Update README.md

README.md

@@ -21,4 +21,35 @@ 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).
+- **Example**:  
+  - **Batch 1 (input)**: `"IN the house of"`  
+    - **Batch 1 (output)**: `"the house of there"`  
+  - **Batch 2 (input)**: `"there lives a wolf,"`
+    - **Batch 2 (output)**: `"lives a wolf, some"`  
+  (Hypothetical example)
+
+## 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": "IN the house of",
+  "output_text": "the house of there"
+}