# Multi-task Training

The first version of the Fio series Japanese embedding model, based on BERT architecture, focusing on Japanese text similarity and feature extraction tasks

A sentence embedding model based on BioBERT, trained on multiple NLI datasets, suitable for sentence similarity calculation and semantic search tasks

This is a hybrid model for Persian Automatic Speech Recognition (ASR), combining transducer and CTC decoder losses, optimized based on the FastConformer architecture.

GLuCoSE is a Japanese text embedding model based on LUKE, suitable for sentence similarity and semantic search tasks.

This is a model based on sentence-transformers that can map sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as clustering or semantic search.

A PubMedBERT-based sentence transformer model for generating 768-dimensional vector representations of sentences and paragraphs, suitable for semantic search and clustering tasks.

This is a sentence-transformers-based model that maps sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as clustering or semantic search.