Hugging Face's logo

Spaces:
nutrientdocs
/
doc-split-leaderboard
Running

App Files Community
doc-split-leaderboard
File size: 14,389 Bytes
0c5549b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>Page Stream Segmentation β€” Unified Leaderboard</title>
<style>
  :root{
    --bg:#f6f8fb; --surface:#ffffff; --surface-2:#f0f4f8;
    --text:#111820; --muted:#5c6773; --faint:#8a96a2; --border:#e2e8ef;
    --accent:#1d5fa8; --accent-soft:#e9eefb; --accent-line:#c8d5f6;
    --good:#0f7a58; --good-bg:#e2f3ec; --ok:#6b7280; --ok-bg:#eef1f4;
    --warn:#9a6a12; --warn-bg:#f7edda; --poor:#a83a3a; --poor-bg:#f6e3e3;
    --shadow:0 1px 2px rgba(16,32,64,.05),0 4px 16px rgba(16,32,64,.05);
    --mono:ui-monospace,"SF Mono",Menlo,Consolas,monospace;
    --sans:system-ui,-apple-system,"Segoe UI",Roboto,sans-serif;
  }
  @media (prefers-color-scheme:dark){:root{
    --bg:#0c1016; --surface:#141a22; --surface-2:#1a222c;
    --text:#e6edf4; --muted:#94a1af; --faint:#6a7684; --border:#232c37;
    --accent:#6aa9e6; --accent-soft:#182238; --accent-line:#2c3d63;
    --good:#3bbd8e; --good-bg:#122a22; --ok:#9aa5b1; --ok-bg:#1b232d;
    --warn:#d6a24a; --warn-bg:#2b2416; --poor:#e07373; --poor-bg:#2c1a1a;
    --shadow:0 1px 2px rgba(0,0,0,.3),0 6px 22px rgba(0,0,0,.35);
  }}
  :root[data-theme="light"]{
    --bg:#f6f8fb; --surface:#ffffff; --surface-2:#f0f4f8; --text:#111820; --muted:#5c6773; --faint:#8a96a2; --border:#e2e8ef;
    --accent:#1d5fa8; --accent-soft:#e9eefb; --accent-line:#c8d5f6;
    --good:#0f7a58; --good-bg:#e2f3ec; --ok:#6b7280; --ok-bg:#eef1f4; --warn:#9a6a12; --warn-bg:#f7edda; --poor:#a83a3a; --poor-bg:#f6e3e3;
    --shadow:0 1px 2px rgba(16,32,64,.05),0 4px 16px rgba(16,32,64,.05);
  }
  :root[data-theme="dark"]{
    --bg:#0c1016; --surface:#141a22; --surface-2:#1a222c; --text:#e6edf4; --muted:#94a1af; --faint:#6a7684; --border:#232c37;
    --accent:#6aa9e6; --accent-soft:#182238; --accent-line:#2c3d63;
    --good:#3bbd8e; --good-bg:#122a22; --ok:#9aa5b1; --ok-bg:#1b232d; --warn:#d6a24a; --warn-bg:#2b2416; --poor:#e07373; --poor-bg:#2c1a1a;
    --shadow:0 1px 2px rgba(0,0,0,.3),0 6px 22px rgba(0,0,0,.35);
  }
  *{box-sizing:border-box}
  body{margin:0;background:var(--bg);color:var(--text);font-family:var(--sans);line-height:1.5;
    -webkit-font-smoothing:antialiased;font-size:15px}
  .wrap{max-width:1120px;margin:0 auto;padding:48px 24px 72px}
  header .eyebrow{font-size:12px;letter-spacing:.14em;text-transform:uppercase;color:var(--accent);font-weight:600}
  h1{font-size:clamp(26px,4vw,38px);line-height:1.12;margin:.35em 0 .25em;letter-spacing:-.02em;text-wrap:balance;font-weight:700}
  .lede{color:var(--muted);max-width:64ch;margin:0}
  .meta{margin-top:14px;font-size:12.5px;color:var(--faint);font-family:var(--mono)}

