---
license: cc-by-4.0
language:
- en
task_categories:
- text-classification
tags:
- moderation
- toxicity
- cyberbullying
- harassment
- meme-comments
- roast-comments
- synthetic
pretty_name: Meme & Roast Comment Moderation
size_categories:
- 1K<n<10K
configs:
- config_name: default
  data_files:
  - split: train
    path: train.jsonl
  - split: validation
    path: validation.jsonl
  - split: test
    path: test.jsonl
---

# Meme & Roast Comment Moderation

A synthetic English dataset for training and evaluating moderation systems that distinguish harmless meme or roast comments from hurtful, abusive, or dangerous comments.

The dataset contains **1,260 comments** and ready-made `train`, `validation`, and `test` splits.

## Dataset structure

Each row contains:

| Column | Description |
|---|---|
| `text` | The comment being classified |
| `label` | Detailed moderation category |
| `action` | Recommended action: `allow` or `block` |
| `severity` | Severity score from `0` to `5` |
| `reason` | Short human-readable reason |

## Labels

| Label | Action | Meaning |
|---|---|---|
| `allow` | allow | Harmless jokes, playful roasts, disagreement, or non-targeted humor |
| `block_insult` | block | Direct degrading or humiliating insults |
| `block_harassment` | block | Repeated, coordinated, or targeted harassment |
| `block_body_shaming` | block | Attacks about appearance or body |
| `block_threat` | block | Threats of violence or intimidation |
| `block_sexual_harassment` | block | Sexual harassment, coercion, or inappropriate requests |
| `block_hate` | block | Attacks based on a protected characteristic |
| `block_self_harm_encouragement` | block | Encouragement of suicide or self-harm |

## Splits

| Split | Approximate size |
|---|---:|
| Train | 80% |
| Validation | 10% |
| Test | 10% |

## Usage

```python
from datasets import load_dataset

dataset = load_dataset("exnivo/meme-roast-moderation")

print(dataset)
print(dataset["train"][0])
```

Binary classification using the `action` field:

```python
label_to_id = {
    "allow": 0,
    "block": 1,
}

def add_binary_label(example):
    example["binary_label"] = label_to_id[example["action"]]
    return example

dataset = dataset.map(add_binary_label)
```

Multi-class classification using the `label` field:

```python
labels = sorted(set(dataset["train"]["label"]))
label_to_id = {label: index for index, label in enumerate(labels)}

def add_label_id(example):
    example["label_id"] = label_to_id[example["label"]]
    return example

dataset = dataset.map(add_label_id)
```

## Intended uses

This dataset can be used as starter data for:

- Comment moderation classifiers
- Roblox, game-chat, and community safety systems
- Cyberbullying and harassment detection experiments
- Binary `allow` versus `block` classification
- Multi-class harmful-content classification

## Limitations

This is a **synthetic starter dataset**, not a production-ready safety benchmark.

The examples may not represent all slang, spelling mistakes, languages, cultural contexts, reclaimed language, sarcasm, or rapidly changing meme vocabulary. Models trained only on this dataset may over-block friendly banter or fail to detect indirect harassment.

Before production use:

1. Add anonymized real-world examples from the target platform.
2. Have humans review uncertain and high-severity predictions.
3. Evaluate false positives separately for harmless jokes and friendly roasting.
4. Test obfuscated spellings, emojis, slang, and conversational context.
5. Do not use the model as the sole basis for serious account penalties.

## Sensitive content

The dataset contains examples of insults, threats, harassment, sexual harassment, hate-based attacks, and self-harm encouragement for moderation research. It does not intentionally contain real personal information.

## Data creation

The comments were synthetically generated and automatically assigned moderation fields. The dataset should be manually audited and expanded before serious deployment.

## License

Released under the Creative Commons Attribution 4.0 International license.