Datasets:

LumeData
/

HandleAtlas-benchmark

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 and LumeData/HandleAtlas-166m-CPU model cards.

Schema

Each record:

{
  "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

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.