# BERT fine-tuned model

Fine-tuned model based on BERT-base for text classification tasks, achieving an F1 score of 0.9313 on the evaluation set

A Russian miniature language model based on BERT architecture, suitable for sentence similarity calculation and feature extraction tasks

This model is a BERT fine-tuned aspect sentiment analysis model capable of classifying emotions related to specific aspects in text. It provides valuable insights for analyzing customer opinions and sentiments toward different features in user-generated content.

A BERT-based Spanish sentiment analysis classifier used to detect positive and negative sentiments in text.

adeBERT is a fine-tuned model based on the BERT-large architecture, focusing on domain-specific tasks and demonstrating excellent performance on evaluation datasets.

A hate speech classifier fine-tuned on the BERT-large Portuguese model, achieving 78.14% accuracy on the hate_speech_portuguese dataset

An Indonesian fake news classification model based on IndoBERT, fine-tuned on an unknown dataset with an accuracy of 80.59%

A multilingual sentiment analysis model fine-tuned based on bert-base-multilingual-uncased, supporting sentiment analysis of product reviews in 6 languages

A text classification model fine-tuned on the Amazon product review sentiment analysis dataset based on the BERT base model, achieving 94.65% accuracy

A Spanish text classification model built on PyTorch for determining whether text is technically relevant.

This model is a fine-tuned version of bert-base-uncased on text related to door closing systems, specializing in handling text tasks associated with door systems.

Arabic grammar error correction model fine-tuned based on aubmindlab/bert-base-arabertv02

BERT model for detecting subjectivity or objectivity in Danish text

A text classification model fine-tuned on Reddit dialogue dataset based on bert-base-uncased, used to determine whether two sentences are related

A hate speech detection model based on Danish BERT, used to identify offensive content in Danish text.