Datasets:
File size: 3,314 Bytes
4de12e0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 | ## HandleAtlas Benchmark — evaluation spec
##
## Self-contained recipe for reproducing the numbers on the dataset card.
## Spans are character offsets into `text` (Python code points, end exclusive).
## Scoring is label-agnostic span F1 at IoU >= 0.5, plus exact span+label F1.
name: handleatlas-benchmark
version: 1
dataset:
repo_id: LumeData/HandleAtlas-benchmark
config: default
split: test
loader: |
from datasets import load_dataset
ds = load_dataset("LumeData/HandleAtlas-benchmark", split="test")
task:
type: token-classification
subtype: span-extraction
input_field: text
target_field: entities # list[{start, end, label}]
offsets: python-char # not UTF-16, not BPE tokens
end_exclusive: true
multi_label_spans: true # identical (start,end) with different labels are allowed
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
metrics:
- id: span_only_f1
description: Label-agnostic span detection. A prediction matches a gold span
when their character-offset IoU >= iou_threshold. Each gold span is matched
at most once (greedy by best IoU).
iou_threshold: 0.5
primary: true
aggregate: micro
reports: [precision, recall, f1, tp, fp, fn]
- id: span_label_f1
description: Exact match on (start, end, label). Stricter than span_only_f1
and only fair to compare across models that share this label taxonomy.
aggregate: micro
reports: [precision, recall, f1, tp, fp, fn]
- id: latency_ms
description: Wall-clock ms per record, single batch=1 inference. CPU only.
reports: [mean, p50, p95]
hardware: MacBook Pro M5 Pro
threads: 8
reference_implementation:
language: python
file: data/benchmark.py
scoring_fns:
- score_span_only
- score_span_label
inference_protocol:
threshold: 0.5
per_label_threshold_overrides:
generic_username: 0.65
drop_predictions_with_label:
- discord_invite # excluded from this taxonomy
decoding: greedy
batch_size: 1
baselines:
- model: LumeData/HandleAtlas-166m
span_only_f1: 0.955
span_label_f1: 0.887
precision: 0.914
recall: 1.000
latency_ms_mean: 37.1
latency_ms_p95: 52.2
runtime: pytorch-float
- model: LumeData/HandleAtlas-166m-CPU
span_only_f1: 0.955
span_label_f1: 0.887
latency_ms_mean: 13.3
latency_ms_p95: 22.3
runtime: onnx-int8
- model: urchade/gliner_small-v2.1
span_only_f1: 0.061
span_label_f1: 0.031
precision: 1.000
recall: 0.031
latency_ms_mean: 35.0
latency_ms_p95: 49.9
runtime: pytorch-float
note: zero-shot, same label list at threshold 0.5
- model: openai/privacy-filter
span_only_f1: 0.402
precision: 0.305
recall: 0.591
latency_ms_mean: 113.6
latency_ms_p95: 288.1
runtime: pytorch-float
note: different label taxonomy — span+label F1 not comparable
splits:
test:
n_records: 100
n_spans: 127
seed: 123
notes: shuffle(annotations) -> take first 100; reproducible with SEED=123
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