  .findings{display:grid;grid-template-columns:repeat(auto-fit,minmax(220px,1fr));gap:14px;margin:32px 0 8px}
  .finding{background:var(--surface);border:1px solid var(--border);border-radius:10px;padding:16px 18px;box-shadow:var(--shadow)}
  .finding b{display:block;font-size:22px;letter-spacing:-.01em;font-family:var(--mono);font-variant-numeric:tabular-nums}
  .finding span{color:var(--muted);font-size:13px}
  .finding .k{color:var(--accent);font-weight:600}

  h2{font-size:13px;letter-spacing:.12em;text-transform:uppercase;color:var(--muted);font-weight:600;margin:40px 0 12px}
  .tablecard{background:var(--surface);border:1px solid var(--border);border-radius:12px;box-shadow:var(--shadow);overflow:hidden}
  .scroll{overflow-x:auto}
  table{border-collapse:collapse;width:100%;min-width:820px}
  thead th{position:sticky;top:0;background:var(--surface);z-index:1}
  th,td{text-align:center;padding:10px 12px;border-bottom:1px solid var(--border);white-space:nowrap}
  th.model,td.model{text-align:left;white-space:normal;min-width:230px;position:sticky;left:0;background:var(--surface);z-index:2}
  thead th{font-size:11.5px;letter-spacing:.04em;color:var(--muted);font-weight:600;text-transform:uppercase;border-bottom:1.5px solid var(--border)}
  thead th .sub{display:block;font-size:10px;color:var(--faint);text-transform:none;letter-spacing:0;font-weight:400}
  .grouprow td{background:var(--surface-2);font-size:11px;letter-spacing:.1em;text-transform:uppercase;color:var(--faint);font-weight:600;text-align:left;padding:7px 12px}
  td .f1{font-family:var(--mono);font-variant-numeric:tabular-nums;font-weight:600;font-size:14.5px}
  td .kap{display:block;font-family:var(--mono);font-size:10.5px;color:var(--faint);margin-top:1px}
  td.na{color:var(--faint)}
  .modelname{font-weight:600;font-size:14px}
  .modelsub{color:var(--muted);font-size:12px;margin-top:1px}
  .chip{display:inline-block;font-size:10px;font-weight:600;letter-spacing:.03em;padding:2px 7px;border-radius:20px;margin-top:5px;text-transform:uppercase}
  .chip.flag{background:var(--accent-soft);color:var(--accent);border:1px solid var(--accent-line)}
  .chip.pub{background:var(--good-bg);color:var(--good)}
  .chip.int{background:var(--ok-bg);color:var(--ok)}
  .chip.cloud{background:var(--warn-bg);color:var(--warn)}
  .chip.pubd{background:var(--ok-bg);color:var(--ok)}
  tr.flagrow td.model{box-shadow:inset 3px 0 0 var(--accent)}
  /* value heatmap (semantic, not the accent) */
  .g1{background:var(--good-bg);color:var(--good)}     /* >=.85 */
  .g2{color:var(--text)}                                /* .70-.85 */
  .g3{background:var(--warn-bg);color:var(--warn)}      /* .50-.70 */
  .g4{background:var(--poor-bg);color:var(--poor)}      /* <.50 */
  .foot{margin-top:34px;display:grid;gap:9px}
  .foot p{margin:0;color:var(--muted);font-size:12.5px;max-width:88ch;padding-left:16px;position:relative}
  .foot p::before{content:"";position:absolute;left:0;top:8px;width:6px;height:6px;border-radius:50%;background:var(--accent-line)}
  .legend{display:flex;flex-wrap:wrap;gap:14px;margin:10px 0 0;font-size:11.5px;color:var(--muted)}
  .legend span{display:inline-flex;align-items:center;gap:6px}
  .sw{width:12px;height:12px;border-radius:3px;border:1px solid var(--border)}
  a{color:var(--accent)}
</style>
</head>
<body>
<div class="wrap">
  <header>
    <div class="eyebrow">Nutrient Β· Document Intelligence</div>
    <h1>Page Stream Segmentation β€” Unified Leaderboard</h1>
    <p class="lede">One boundary metric, one harness, every contender re-measured: our flagship and open-weight
      models against cloud VLMs and the field's self-declared numbers. Cells show boundary <b>F1</b>; the small
      figure is chance-corrected <b>ΞΊ</b>.</p>
    <div class="meta">updated 2026-08-11 Β· metric: boundary page-F1 (page 0 forced) + Cohen's ΞΊ</div>
  </header>

