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Language modeling: Difference between revisions

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* [https://huggingface.co/docs/transformers/model_doc/mbart MBart]:  Multilingual Denoising Pre-training for Neural Machine Translation
* [https://huggingface.co/docs/transformers/model_doc/mbart MBart]:  Multilingual Denoising Pre-training for Neural Machine Translation
* [https://huggingface.co/docs/transformers/v4.14.1/model_doc/mt5 mT5:]  mT5: A massively multilingual pre-trained text-to-text transformer
* [https://huggingface.co/docs/transformers/v4.14.1/model_doc/mt5 mT5:]  mT5: A massively multilingual pre-trained text-to-text transformer
* [https://huggingface.co/docs/transformers/model_doc/nllb NLLB]: No Language Left Behind


==SpaCy== <!--T:5-->
==SpaCy== <!--T:5-->

Latest revision as of 11:56, 1 April 2025

Large Language Models

Multilingual Language Models including Dutch

  • MBart: Multilingual Denoising Pre-training for Neural Machine Translation
  • mT5: mT5: A massively multilingual pre-trained text-to-text transformer
  • NLLB: No Language Left Behind

SpaCy

spaCy is a free open-source library for Natural Language Processing in Python.

Language Modeling Benchmarks

DUMB

DUMB is a benchmark for evaluating the quality of language models for Dutch NLP tasks. The set of tasks is designed to be diverse and challenging, to test the limits of current language models. The specific datasets and formats are particularly suitable for fine-tuning encoder models, and applicability for large generative models is yet to be determined. Please read the paper for more details.

LLM Leaderboard

This is a leaderboard for Dutch benchmarks for large language models.

Scandeval Dutch NLG

n-gram modeling

Colibri core is an NLP tool as well as a C++ and Python library for working with basic linguistic constructions such as n-grams and skipgrams (i.e. patterns with one or more gaps, either of fixed or dynamic size) in a quick and memory-efficient way. At the core is the tool colibri-patternmodeller which allows you to build, view, manipulate and query pattern models.