Multilingual E5 Text Embeddings: A Technical Report

• URL: http://arxiv.org/abs/2402.05672v1
• Date: Thu, 8 Feb 2024 13:47:50 GMT
• Title: Multilingual E5 Text Embeddings: A Technical Report
• Authors: Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei
• Abstract summary: Three embedding models of different sizes are provided, offering a balance between the inference efficiency and embedding quality. We introduce a new instruction-tuned embedding model, whose performance is on par with state-of-the-art, English-only models of similar sizes.
• Score: 63.503320030117145
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This technical report presents the training methodology and evaluation results of the open-source multilingual E5 text embedding models, released in mid-2023. Three embedding models of different sizes (small / base / large) are provided, offering a balance between the inference efficiency and embedding quality. The training procedure adheres to the English E5 model recipe, involving contrastive pre-training on 1 billion multilingual text pairs, followed by fine-tuning on a combination of labeled datasets. Additionally, we introduce a new instruction-tuned embedding model, whose performance is on par with state-of-the-art, English-only models of similar sizes. Information regarding the model release can be found at https://github.com/microsoft/unilm/tree/master/e5 .

Related papers

• CroissantLLM: A Truly Bilingual French-English Language Model [42.03897426049679]
  We introduce CroissantLLM, a 1.3B language model pretrained on a set of 3T English and French tokens. We pioneer the approach of training an intrinsically bilingual model with a 1:1 English-to-French pretraining data ratio. To assess performance outside of English, we craft a novel benchmark, FrenchBench.
  arXiv  Detail & Related papers  (2024-02-01T17:17:55Z)
• A Text-to-Text Model for Multilingual Offensive Language Identification [19.23565690468299]
  This study presents the first pre-trained model with encoder-decoder architecture for offensive language identification with text-to-text transformers (T5) Our pre-trained T5 model outperforms other transformer-based models fine-tuned for offensive language detection, such as fBERT and HateBERT, in multiple English benchmarks. Following a similar approach, we also train the first multilingual pre-trained model for offensive language identification using mT5.
  arXiv  Detail & Related papers  (2023-12-06T09:37:27Z)
• Accidental Learners: Spoken Language Identification in Multilingual Self-Supervised Models [11.439430077017635]
  We find that pre-trained speech models optimally encode language discriminatory information in lower layers. We demonstrate that the embeddings obtained from these layers are significantly robust to classify unseen languages. We open-source the model through the NVIDIA NeMo toolkit.
  arXiv  Detail & Related papers  (2022-11-09T18:53:59Z)
• 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)
• Unifying Language Learning Paradigms [96.35981503087567]
  We present a unified framework for pre-training models that are universally effective across datasets and setups. We show how different pre-training objectives can be cast as one another and how interpolating between different objectives can be effective. Our model also achieve strong results at in-context learning, outperforming 175B GPT-3 on zero-shot SuperGLUE and tripling the performance of T5-XXL on one-shot summarization.
  arXiv  Detail & Related papers  (2022-05-10T19:32:20Z)
• Few-shot learning through contextual data augmentation [74.20290390065475]
  Machine translation models need to adapt to new data to maintain their performance over time. We show that adaptation on the scale of one to five examples is possible. Our model reports better accuracy scores than a reference system trained with on average 313 parallel examples.
  arXiv  Detail & Related papers  (2021-03-31T09:05:43Z)
• Introducing various Semantic Models for Amharic: Experimentation and Evaluation with multiple Tasks and Datasets [19.855120632909124]
  We introduce different semantic models for Amharic. Models are build using word2Vec embeddings, distributional thesaurus (DT), contextual embeddings, and DT embeddings. We find that newly trained models perform better than pre-trained multilingual models.
  arXiv  Detail & Related papers  (2020-11-02T17:48:25Z)
• mT5: A massively multilingual pre-trained text-to-text transformer [60.0210636815514]
  "Text-to-Text Transfer Transformer" (T5) leveraged a unified text-to-text format and scale to attain state-of-the-art results on English-language NLP tasks. We introduce mT5, a multilingual variant of T5 that was pre-trained on a new Common Crawl-based dataset covering 101 languages.
  arXiv  Detail & Related papers  (2020-10-22T17:58:14Z)
• Beyond English-Centric Multilingual Machine Translation [74.21727842163068]
  We create a true Many-to-Many multilingual translation model that can translate directly between any pair of 100 languages. We build and open source a training dataset that covers thousands of language directions with supervised data, created through large-scale mining. Our focus on non-English-Centric models brings gains of more than 10 BLEU when directly translating between non-English directions while performing competitively to the best single systems of WMT.
  arXiv  Detail & Related papers  (2020-10-21T17:01:23Z)
• The Tatoeba Translation Challenge -- Realistic Data Sets for Low Resource and Multilingual MT [0.0]
  This paper describes the development of a new benchmark for machine translation that provides training and test data for thousands of language pairs. The main goal is to trigger the development of open translation tools and models with a much broader coverage of the World's languages.
  arXiv  Detail & Related papers  (2020-10-13T13:12:21Z)

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.