  <section class="findings">
    <div class="finding"><b>0.891</b><span>flagship <span class="k">OpenPSS-long</span> F1 β€” vs best cloud <b style="font-size:13px">0.244</b></span></div>
    <div class="finding"><b>1 model</b><span>beats OpenPSS's <span class="k">two</span> specialists across both slices</span></div>
    <div class="finding"><b>~0.0007</b><span>USD / 1k pages (A40) β€” cloud VLMs β‰ˆ <span class="k">$0.014 / stream</span></span></div>
    <div class="finding"><b>4.5Γ—</b><span>lighter open model (<span class="k">doc-split-v1</span>) at near-flagship quality</span></div>
  </section>

  <h2>Boundary F1 Β· ΞΊ  β€”  across six evaluation cuts</h2>
  <div class="tablecard"><div class="scroll">
    <table>
      <thead><tr>
        <th class="model">Model</th>
        <th>our-200<span class="sub">easy, saturated</span></th>
        <th>OpenPSS-short<span class="sub">sparse Β· hardest</span></th>
        <th>OpenPSS-long<span class="sub">long streams</span></th>
        <th>TABME++<span class="sub">test</span></th>
        <th>Tobacco800<span class="sub">test</span></th>
        <th>val-full<span class="sub">our real-doc</span></th>
      </tr></thead>
      <tbody>
        <tr class="grouprow"><td colspan="7">Ours</td></tr>
        <tr class="flagrow">
          <td class="model"><div class="modelname">doc-split-v2</div><div class="modelsub">flagship Β· one model for short + long streams Β· ~1.0B Β· on-prem</div><span class="chip flag">Commercial</span></td>
          <td class="g1"><span class="f1">0.944</span><span class="kap">.79</span></td>
          <td class="g3"><span class="f1">0.652</span><span class="kap">.60</span></td>
          <td class="g1"><span class="f1">0.891</span><span class="kap">.86</span></td>
          <td class="g1"><span class="f1">0.943</span><span class="kap">.91</span></td>
          <td class="g1"><span class="f1">0.969</span><span class="kap">.93</span></td>
          <td class="g1"><span class="f1">0.917</span><span class="kap">.86</span></td>
        </tr>
        <tr>
          <td class="model"><div class="modelname">doc-split-v1</div><div class="modelsub">open-weight Β· ~4.5Γ— faster</div><span class="chip pub">Open-weight</span></td>
          <td class="g1"><span class="f1">0.936</span><span class="kap">.78</span></td>
          <td class="g3"><span class="f1">0.585</span><span class="kap">.53</span></td>
          <td class="g1"><span class="f1">0.859</span><span class="kap">.82</span></td>
          <td class="g2"><span class="f1">0.704</span><span class="kap">.56</span></td>
          <td class="g2"><span class="f1">0.820</span><span class="kap">.60</span></td>
          <td class="g1"><span class="f1">0.918</span><span class="kap">.86</span></td>
        </tr>

        <tr class="grouprow"><td colspan="7">Cloud VLM Β· image-only Β· single-prompt</td></tr>
        <tr>
          <td class="model"><div class="modelname">gemini-flash</div><div class="modelsub">best cloud on OpenPSS Β· ~$0.014/stream</div><span class="chip cloud">Cloud</span></td>
          <td class="g1"><span class="f1">0.917</span></td>
          <td class="g3"><span class="f1">0.598</span><span class="kap">.53</span></td>
          <td class="g4"><span class="f1">0.244</span><span class="kap">.16</span></td>
          <td class="na">β€”</td><td class="na">β€”</td><td class="na">β€”</td>
        </tr>
        <tr>
          <td class="model"><div class="modelname">gemini-pro</div><span class="chip cloud">Cloud</span></td>
          <td class="g1"><span class="f1">0.936</span></td>
          <td class="g3"><span class="f1">0.530</span><span class="kap">.45</span></td>
          <td class="g4"><span class="f1">0.196</span><span class="kap">.11</span></td>
          <td class="na">β€”</td><td class="na">β€”</td><td class="na">β€”</td>
        </tr>
        <tr>
          <td class="model"><div class="modelname">gpt-sol</div><div class="modelsub">best cloud on our-200</div><span class="chip cloud">Cloud</span></td>
          <td class="g1"><span class="f1">0.942</span></td>
          <td class="g4"><span class="f1">0.193</span><span class="kap">.17</span></td>
          <td class="g4"><span class="f1">0.025</span><span class="kap">.02</span></td>
          <td class="na">β€”</td><td class="na">β€”</td><td class="na">β€”</td>
        </tr>
        <tr>
          <td class="model"><div class="modelname">claude-opus</div><span class="chip cloud">Cloud</span></td>
          <td class="na">β€”</td>
          <td class="g4"><span class="f1">0.318</span><span class="kap">.28</span></td>
          <td class="g4"><span class="f1">0.047</span><span class="kap">.03</span></td>
          <td class="na">β€”</td><td class="na">β€”</td><td class="na">β€”</td>
        </tr>

