# English text classification

DistilBERT is a lightweight distilled version of BERT, retaining 97% of BERT's performance while being 40% smaller in size.

This is a binary classification model for machine-generated text, which won first place in the monolingual subtask of the COLING 2025 GenAI detection task.

This is a SetFit model trained on the sst2 dataset for English text classification tasks, utilizing efficient few-shot learning techniques.

A Big Five personality prediction model fine-tuned based on BERT BASE UNCASED, which predicts five major personality traits such as extraversion and neuroticism through text analysis.

A Twitter sentiment analysis model fine-tuned on distilbert-base-uncased, trained on the tweet_eval dataset

This is an English sequence classification model trained on the MBAD dataset, designed to detect bias and fairness in news articles.

This model is a text classification model fine-tuned on the GLUE CoLA dataset based on DistilBERT, used to determine the grammatical correctness of English sentences.

This is a sentiment analysis model fine-tuned on the distilbert-base-uncased foundation, specifically trained on the Stanford Sentiment Treebank v2 (SST2) dataset.

A text classification model based on DistilBERT, fine-tuned on the GLUE dataset CoLA task for judging grammatical correctness of sentences

A BERT-based text classification model for detecting hate speech and offensive content, with rationale prediction capability

This is an adapter trained on the roberta-base model, specifically designed for sentiment analysis tasks on IMDB movie reviews.