Paste CSV data with a header row. Commas, tabs, semicolons, and pipes are supported.
Enter one field per line or comma separated values.
Ready to map fields
Paste your data, define dataset fields, and build a mapping.// map dataset columns to export-ready schema fields
Map CSV or pasted table columns to target dataset fields, preview transformed rows, and export reusable field mappings in the browser.
Paste CSV data with a header row. Commas, tabs, semicolons, and pipes are supported.
Enter one field per line or comma separated values.
Ready to map fields
Paste your data, define dataset fields, and build a mapping.Add CSV or table-like data with a header row so the mapper can detect columns.
List the dataset fields you want to export, such as instruction, output, label, or metadata.
Pick a source column for each target field, preview the result, then copy or download the mapping JSON.
Dataset Field Mapper helps you bridge the gap between messy source columns and a clean dataset schema. It is useful when your CSV headers do not match the fields your training, evaluation, or export pipeline expects.
No. This version is designed for browser-based mapping and preview workflows.
This starter version supports one source column per target field to keep the exported mapping simple and reusable.
CSV plus common delimiter-separated text such as tab, semicolon, and pipe-delimited tables.
The export contains target fields mapped to source column names so you can reuse the configuration elsewhere.
A dataset field mapper is a lightweight transformation helper that lets you line up the columns you already have with the dataset fields you actually need. Teams run into this problem constantly. One CSV might use headers such as prompt, answer, and tag, while another training workflow expects instruction, output, and label. The data itself may already be good enough, but the field names do not match the target schema. A field mapper solves that mismatch without forcing you to manually rewrite every row.
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When you work with AI prompts, evaluation datasets, annotations, or tabular exports, consistency matters. Schema drift is one of the most common causes of broken imports and confusing downstream workflows. Even small differences such as question versus instruction or gold_answer versus output can create avoidable friction. A mapping layer lets you standardize inputs before you commit to a final export format.
The benefit is not just technical cleanliness. It also improves collaboration. Analysts, prompt designers, and operations teams often work from different source files created by different systems. A clear mapper provides a human-readable bridge between those systems. That makes review easier, handoff cleaner, and repeated imports far less error-prone.
The basic process is simple. First, the tool reads your header row and detects the available source columns. Next, you define the target fields that your export pipeline needs. Finally, you assign one source column to each target field. Once the mapping is selected, the tool builds a small preview using the first few rows so you can verify that each field is filled correctly.
This preview step is important because a bad mapping can look valid at a glance. For example, you might accidentally map category into output, or use a short title field instead of the full response column. A row preview catches those mistakes early, before you rely on the mapping in another tool or script.
prompt to instruction and response to output.text to input and class to label.question, reference_answer, and difficulty into a shared schema.A reusable mapping file means you do not have to start from scratch every time you receive a new batch from the same source. If a vendor, spreadsheet, or internal process keeps the same source column names, you can keep reusing the same mapping JSON. That turns the mapper from a one-off helper into a repeatable operational shortcut.
It also makes integrations easier. A JSON export can be read by scripts, transformation utilities, or another browser tool. Instead of embedding column assumptions directly into code, you can externalize that logic into a small configuration object. That keeps your pipeline more flexible and easier to maintain.
Start with a trustworthy header row. Make sure the first row truly contains column names and not sample data. Use target field names that match your final schema exactly, including capitalization rules if your pipeline depends on them. Review at least a few preview rows before saving the mapping, especially when source columns have similar names.
It also helps to keep optional fields separate from required ones. If your schema includes only a few mandatory fields, map those first and verify them before adding labels, IDs, or metadata. This reduces confusion and makes debugging simpler if something looks off in the preview.
Use a dataset field mapper whenever your source data is structurally close to what you need but naming differences prevent a direct export. It is especially helpful for AI datasets, prompt libraries, annotation files, spreadsheet-based reviews, and one-off vendor imports. Instead of renaming columns manually or editing raw files in a text editor, you can make the structure explicit and save the result as a reusable rule set.
That combination of visibility, speed, and repeatability is exactly why a small browser-based mapper is so practical. It keeps the transformation step simple without hiding the logic from the person doing the work.