45 citations · 88 across the 9 of their papers we have counts for
9 papers
MVEB: Massive Video Embedding Benchmark
Adnan El Assadi, Roman Solomatin, Isaac Chung +13
We introduce the Massive Video Embedding Benchmark (MVEB), a 23-task benchmark for video embeddings spanning classification, zero-shot classification, clustering, pair classificati…
MMTEB: Massive Multilingual Text Embedding Benchmark
Kenneth Enevoldsen, Isaac Chung, Imene Kerboua +83
Text embeddings are typically evaluated on a limited set of tasks, which are constrained by language, domain, and task diversity. To address these limitations and provide a more co…
MTEB-French: Resources for French Sentence Embedding Evaluation and Analysis
Mathieu Ciancone, Imene Kerboua, Marion Schaeffer +1
Recently, numerous embedding models have been made available and widely used for various NLP tasks. The Massive Text Embedding Benchmark (MTEB) has primarily simplified the process…
The Importance of Future Information in Credit Card Fraud Detection
Van Bach Nguyen, Kanishka Ghosh Dastidar, Michael Granitzer +1
Fraud detection systems (FDS) mainly perform two tasks: (i) real-time detection while the payment is being processed and (ii) posterior detection to block the card retrospectively…
Transfer Learning for Credit Card Fraud Detection: A Journey from Research to Production
Wissam Siblini, Guillaume Coter, Rémy Fabry +5
The dark face of digital commerce generalization is the increase of fraud attempts. To prevent any type of attacks, state-of-the-art fraud detection systems are now embedding Machi…
Delaying Interaction Layers in Transformer-based Encoders for Efficient Open Domain Question Answering
Wissam Siblini, Mohamed Challal, Charlotte Pasqual
Open Domain Question Answering (ODQA) on a large-scale corpus of documents (e.g. Wikipedia) is a key challenge in computer science. Although transformer-based language models such…
Multilingual Question Answering from Formatted Text applied to Conversational Agents
Wissam Siblini, Charlotte Pasqual, Axel Lavielle +2
Recent advances with language models (e.g. BERT, XLNet, ...), have allowed surpassing human performance on complex NLP tasks such as Reading Comprehension. However, labeled dataset…
Master your Metrics with Calibration
Wissam Siblini, Jordan Fréry, Liyun He-Guelton +2
Machine learning models deployed in real-world applications are often evaluated with precision-based metrics such as F1-score or AUC-PR (Area Under the Curve of Precision Recall).…
VIPE: A new interactive classification framework for large sets of short texts - application to opinion mining
Wissam Siblini, Frank Meyer, Pascale Kuntz
This paper presents a new interactive opinion mining tool that helps users to classify large sets of short texts originated from Web opinion polls, technical forums or Twitter. Fro…