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feat: add figure in markdown (#98)

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* feat: add figures in markdown

Signed-off-by: Michele Dolfi <dol@zurich.ibm.com>

* update to new docling-core and update test results with figures

Signed-off-by: Michele Dolfi <dol@zurich.ibm.com>

* update with improved docling-core

Signed-off-by: Michele Dolfi <dol@zurich.ibm.com>

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Signed-off-by: Michele Dolfi <dol@zurich.ibm.com>
This commit is contained in:
Michele Dolfi
2024-09-24 17:28:23 +02:00
committed by GitHub
parent 001d214a13
commit 6a03c208ec
9 changed files with 284 additions and 58 deletions
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tests/data/2305.03393v1-pg9.md
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@@ -5,7 +5,6 @@ order to compute the TED score. Inference timing results for all experiments wer
We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML.
Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.
| # | # | Language | TEDs | TEDs | TEDs | mAP | Inference |
|------------|------------|------------|-------------|-------------------|-------------|-------------|-------------|
| enc-layers | dec-layers | Language | simple | complex | all | (0.75) | time (secs) |