# Multi-metric evaluation

This model is fine-tuned based on `dbmdz/bert-base-turkish-cased` and is used for binary toxicity classification of Turkish texts.

Giga Embeddings Instruct is a high-performance embedding model focused on text classification and retrieval tasks, excelling in multiple benchmark tests.

Based on the transformers library, this is a fine-tuned version of openai/whisper-large-v3-turbo on the common_voice_11_0 dataset, optimized specifically for Arabic speech recognition.

A Ukrainian automatic speech recognition model trained on the mHuBERT-147 base model, supporting Ukrainian speech-to-text tasks.

This model is a fine-tuned version of t5-small on the aslg_pc12 dataset, primarily used for sign language translation tasks.

This model is a fine-tuned Korean-to-English translation model based on Helsinki-NLP/opus-mt-ko-en, trained using the Korean Parallel Corpora dataset.

An image classification model fine-tuned on the image folder dataset based on microsoft/cvt-13-384-22k, achieving an accuracy of 83.46% on the evaluation set

The first Chinese generative Q&A pre-trained T5 model, pre-trained on WuDao 180G corpus and fine-tuned on Chinese SQuAD and CMRC2018 datasets

Abstractive summarization model fine-tuned on the Arabic summarization dataset xlsum based on AraT5-base

A Telugu speech recognition model fine-tuned on the OPENSLR_SLR66-NA dataset based on facebook/wav2vec2-xls-r-1b, supporting automatic speech recognition tasks.

Indonesian natural language inference classifier based on the Indo-roberta model, trained using the IndoNLI dataset