---
license: cc-by-4.0
language:
- en
pretty_name: NAPEval Spreadsheet Trajectories
size_categories:
- n<1K
tags:
- spreadsheets
- excel
- next-action-prediction
- autocomplete
- symbolic
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test.jsonl
dataset_info:
  features:
  - name: name
    dtype: string
  - name: operations
    sequence: string
  - name: num_operations
    dtype: int64
  splits:
  - name: test
    num_examples: 52
---

# NAPEval Spreadsheet Trajectories

Benchmark trajectories for **predictive auto-completion in spreadsheets**. Each
record is one spreadsheet-building session represented as an *ordered* sequence
of symbolic cell operations. Given a prefix of a trajectory, the task is to
predict the next operation(s) the user will perform.

This is the evaluation data for the
[`next_action_pred_eval`](https://github.com/napeval/next_action_pred_eval)
framework. See the [project page](https://napeval.github.io) and
[paper](https://arxiv.org/abs/2606.13802).

## Dataset summary

- **52 trajectories** (single `test` split — this is an evaluation benchmark).
- **11907 total operations**; per-trajectory length ranges 35–821.
- **23 operation types**: `ALIGN_HORIZONTAL`, `ALIGN_VERTICAL`, `AUTOFILL`, `BORDER_ALL`, `BORDER_BOTTOM`, `BORDER_INSIDE_VERTICAL`, `BORDER_LEFT`, `BORDER_OUTSIDE`, `BORDER_RIGHT`, `BORDER_TOP`, `FILL_COLOR`, `FONT_BOLD`, `FONT_COLOR`, `FONT_ITALIC`, `FONT_NAME`, `FONT_SIZE`, `FONT_UNDERLINE`, `INPUT`, `MERGE`, `NUMBER_FORMAT`, `PASTE_FROM`, `TEXT_ORIENTATION`, `WRAP_TEXT`.

## Fields

| Field | Type | Description |
|---|---|---|
| `name` | string | Trajectory id (stable, anonymised). |
| `operations` | list[string] | Ordered editing history; each entry is one symbolic operation. |
| `num_operations` | int | Convenience length of `operations`. |

## The symbolic DSL

Every spreadsheet edit is a pipe-delimited string:

```
OPERATION_TYPE | Sheet!Range | value
```

Examples:

| Symbolic string | Meaning |
|---|---|
| `INPUT \| Sheet1!A1 \| "Title"` | Set A1 to a string value |
| `INPUT \| Sheet1!A1:B2 \| [[1, 2], [3, 4]]` | Bulk-input a 2×2 block |
| `FONT_BOLD \| Sheet1!A5 \| True` | Bold A5 |
| `MERGE \| Sheet1!A1:G1 \| true` | Merge a range |
| `BORDER_TOP \| Sheet1!A8:G8 \| Thin, Continuous, #000000` | Top border on a row |

The canonical parsing/serialisation logic lives in the framework's
`core/operations/` module.

## Usage

```python
from datasets import load_dataset

ds = load_dataset("Tej-a55/napeval", split="test")
print(ds[0]["name"], ds[0]["num_operations"])
prefix = ds[0]["operations"][:10]   # history fed to a solver
```

## Supplementary raw artifacts

Each trajectory also ships per-trajectory raw artifacts under `raw/<id>/`:

| File | Description |
|---|---|
| `spreadsheet.xlsx` | The finished workbook (rebuilt from the operations). |
| `sheet_image.png` | A rendered image of the finished sheet. |
| `predictable_state.json` | Annotations: per-cell predictable properties + coverage stats. |
| `operations.txt` | Plain-text mirror of `operations` (one op per line). |

These are **supplementary** — not required to run the benchmark. They live
outside the parquet dataset (so they do not appear in the dataset viewer).
Download them with:

```python
from huggingface_hub import snapshot_download

snapshot_download("Tej-a55/napeval", repo_type="dataset", allow_patterns="raw/*")
```

## License

[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/).