size_categories:1K<n<10K format:json modality:text library:datasets library:pandas library:mlcroissant library:polars region:us
---
configs:
- config_name: default
  data_files:
  - split: train_rapid_transit_systems
    path: "rapid_transit_systems/train.jsonl"
  - split: loc_rapid_transit_systems
    path: "rapid_transit_systems/loc.jsonl"
  - split: train_smoking
    path: "smoking/train.jsonl"
  - split: loc_smoking
    path: "smoking/loc.jsonl"
  - split: train_liquor_control
    path: "liquor_control/train.jsonl"
  - split: loc_liquor_control
    path: "liquor_control/loc.jsonl"
---


# Regulations:

- Mass Rapid Transit system (M)
- Smoking Control (S)
- Liquor Control (L)

Severity: Describes how severe the scenario is:

```bash
raw_severity(x) = [0 - 1] assigned by the `gpt-4o`
actions = list of actions in the scenario
severity = average(raw_severity(action) - mitigating_factor(action) + aggravating_factor(action) for action in actions)
```

# Split:

- train_rapid_transit_systems: contains scenarios of M with severity > 0
- loc_rapid_transit_systems: contains scenarios of M with severity == 0
- train_smoking: contains scenarios of S with severity > 0
- loc_smoking: contains scenarios of S with severity == 0
- train_liquor_control: contains scenarios of L with severity > 0
- loc_liquor_control: contains scenarios of L with severity == 0

# Usage:

``` python
from datatasets import load_dataset

load_dataset('taidn/Llama-3.1-8B-Instruct', split='xxxx') # xxx is the splitname
```