# F1 Optimization

Legal text sentiment analysis model fine-tuned on Legal-BERT, achieving 88.6% accuracy

This model is a fine-tuned version of the RobBERT v2 Dutch base model for sentiment analysis tasks, primarily used for sentiment classification of Dutch texts.

A lightweight text sentiment classification model based on DistilBERT, fine-tuned on the emotion dataset with an accuracy of 93.05%

This is a medical image classification model based on the ViT architecture, specifically designed for breast composition analysis, fine-tuned on the preprocessed 1024 configuration dataset.

A visual classification model fine-tuned based on Google's ViT base model, suitable for domain-specific image recognition tasks

A token classification model fine-tuned on the xtreme dataset based on XLM-RoBERTa-base, used for named entity recognition tasks

A tag classification model fine-tuned on the xtreme dataset based on XLM-RoBERTa-base

A tag classification model fine-tuned on the xtreme dataset based on XLM-RoBERTa-base for named entity recognition tasks.

A token classification model fine-tuned on the xtreme dataset based on XLM-RoBERTa-base, primarily used for named entity recognition tasks.

A lightweight sentiment analysis model based on DistilBERT, fine-tuned on an unknown dataset with an accuracy of 93.45%

A text classification model fine-tuned on Amazon Chinese review dataset based on bert-base-chinese