---
license: cc-by-sa-4.0
language:
- en
pretty_name: SN22 sandbox embed texts
size_categories:
- 10K<n<100K
tags:
- embeddings
- bittensor
- desearch
configs:
- config_name: default
  data_files: data/*.parquet
---

# SN22 sandbox embed texts

Texts and reference embeddings for testing a [Desearch Subnet 22](https://github.com/Desearch-ai/subnet-22)
embed miner locally, before it takes tasks on mainnet.

Run `python -m sandbox embed` in the subnet-22 repository. It downloads these files, queues the texts
as embed tasks in a local task API, and runs a local validator that checks your miner's embeddings
against the reference vectors here, the way mainnet validators check embed tasks. Point your miner at the
local API and see each verdict, the texts that did not match and by how much, and how fast your GPU
gets through a task. The [guide](https://github.com/Desearch-ai/subnet-22/blob/develop/docs/test-your-miner.md)
has the steps.

## Dataset details

| | |
|---|---|
| Texts | 14,226: 3,000 page texts and 11,226 passages |
| Pages | 3,000 English Wikipedia articles, sampled at random |
| Characters | 15.6 million |
| Reference model | `desearch-4b-v1`: [desearch/Desearch-Embedding-4B](https://huggingface.co/desearch/Desearch-Embedding-4B) at revision `12e5c6910d49dd5534e8a77a6fa7d8a057bd534a` |
| Reference vectors | 2,560 dimensions, float32, L2-normalized |
| Files | `data/texts-NNNNN.parquet`, rows grouped by page; `manifest.json` lists them |
| Source | [wikimedia/wikipedia](https://huggingface.co/datasets/wikimedia/wikipedia), snapshot `20231101.en` |
| License | [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) |

## Columns

| Column | Type | |
|---|---|---|
| `text_id` | string | the text's id within its page: `<page_key>#page` or `<page_key>#p<n>` |
| `page_key` | string | the page the text was taken from |
| `url` | string | the article's Wikipedia URL |
| `content_sha1` | string | the version of the page the text was taken from |
| `kind` | string | `page` for a page's own text, `passage` for a passage of a long article |
| `index` | int32 | 0 for the page text, 1, 2, … for its passages |
| `text` | large string | exactly what is embedded |
| `reference` | binary | the reference vector: 2,560 little-endian float32 values |

The first seven columns are the columns of a mainnet embed task's input, so the sandbox serves these rows to
miners unchanged.

## How the texts were made

Each article becomes texts the same way a crawled page does on mainnet, with the same code:

- **Page text:** the title, then the article's text, cut at 10,000 characters.
- **Passages:** an article longer than 2,000 characters also gives up to 16 passages of 600 to 1,500
  characters of consecutive paragraphs, split at its section headings, each starting with the article's title
  and the section's heading.

Random articles give the mix of short and long pages a real task has.

## How the reference vectors were computed

The reference vectors come from the exact model in full precision: vLLM 0.31.0 with `--runner pooling
--dtype float32 --no-enable-chunked-prefill --max-num-batched-tokens 16384`, attention kernels in float32
too (`TRITON_F32_DEFAULT=ieee`). Each text was sent as is, with no prompt, and cut at 2,048 tokens
(`truncate_prompt_tokens: 2048`).

The model served as miners run it, in float16 with the documented command, matches these vectors with a
cosine similarity of at least 0.99998 on a random sample of 500 texts. Mainnet's check asks for 0.999.

## Loading

```python
import numpy as np
from datasets import load_dataset

rows = load_dataset("desearch/sn22-sandbox-embed", split="train")
row = rows[0]
reference = np.frombuffer(row["reference"], dtype="<f4")  # shape (2560,)
print(row["text_id"], row["kind"], len(row["text"]), reference.shape)
```

## Source and license

The texts are adapted from Wikipedia articles by Wikipedia contributors, through the
[wikimedia/wikipedia](https://huggingface.co/datasets/wikimedia/wikipedia) dataset (snapshot `20231101.en`).
Like Wikipedia, this dataset is licensed under
[CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/).