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feat: Use new TableFormer model weights and default to accurate model version (#1100)

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* feat: New tableformer model weights [WIP]

Signed-off-by: Christoph Auer <60343111+cau-git@users.noreply.github.com>

* Updated TF version

Signed-off-by: Maksym Lysak <mly@zurich.ibm.com>

* Updated tests, after merging with Main, Switched to Accurate TF model by default

Signed-off-by: Maksym Lysak <mly@zurich.ibm.com>

---------

Signed-off-by: Christoph Auer <60343111+cau-git@users.noreply.github.com>
Signed-off-by: Maksym Lysak <mly@zurich.ibm.com>
Co-authored-by: Maksym Lysak <mly@zurich.ibm.com>
This commit is contained in:
Christoph Auer
2025-03-11 10:53:49 +01:00
committed by GitHub
parent 5e30381c0d
commit eb97357b05
43 changed files with 213 additions and 229 deletions
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tests/data/groundtruth/docling_v1/2203.01017v2.md
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@@ -25,12 +25,12 @@ The occurrence of tables in documents is ubiquitous. They often summarise quanti
Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: 'PMC2944238 004 02'.
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Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document.
@@ -241,7 +241,7 @@ Text is aligned to match original for ease of viewing
| 第 17 回人工知能学会全国大会 (2003) | 208 | 5 | 203 | 152 | 244 |
| 自然言語処理研究会第 146 〜 155 回 | 98 | 2 | 96 | 150 | 232 |
| WWW から収集した論文 | 107 | 73 | 34 | 147 | 96 |
| | 945 | 294 | 651 | 1122 | 955 |
| 計 | 945 | 294 | 651 | 1122 | 955 |
| | Shares (in millions) | Shares (in millions) | Weighted Average Grant Date Fair Value | Weighted Average Grant Date Fair Value |
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