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---
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
---
# 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"
}
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