Sequence Classification

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

{
  "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):

{"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}

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

SFT (VLM)DPO (LLM)

⌘I

Get Started

Types of Inference

Playground

Model Compilation

Deployment

Benchmarking

Training

Settings

Observability

References

Sequence Classification

Example JSONL File

​Example JSONL File

Example JSONL File