PL-MTEB: Polish Massive Text Embedding Benchmark

• URL: http://arxiv.org/abs/2405.10138v1
• Date: Thu, 16 May 2024 14:33:39 GMT
• Title: PL-MTEB: Polish Massive Text Embedding Benchmark
• Authors: Rafał Poświata, Sławomir Dadas, Michał Perełkiewicz,
• Abstract summary: Polish Massive Text Embedding Benchmark (PL-MTEB) is a benchmark for text embeddings in Polish. PL-MTEB consists of 28 diverse NLP tasks from 5 task types.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we introduce the Polish Massive Text Embedding Benchmark (PL-MTEB), a comprehensive benchmark for text embeddings in Polish. The PL-MTEB consists of 28 diverse NLP tasks from 5 task types. We adapted the tasks based on previously used datasets by the Polish NLP community. In addition, we created a new PLSC (Polish Library of Science Corpus) dataset consisting of titles and abstracts of scientific publications in Polish, which was used as the basis for two novel clustering tasks. We evaluated 15 publicly available models for text embedding, including Polish and multilingual ones, and collected detailed results for individual tasks and aggregated results for each task type and the entire benchmark. PL-MTEB comes with open-source code at https://github.com/rafalposwiata/pl-mteb.

Related papers

• The Scandinavian Embedding Benchmarks: Comprehensive Assessment of Multilingual and Monolingual Text Embedding [8.097049661773465]
  Scandinavian Embedding Benchmark (SEB) is a framework that enables text embedding evaluation for Scandinavian languages. Building on SEB, we evaluate more than 26 models, uncovering significant performance disparities between public and commercial solutions. We open-source SEB and integrate it with MTEB, thus bridging the text embedding evaluation gap for Scandinavian languages.
  arXiv  Detail & Related papers  (2024-06-04T15:11:27Z)
• A Novel Cartography-Based Curriculum Learning Method Applied on RoNLI: The First Romanian Natural Language Inference Corpus [71.77214818319054]
  Natural language inference is a proxy for natural language understanding. There is no publicly available NLI corpus for the Romanian language. We introduce the first Romanian NLI corpus (RoNLI) comprising 58K training sentence pairs.
  arXiv  Detail & Related papers  (2024-05-20T08:41:15Z)
• PIRB: A Comprehensive Benchmark of Polish Dense and Hybrid Text Retrieval Methods [0.552480439325792]
  We present Polish Information Retrieval Benchmark (PIRB), a comprehensive evaluation framework encompassing 41 text information retrieval tasks for Polish. The benchmark incorporates existing datasets as well as 10 new, previously unpublished datasets covering diverse topics such as medicine, law, business, physics, and linguistics. We conduct an extensive evaluation of over 20 dense and sparse retrieval models, including the baseline models trained by us.
  arXiv  Detail & Related papers  (2024-02-20T19:53:36Z)
• Improving Text Embeddings with Large Language Models [59.930513259982725]
  We introduce a novel and simple method for obtaining high-quality text embeddings using only synthetic data and less than 1k training steps. We leverage proprietary LLMs to generate diverse synthetic data for hundreds of thousands of text embedding tasks across 93 languages. Experiments demonstrate that our method achieves strong performance on highly competitive text embedding benchmarks without using any labeled data.
  arXiv  Detail & Related papers  (2023-12-31T02:13:18Z)
• This is the way: designing and compiling LEPISZCZE, a comprehensive NLP benchmark for Polish [5.8090623549313944]
  We introduce LEPISZCZE, a new, comprehensive benchmark for Polish NLP. We use five datasets from the Polish benchmark and add eight novel datasets. We provide insights and experiences learned while creating the benchmark for Polish as the blueprint to design similar benchmarks for other low-resourced languages.
  arXiv  Detail & Related papers  (2022-11-23T16:51:09Z)
• Evaluation of Transfer Learning for Polish with a Text-to-Text Model [54.81823151748415]
  We introduce a new benchmark for assessing the quality of text-to-text models for Polish. The benchmark consists of diverse tasks and datasets: KLEJ benchmark adapted for text-to-text, en-pl translation, summarization, and question answering. We present plT5 - a general-purpose text-to-text model for Polish that can be fine-tuned on various Natural Language Processing (NLP) tasks with a single training objective.
  arXiv  Detail & Related papers  (2022-05-18T09:17:14Z)
• Benchmarking Multimodal AutoML for Tabular Data with Text Fields [83.43249184357053]
  We assemble 18 multimodal data tables that each contain some text fields. Our benchmark enables researchers to evaluate their own methods for supervised learning with numeric, categorical, and text features.
  arXiv  Detail & Related papers  (2021-11-04T09:29:16Z)
• Unsupervised Bitext Mining and Translation via Self-trained Contextual Embeddings [51.47607125262885]
  We describe an unsupervised method to create pseudo-parallel corpora for machine translation (MT) from unaligned text. We use multilingual BERT to create source and target sentence embeddings for nearest-neighbor search and adapt the model via self-training. We validate our technique by extracting parallel sentence pairs on the BUCC 2017 bitext mining task and observe up to a 24.5 point increase (absolute) in F1 scores over previous unsupervised methods.
  arXiv  Detail & Related papers  (2020-10-15T14:04:03Z)
• KLEJ: Comprehensive Benchmark for Polish Language Understanding [4.702729080310267]
  We introduce a comprehensive multi-task benchmark for the Polish language understanding, accompanied by an online leaderboard. We also release HerBERT, a Transformer-based model trained specifically for the Polish language, which has the best average performance and obtains the best results for three out of nine tasks.
  arXiv  Detail & Related papers  (2020-05-01T21:55:40Z)
• XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation [100.09099800591822]
  XGLUE is a new benchmark dataset that can be used to train large-scale cross-lingual pre-trained models. XGLUE provides 11 diversified tasks that cover both natural language understanding and generation scenarios.
  arXiv  Detail & Related papers  (2020-04-03T07:03:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.