> ## Documentation Index
> Fetch the complete documentation index at: https://docs.simplismart.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Sequence Classification

> Sequence classification dataset preparation

Your dataset must be in **JSONL format**, where each line contains the input text and its corresponding label for text classification.

**Example JSONL Entry:**

```json theme={null}
{
  "messages": [
    {
      "role": "user",
      "content": "The weather is really nice today"
    }
  ],
  "label": 1
}
{
  "messages": [
    {
      "role": "user",
      "content": "Today is really unlucky"
    }
  ],
  "label": 0
}
```

**Field Descriptions:**

* **`messages`** – Array containing the input text for classification. Each message has a `role` and `content`.
* **`role`** – Indicates the message source. Use `"user"` for encoder model training.
* **`content`** – The text sequence to be classified (e.g., product review, customer query, email content).
* **`label`** – Integer representing the target class (e.g., `0` for negative sentiment, `1` for positive sentiment).

### Example JSONL File

Here's a complete example for sentiment analysis (binary classification with 2 labels):

```json theme={null}
{"messages": [{"role": "user", "content": "The weather is really nice today"}], "label": 1}
{"messages": [{"role": "user", "content": "Today is really unlucky"}], "label": 0}
{"messages": [{"role": "user", "content": "So happy"}], "label": 1}
{"messages": [{"role": "user", "content": "This product is terrible"}], "label": 0}
{"messages": [{"role": "user", "content": "Excellent service and quality"}], "label": 1}
```

<Info>
  **Label Guidelines:**

  * Labels must be integers starting from `0`
  * For binary classification: use `0` and `1`
  * For multi-class: use `0`, `1`, `2`, etc. (e.g., 5 classes = labels 0-4)
  * Ensure all labels in your dataset are represented in the training data
</Info>