Datasets:
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license: apache-2.0
language:
- en
- multilingual
task_categories:
- token-classification
tags:
- ner
- social-media
- username-extraction
- handle-extraction
- twitter
- bios
- gliner
pretty_name: HandleAtlas Benchmark
size_categories:
- n<1K
configs:
- config_name: default
data_files:
- split: test
path: test.jsonl
---
# HandleAtlas Benchmark
Hand-labeled NER **evaluation set** for extracting social-media handles from
Twitter / X bios. These are the **exact 100 records** (seed = 123) used to
compute the benchmark numbers in the [LumeData/HandleAtlas-166m](https://huggingface.co/LumeData/HandleAtlas-166m)
and [LumeData/HandleAtlas-166m-CPU](https://huggingface.co/LumeData/HandleAtlas-166m-CPU)
model cards.
## Schema
Each record:
```json
{
"id": 2,
"text": "🍑 Ig | pea_arunya",
"entities": [
{"start": 7, "end": 17, "label": "instagram_username"}
]
}
```
- `text` — the raw bio (UTF-8, may contain emojis, multi-language text, line breaks).
- `entities` — list of NER spans. `start` and `end` are **Python character indices**
(code points, not UTF-16 code units) into `text`. `end` is exclusive — `text[start:end]`
gives the surface form of the entity.
- Multiple labels on the same span are encoded as separate entries with identical
`start`/`end` (e.g. one handle written both as "ig + sc" gets two entries).
## Labels
- `instagram_username`
- `snapchat_username`
- `youtube_username`
- `twitch_username`
- `tiktok_username`
- `discord_username`
- `x_username`
- `cashapp_username`
- `onlyfans_username`
- `tumblr_username`
- `github_username`
- `kofi_username`
- `patreon_username`
- `roblox_username`
- `generic_username`
`generic_username` is a fallback for handle-shaped strings without a clear
platform indicator (e.g. `@somebody on twitter` where the user is the writer's
non-Twitter alt).
## Size
| split | records | spans |
|-------|--------:|------:|
| test | 100 | 137 |
## Reproducing the benchmark numbers
Reference scores on this split:
| model | span-only F1 | span+label F1 |
|--------------------------------|-------------:|--------------:|
| HandleAtlas-166m / -CPU | 0.955 | 0.887 |
| GLiNER-small v2.1 (zero-shot) | 0.061 | 0.031 |
| OpenAI Privacy Filter (1.5B) | 0.402 | n/a (different labels) |
## Usage
```python
from datasets import load_dataset
ds = load_dataset("LumeData/HandleAtlas-benchmark", split="test")
print(ds[0])
# {"id": ..., "text": "🍑 Ig | pea_arunya",
# "entities": [{"start": 7, "end": 17, "label": "instagram_username"}]}
```
## Provenance
Bios were sampled from a public Twitter/X user-description CSV and labeled
by hand using a single annotator. Each bio was either fully labeled
(at least one platform handle present) or skipped. Skipped records are
**not** included — every record in this dataset has at least one entity.
## Limitations & ethics
- All bios are public Twitter/X profile descriptions but contain handles
that point to real accounts. Treat the dataset accordingly when training
downstream models.
- Single-annotator dataset; no inter-annotator agreement was measured.
- Skewed toward the most common platforms (instagram, snapchat). Rare-label
performance should be validated against your own data before deploying.
|