# Pre-training Fine-tuning

This is a Bert model optimized for German, focusing on text classification tasks in the quality domain.

A video classification model based on the VideoMAE architecture, fine-tuned specifically for shoplifting behavior detection

Bio_ClinicalBERT is a clinical text processing model based on the BERT architecture, specifically optimized for biomedical and clinical domain texts.

This is a code summarization generation model based on the CodeT5-base architecture, fine-tuned using the Python portion of the CodeXGLUE dataset.

A Chinese PEGASUS-large model specialized in text summarization tasks, fine-tuned on multiple Chinese summarization datasets

A Chinese PEGASUS-large model specialized in text summarization tasks, fine-tuned on multiple Chinese summarization datasets

TAPEX-large is a large-scale language model pre-trained on tabular data, specifically fine-tuned for table question answering tasks. It achieves table understanding through a neural SQL executor and can answer natural language questions about table content.

TAPEX-large is a table QA model fine-tuned on the WikiSQL dataset, capable of understanding natural language questions and generating SQL queries or direct answers based on tabular data.

A Thai Q&A system fine-tuned based on the WangchanBERTa model, trained using multiple Thai QA datasets

A Korean dialogue summarization model based on the BART pre-trained model, trained with R3F technology, participating in the 2021 Hunminjeongeum Korean Speech & Natural Language AI Competition

An Arabic named entity recognition model fine-tuned based on CAMeLBERT Mix, supporting entity recognition in Modern Standard Arabic, dialects, and Classical Arabic

A pre-trained model based on the RoBERTa architecture, optimized for tasks like question answering, with enhanced reasoning capabilities.

This is a Danish pre-trained BERT model developed by Certainly (formerly BotXO), later fine-tuned by Malte Højmark-Bertelsen on the DaNE dataset for named entity recognition tasks.

A sentiment analysis model fine-tuned based on the CAMeLBERT mixed model, supporting Arabic text sentiment classification