Search Results for author:
Found 16 papers, 7 papers with code
SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversion
We introduce SmolDocling, an ultra-compact vision-language model targeting end-to-end document conversion.
Docling: An Efficient Open-Source Toolkit for AI-driven Document Conversion
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
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.
Data-Prep-Kit: getting your data ready for LLM application development
We believe DPK is a valuable contribution to the AI community to easily prepare data to enhance the performance of their LLM models or to fine-tune models with Retrieval-Augmented Generation (RAG).
Docling Technical Report
This technical report introduces Docling, an easy to use, self-contained, MIT-licensed open-source package for PDF document conversion.
Statements: Universal Information Extraction from Tables with Large Language Models for ESG KPIs
We demonstrate the advantages of statements by applying our model to over 2700 tables from ESG reports.
Ranked #1 on
Information Extraction
on SemTabNet
INDUS: Effective and Efficient Language Models for Scientific Applications
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.
ICDAR 2023 Competition on Robust Layout Segmentation in Corporate Documents
The results demonstrate substantial progress towards achieving robust and highly generalizing methods for document layout understanding.
FETA: Towards Specializing Foundation Models for Expert Task Applications
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.
Ranked #1 on
Image-to-Text Retrieval
on FETA Car-Manuals
DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis
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.
Delivering Document Conversion as a Cloud Service with High Throughput and Responsiveness
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.
Robust PDF Document Conversion Using Recurrent Neural Networks
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.
An Information Extraction and Knowledge Graph Platform for Accelerating Biochemical Discoveries
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
In this paper, we present a modular, cloud-based platform to ingest documents at scale.
Corpus Conversion Service: A machine learning platform to ingest documents at scale [Poster abstract]
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.
