latent thoughts behind
logged, action-only agent demonstrations via an offline EM. For each
logged action x in state s we sample G=4 candidate thoughts z,
score each by the length-penalized action likelihood
reward(z) = p(x | s, z)
(len_frac grows with the thought's token length), and mark the best thought
(argmax reward). The (thought + action) span is then what downstream SFT… See the full description on the dataset page: https://huggingface.co/datasets/mzio/aprm-sft-thoughts-snorkel-insurance-policy_best-adamw30-lp0.insurance-charge-logsHealthyLife-Insurance-Charge-Prediction-v2insurance-charge-mlops-logsinsurance-charge-mlops-logsinsurance-charge-mlops-logsaprm-sft-thoughts-snorkel-insurance-base_best-adamw30-lp0
Act-PRM SFT thoughts — snorkel-insurance insurance
Act-PRM (Action Process Reward Models) infers the latent thoughts behind
logged, action-only agent demonstrations via an offline EM. For each
logged action x in state s we sample G=4 candidate thoughts z,
score each by the length-penalized action likelihood
reward(z) = p(x | s, z)
(len_frac grows with the thought's token length), and mark the best thought
(argmax reward). The (thought + action) span is then what downstream SFT… See the full description on the dataset page: https://huggingface.co/datasets/mzio/aprm-sft-thoughts-snorkel-insurance-base_best-adamw30-lp0.insurance-charge-mlops-logsinsurance-charge-mlops-logsinsurance-charge-mlops-logsinsurance-charge-mlops-logsai-insurance-2026insurance-charge-mlops-logsinsurance-charge-mlops-logsinsurance-charge-mlops-logs-v2africa-synth-livestock-livestock-insurance-data-all
African Livestock Insurance Data | Africa (Electric Sheep Africa metadata inventory)
Size category: 10K<n<100K - Formats: csv - Sector: agriculture_food - Engineered by Electric Sheep Africa
TL;DR
This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context.
What This Dataset Covers
Public datasets… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-synth-livestock-livestock-insurance-data-all.insurance-charge-mlops-logs2insurance-charge-mlops-logsQwen3.6-27B-insurance-benchmarkBenchmark of Qwen/Qwen3.6-27B against kth8/insurance dataset.
Accuracy: 92.0%.
Metric
Value
Correct
46
Incorrect
4
Errors
0
Total samples
50
Total completion tokens
57,899
Raw stats:
{
"accuracy": 0.92,
"correct": 46,
"incorrect": 4,
"error": 0,
"total": 50,
"python_tool_calls": 0,
"completion_tokens": 57899
}
insurance-charge-mlops-logs
Insurance Charge MLOps Logs
Dataset Description
This dataset contains inference-time logs generated by a deployed machine learning model
that predicts insurance charges based on customer attributes.
The data is produced by a Gradio application running on Hugging Face Spaces as part of an
MLOps learning project.
Columns
age: Age of the individual
bmi: Body Mass Index
children: Number of dependents
sex: Gender (male, female)
smoker: Smoking status (yes, no)… See the full description on the dataset page: https://huggingface.co/datasets/salmadrigal/insurance-charge-mlops-logs.insurance-charge-mlops-logs3