---
dataset_info:
  features:
  - name: input_text
    dtype: string
  - name: output_text
    dtype: string
  splits:
  - name: train
    num_bytes: 300443
    num_examples: 2629
  download_size: 198694
  dataset_size: 300443
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: apache-2.0
task_categories:
- token-classification
language:
- en
pretty_name: Pretraining_dataset
---

# sample-no-overfit

A short-story dataset where **each input is a non-overlapping context of 20 tokens**, and the **output** is the **same 20 tokens shifted by one position**. This means **no overlap** between consecutive batches, reducing the risk of overfitting to the same text segments.

## Dataset Overview

- **Name:** `sample-no-overfit`
- **Context Size (`context_size`):** 20
- **Stride/Step:** After one batch of 20 tokens, we move to the **next 20 tokens** (no overlap).

## Why No Overlap?
Typical language modeling approaches may overlap consecutive batches for more training samples, but can lead to learning the same context repeatedly. Here, **each batch is distinct** and does **not share** tokens with the previous batch. This helps **reduce overfitting** and ensures **more variety** in each batch.

## Data Format
Each row in the dataset contains:
- **`input_text`**: A 20-token sequence from the short story.
- **`output_text`**: The **next 20 tokens**, shifted by one position.

**Example Row**:
```json
{
  "input_text": "t huis, waar deze eerlooze schurk, Michael Popow",
  "output_text": "huis, waar deze eerlooze schurk, Michael Popowitch"
}