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language:
- en
license: mit
task_categories:
- text-generation
- question-answering
tags:
- reddit
- education
- ipmat
- entrance-exam
- india
size_categories:
- 1K<n<10K
---
# r/IPMATtards Reddit Dataset
## Dataset Description
This dataset contains scraped posts and comments from the **r/IPMATtards** subreddit, a community dedicated to aspirants of the **Integrated Programme in Management Aptitude Test (IPMAT)** in India.
The data is structured into two main components:
1. **Posts**: Top-level submissions including titles, body text, scores, and metadata.
2. **Comments**: Threaded replies associated with the posts, including recursion depth and parent-child relationships.
## Dataset Structure
The dataset is provided in **Parquet** format, optimized for the Hugging Face Hub.
### 1. Posts (`posts.parquet`)
| Feature | Type | Description |
|:--------|:-----|:------------|
| `id` | string | Unique Reddit ID for the post (e.g., `t3_xyz`) |
| `title` | string | Title of the post |
| `author` | string | Reddit username of the author |
| `body` | string | The main text content of the post |
| `score` | int64 | Net upvotes (karma) at time of scraping |
| `comments_count` | int64 | Number of comments on the post |
| `created_utc` | string | Creation timestamp (UTC) |
| `url` | string | Direct URL to the reddit post |
| `crawled_at` | string | Timestamp when the data was scraped |
### 2. Comments (`comments.parquet`)
| Feature | Type | Description |
|:--------|:-----|:------------|
| `id` | string | Unique Reddit ID for the comment (e.g., `t1_abc`) |
| `post_id` | string | ID of the parent post (`t3_...`) |
| `parent_id` | string | ID of the parent comment or post this replies to |
| `author` | string | Reddit username of the author |
| `body` | string | Text content of the comment |
| `score` | int64 | Net upvotes |
| `depth` | int64 | Nesting level (0 = top-level comment) |
| `path` | string | Materialized path for reconstructing the tree |
| `created_utc` | string | Creation timestamp (UTC) |
## Usage
You can load this dataset directly using the `datasets` library:
```python
from datasets import load_dataset
# Load posts
posts = load_dataset("parquet", data_files="parquet_export/posts.parquet", split="train")
# Load comments
comments = load_dataset("parquet", data_files="parquet_export/comments.parquet", split="train")
print(posts[0])
```
## Collection
This data was collected using a custom Python scraper utilizing `requests` and `BeautifulSoup`. It targets the old.reddit.com interface for efficiency.
## License
This dataset is distributed under the MIT License. Please respect Reddit's API cleaning rules and user privacy when using this data.
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