---
license: apache-2.0
task_categories:
  - time-series-forecasting
tags:
  - timeseries
  - forecasting
  - benchmark
  - gifteval
  - pretraining
size_categories:
  - 100K<n<1M
---

# gift-pretrain-small

A small, single-source training bundle plus a mirror of the GIFT-Eval
evaluation set, packaged together for convenience. Built by uniformly
sampling **10 series from every sub-dataset** of
[`Salesforce/GiftEvalPretrain`](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain),
then cropping each selected series into non-overlapping windows of
length 1025 and globally shuffling the result.

This bundle exists as a sanity / smoke-test corpus before scaling up to
the full GIFT-Eval Pretrain. Every sub-dataset of GIFT-Eval Pretrain
(~152 directories at the time of build) contributes 10 series; nothing
is dropped to byte-weight underflow.

## Layout

```
.
├── small_v1/
│   ├── shard_NNNNN.parquet   ← gift-only training shards
│   └── manifest.json         ← total rows, num shards, source_counts
├── eval/                     ← Salesforce/GiftEval mirror (bytes-as-is)
│   ├── m4_daily/
│   ├── ett1/
│   └── ...
└── README.md
```

The `eval/` tree mirrors `Salesforce/GiftEval` exactly so you can point
the official `gift-eval` tooling at it without translation.

## Schema (`small_v1/shard_*.parquet`)

| Column | Type | Notes |
|---|---|---|
| `series` | `list<float32>[1025]` | Fixed-length non-overlapping window |
| `source_id` | `uint8` | Always `0` (gift) — bundle is single-source |
| `meta` | `string` | Original `item_id` from the source arrow file |

Compression: `zstd`, row group size 10_000.

## Sampling

For each sub-dataset (top-level directory) in
`Salesforce/GiftEvalPretrain`, pick the **smallest** arrow file in
that directory and sample `K=10` rows from it uniformly without
replacement. From each selected row, emit every non-overlapping
window of length 1025. The output is globally shuffled at row
granularity via the two-pass bucket shuffle used by the main
contrastive-training bundles.

Sampling-bias notes:

- **One arrow file per sub-dataset.** Multi-shard sub-datasets like
  `buildings_900k/` split a single logical dataset across many arrow
  files of similar size; sampling K series within one of them is a
  valid (slightly biased) sample of the whole. Picking the smallest
  file minimizes download bandwidth.
- **No byte-weight underflow.** The byte-weighted multinomial used by
  the full contrastive-training pipeline can drop small sub-datasets
  at low budget; this bundle deliberately gives every sub-dataset an
  equal seat at the table.
- **Coverage of arrow files within a sub-dataset is intentionally
  low.** This is a "small" bundle, not an unbiased sample of the
  whole 975 GB corpus.

## Reproducing

The pipeline lives in
[`scripts/training_data_prep/`](https://github.com/jeremycochoy/rnd) of
the generating repo. Build script:

```bash
python -m training_data_prep.build_gift_only_bundle \
    --output-dir /path/to/out \
    --series-per-subdataset 10 \
    --max-workers 4
```

Upload (two calls of the existing `stage4_upload_hf.py`, one for the
bundle and one recursive for `eval/`):

```bash
python -m training_data_prep.stage4_upload_hf \
    --repo-id <user>/gift-pretrain-small \
    --bundle-dir /path/to/out/final \
    --path-in-repo small_v1 \
    --create-repo

python -m training_data_prep.stage4_upload_hf \
    --repo-id <user>/gift-pretrain-small \
    --bundle-dir /path/to/gift-eval-data \
    --path-in-repo eval \
    --recursive
```