Search Results for author: Michele Dolfi

Found 16 papers, 7 papers with code

Docling: An Efficient Open-Source Toolkit for AI-driven Document Conversion

no code implementations27 Jan 2025 Nikolaos Livathinos, Christoph Auer, Maksym Lysak, Ahmed Nassar, Michele Dolfi, Panos Vagenas, Cesar Berrospi Ramis, Matteo Omenetti, Kasper Dinkla, Yusik Kim, Shubham Gupta, Rafael Teixeira de Lima, Valery Weber, Lucas Morin, Ingmar Meijer, Viktor Kuropiatnyk, Peter W. J. Staar

We introduce Docling, an easy-to-use, self-contained, MIT-licensed, open-source toolkit for document conversion, that can parse several types of popular document formats into a unified, richly structured representation.

Know Your RAG: Dataset Taxonomy and Generation Strategies for Evaluating RAG Systems

no code implementations29 Nov 2024 Rafael Teixeira de Lima, Shubham Gupta, Cesar Berrospi, Lokesh Mishra, Michele Dolfi, Peter Staar, Panagiotis Vagenas

In this paper, we show that using public question and answer (Q&A) datasets to assess retrieval performance can lead to non-optimal systems design, and that common tools for RAG dataset generation can lead to unbalanced data.

Dataset Generation RAG +2

INDUS: Effective and Efficient Language Models for Scientific Applications

no code implementations17 May 2024 Bishwaranjan Bhattacharjee, Aashka Trivedi, Masayasu Muraoka, Muthukumaran Ramasubramanian, Takuma Udagawa, Iksha Gurung, Nishan Pantha, Rong Zhang, Bharath Dandala, Rahul Ramachandran, Manil Maskey, Kaylin Bugbee, Mike Little, Elizabeth Fancher, Irina Gerasimov, Armin Mehrabian, Lauren Sanders, Sylvain Costes, Sergi Blanco-Cuaresma, Kelly Lockhart, Thomas Allen, Felix Grezes, Megan Ansdell, Alberto Accomazzi, Yousef El-Kurdi, Davis Wertheimer, Birgit Pfitzmann, Cesar Berrospi Ramis, Michele Dolfi, Rafael Teixeira de Lima, Panagiotis Vagenas, S. Karthik Mukkavilli, Peter Staar, Sanaz Vahidinia, Ryan McGranaghan, Tsendgar Lee

The suite of models include: (1) an encoder model trained using domain-specific vocabulary and corpora to address NLP tasks, (2) a contrastive-learning based text embedding model trained using a diverse set of datasets to address information retrieval tasks and (3) smaller versions of these models created using knowledge distillation for applications which have latency or resource constraints.

Contrastive Learning Information Retrieval +4

ICDAR 2023 Competition on Robust Layout Segmentation in Corporate Documents

no code implementations24 May 2023 Christoph Auer, Ahmed Nassar, Maksym Lysak, Michele Dolfi, Nikolaos Livathinos, Peter Staar

The results demonstrate substantial progress towards achieving robust and highly generalizing methods for document layout understanding.

Data Augmentation

FETA: Towards Specializing Foundation Models for Expert Task Applications

1 code implementation8 Sep 2022 Amit Alfassy, Assaf Arbelle, Oshri Halimi, Sivan Harary, Roei Herzig, Eli Schwartz, Rameswar Panda, Michele Dolfi, Christoph Auer, Kate Saenko, PeterW. J. Staar, Rogerio Feris, Leonid Karlinsky

However, as we show in this paper, FMs still have poor out-of-the-box performance on expert tasks (e. g. retrieval of car manuals technical illustrations from language queries), data for which is either unseen or belonging to a long-tail part of the data distribution of the huge datasets used for FM pre-training.

Domain Generalization Image Retrieval +7

DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis

3 code implementations2 Jun 2022 Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S Nassar, Peter W J Staar

Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNet-trained models are more robust and thus the preferred choice for general-purpose document-layout analysis.

Document Layout Analysis Object Detection

Delivering Document Conversion as a Cloud Service with High Throughput and Responsiveness

1 code implementation1 Jun 2022 Christoph Auer, Michele Dolfi, André Carvalho, Cesar Berrospi Ramis, Peter W. J. Staar

In this paper, we focus on the case of document conversion to illustrate the particular challenges of scaling a complex data processing pipeline with a strong reliance on machine-learning methods on cloud infrastructure.

document understanding Optical Character Recognition +1

Robust PDF Document Conversion Using Recurrent Neural Networks

no code implementations18 Feb 2021 Nikolaos Livathinos, Cesar Berrospi, Maksym Lysak, Viktor Kuropiatnyk, Ahmed Nassar, Andre Carvalho, Michele Dolfi, Christoph Auer, Kasper Dinkla, Peter Staar

In this paper, we present a novel approach to document structure recovery in PDF using recurrent neural networks to process the low-level PDF data representation directly, instead of relying on a visual re-interpretation of the rendered PDF page, as has been proposed in previous literature.

Articles Feature Engineering +2

An Information Extraction and Knowledge Graph Platform for Accelerating Biochemical Discoveries

no code implementations19 Jul 2019 Matteo Manica, Christoph Auer, Valery Weber, Federico Zipoli, Michele Dolfi, Peter Staar, Teodoro Laino, Costas Bekas, Akihiro Fujita, Hiroki Toda, Shuichi Hirose, Yasumitsu Orii

Information extraction and data mining in biochemical literature is a daunting task that demands resource-intensive computation and appropriate means to scale knowledge ingestion.

Corpus Conversion Service: A machine learning platform to ingest documents at scale [Poster abstract]

no code implementations15 May 2018 Peter W J Staar, Michele Dolfi, Christoph Auer, Costas Bekas

We present a platform to ingest documents at scale which is powered by Machine Learning techniques and allows the user to train custom models on document collections.

Articles BIG-bench Machine Learning

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