Guides
Label data with the tagger
Manual, Co-pilot and Autopilot labeling, review rounds, and export.
The tagger adds labels to the rows of a dataset. You can label every row yourself, or let an AI model label rows while you review its work. The result is a labeled dataset that you can use in a run.
Start a session
Open the tagger in one of these ways:
- Open the tagger from the dashboard.
- In Datasets, select one dataset and click Tag this dataset.
- In the dataset editor, click Tag with AI.
Choose the data
The setup asks for the data to label:
- Choose from your library uses a dataset that you uploaded. To upload a new file, use Datasets first.
- Generate a synthetic dataset creates rows for you. This option needs the assistant.
Choose the task type
| Task type | What you give each row |
|---|---|
| Yes / No question | A yes or no answer. |
| Pick from categories | One or more categories from a list that you define. Add at least two categories. |
| Written answers | A free text answer. |
Choose a mode
The setup asks "How do you want to tag?". There are three modes:
| Mode | What happens |
|---|---|
| Manual | You label every row yourself. No AI is involved. |
| Co-pilot | After a short interview, the AI tags a first batch. You keep or correct each label in review rounds. |
| Autopilot | After a short interview, the AI tags all rows and flags the rows it is not sure about. |
The setup marks one mode as Recommended.
The setup also has a Tagging model choice. The default suits most datasets.
The dataset interview
Co-pilot and Autopilot start with a Dataset interview. The AI asks questions about your data and your labels. You can click Skip the interview.
The interview builds a Labeling guide. The guide holds the rules that the AI follows. Use Add a rule to add your own rule.
At the end, the AI shows Here's the plan. Then you start the work:
- In Co-pilot, click Tag the first {rows} rows for my review.
- In Autopilot, click Tag all {rows} rows.
Co-pilot review rounds
In Co-pilot, the AI first tags a calibration batch. The batch size depends on the task type:
| Task type | First batch |
|---|---|
| Yes / No question | 24 rows |
| Pick from categories | 6 rows for each category, at least 30 and at most 60 |
| Written answers | 30 rows |
If the dataset has fewer rows, the AI tags all of them.
Review each label
For each row, the side rail shows the AI label:
- Read the row and the label.
- To keep the label, click Confirm (Enter) or press Enter.
- To change the label, correct it. The rail shows "Corrected — the AI will learn from this".
- When you finish, click Finish this round.
The rail shows the target: "Once we agree {gate}% of the time, I'll tag the rest myself."
The agreement gate
After each round, Skynet measures how often you agreed with the AI. The gate is 92% for Yes / No and category tasks. It is 85% for written answers.
The round summary shows "You agreed with the AI on {agreement}%". Then you choose:
- One more review round gives you another batch to review.
- Tag the remaining {rows} rows lets the AI tag the rest.
Autopilot and automatic tagging
When the AI tags the remaining rows, the work runs on the server. You can safely leave the page and come back.
- Resume tagging continues a stopped job.
- Cancel — keep what's tagged stops the job and keeps the labels that exist.
Review unsure rows
The AI gives each automatic label a confidence. Skynet flags a row when the confidence is below 0.75. After the job, click Review {count} rows the AI wasn't sure about to check them.
Manual mode
In Manual mode, you label every row yourself. The keyboard shortcuts below help you work fast.
Keyboard shortcuts
| Key | Action |
|---|---|
| Ctrl+H or Cmd+H | Show the shortcuts. |
| Y / N | Answer yes or no on a Yes / No row. |
| 1 to 9 | Select or clear that category on a category row. |
| U | Go to the next untagged row. |
| Home | Go to the first row. |
| E | Export as CSV. |
Results
The results show where each label came from:
| Count | Meaning |
|---|---|
| Labeled by you | Labels that you wrote. |
| AI labels you confirmed | AI labels that you kept in review. |
| Labeled automatically | AI labels with no review. |
| Cost | The cost of the AI work in this session. |
The Source column shows the same values for each row: You, AI confirmed or AI.
Save and export
Save to the library
Click Save to library or Move to Datasets to keep the labeled data as a dataset. Then you can use it in a run.
Export a file
Click Export tags and choose a format:
- CSV
- JSON
- Excel (
.xlsx) - Parquet
- Feather
If some rows have no label, Skynet asks you to confirm the export.
The export adds a label column. Its name depends on the task type:
| Task type | Column |
|---|---|
| Yes / No question | binary_label |
| Pick from categories | selected_categories |
| Written answers | extracted_text |
A label_source column records where each label came from.
Sessions
Each tagging job is a session. A session has one of these statuses: In progress, In review, Auto-labeling or Done. Click Start new session to begin again with new settings.
You can share a session with other people from its share dialog. For sharing rules, see Sharing.
Next steps
- Prepare a dataset before you tag.
- Submit a run with the labeled data.
- Ask the agent panel about your tagging sessions.