        <tr class="grouprow"><td colspan="7">Research β€” self-declared (their metric / in-domain)</td></tr>
        <tr>
          <td class="model"><div class="modelname">OpenPSS SHORT-specialist</div><div class="modelsub">BERT-EffNet ensemble Β· one of two models</div><span class="chip pubd">Published</span></td>
          <td class="na">β€”</td>
          <td class="g2"><span class="f1">0.76</span></td>
          <td class="g4"><span class="f1">0.50</span></td>
          <td class="na">β€”</td><td class="na">β€”</td><td class="na">β€”</td>
        </tr>
        <tr>
          <td class="model"><div class="modelname">OpenPSS LONG-specialist</div><div class="modelsub">separate model Β· cross-slice drops</div><span class="chip pubd">Published</span></td>
          <td class="na">β€”</td>
          <td class="g3"><span class="f1">0.62</span></td>
          <td class="g2"><span class="f1">0.83</span></td>
          <td class="na">β€”</td><td class="na">β€”</td><td class="na">β€”</td>
        </tr>
        <tr>
          <td class="model"><div class="modelname">bert-pss (agiagoulas)</div><div class="modelsub">only released PSS specialist Β· text-only</div><span class="chip pubd">Released</span></td>
          <td class="na">β€”</td><td class="na">β€”</td><td class="na">β€”</td><td class="na">β€”</td>
          <td class="g4"><span class="f1">0.915<span style="font-size:10px">*</span></span><span class="kap">ΞΊ .00 run</span></td>
          <td class="na">β€”</td>
        </tr>
      </tbody>
    </table>
  </div></div>

  <div class="legend">
    <span><i class="sw" style="background:var(--good-bg)"></i>β‰₯ 0.85</span>
    <span><i class="sw" style="background:var(--surface)"></i>0.70–0.85</span>
    <span><i class="sw" style="background:var(--warn-bg)"></i>0.50–0.70</span>
    <span><i class="sw" style="background:var(--poor-bg)"></i>&lt; 0.50</span>
  </div>

  <div class="foot">
    <p><b>One balanced model vs two specialists.</b> No single OpenPSS model wins both slices β€” their short-specialist craters on long (0.50), their long-specialist on short (0.62). the v4 flagship does short + long with one model, and its long (0.891) tops even their long-specialist (0.83).</p>
    <p><b>Cloud VLMs can't ingest long streams.</b> Per-request image caps (Anthropic ~100 / OpenAI ~200 / Gemini ~500) force predict-none on streams over the cap, which dominates OpenPSS-long β†’ best cloud 0.244 vs flagship 0.891, at ~20Γ— the cost per page.</p>
    <p><b>* bert-pss</b> self-declares ~0.915 accuracy / 0.825 ΞΊ on Tobacco800, but collapses to a single class when actually run (ΞΊ β‰ˆ 0) β€” the only released PSS specialist is non-functional off its exact serving harness.</p>
    <p><b>Metric.</b> Ours &amp; cloud: micro boundary-F1 + ΞΊ over internal pages, identical harness. OpenPSS rows: their published per-stream page-F1 (ballpark-comparable). Tobacco incumbents report accuracy β€” compared via ΞΊ. AI-Lab-Splitter omitted: data gated, absolute F1 paywalled.</p>
  </div>
</div>
</body>
</html>