# Multilingual pre-training

CodeGen-HTML 350M is an autoregressive language model fine-tuned from CodeGen-Multi 350M, specifically designed for HTML code generation.

This model is a speech recognition model fine-tuned from facebook/wav2vec2-base, trained using data augmentation techniques

A multilingual speech recognition model based on the wav2vec 2.0 architecture, specifically fine-tuned for Persian, significantly reducing word error rate

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MULTILINGUAL_LIBRISPEECH - German 10-hour dataset for German speech recognition tasks.

This model is an XLSR-Wav2Vec2 speech recognition model fine-tuned on the Turkish Common Voice dataset, primarily used for Turkish speech-to-text tasks.

XLM-RoBERTa is an extended version of a cross-lingual sentence encoder, trained on 2.5T of data across 100 languages, achieving excellent performance in multiple cross-lingual benchmarks.

An automatic speech recognition model based on Facebook's Wav2Vec2 base model, pre-trained on a 10K unlabeled subset of the VoxPopuli corpus and fine-tuned on Finnish transcription data.

A speech recognition model based on Facebook's Wav2Vec2 architecture, pre-trained with 10K unlabeled Czech data from the VoxPopuli corpus and fine-tuned on Czech transcription data.

Pre-trained on 10K unlabeled data from the VoxPopuli corpus and fine-tuned on Polish transcription data

CINO is a multilingual pre-trained model designed for Chinese minority languages, supporting Chinese and 7 minority languages, built on the XLM-R framework.

A multilingual machine translation model fine-tuned based on mBART-large-50, supporting translation between 50 languages

Pre-trained on 10K hours of unlabeled VoxPopuli corpus data and fine-tuned on Slovak transcription data