# Multi-round Training

A Hausa-to-Chinese translation model fine-tuned based on T5-small, achieving a BLEU score of 30.0183 on the evaluation set.

This is an English-to-Hindi neural machine translation model based on the Marian framework, fine-tuned by the Indian Institute of Technology Bombay

This is a speech emotion recognition model based on the facebook/wav2vec2-base model fine-tuned on the IEMOCAP dataset, achieving 73.9% accuracy on the evaluation set.

An automatic speech recognition model trained on an unknown dataset, supporting language identification tasks

This model is a fine-tuned speech recognition model based on wav2vec2-base-checkpoint-8 on the common_voice dataset, achieving a word error rate of 0.3258 on the evaluation set.

BibliBERT is a fine-tuned version based on the Italian pre-trained model dbmdz/bert-base-italian-xxl-cased, primarily used for masked language modeling tasks.

This model is a PyTorch pretrained model obtained by converting TensorFlow checkpoints from the official Google BERT repository, originating from the paper 'Well-Read Students Learn Better: On the Importance of Pre-training Compact Models,' and trained on the MNLI dataset.