# English Text Classification

This is a Bert model optimized for the COVID-19 domain, developed by the Multi2ConvAI project

DeBERTa-v3-small is a lightweight variant of the DeBERTa model released by Microsoft, suitable for text classification tasks.

A text classification model based on DistilBERT for detecting whether input text qualifies as a tarot divination question.

A fine-tuned model based on RoBERTa-base for detecting AI-generated English text.

This is a dataset for phishing attack detection, primarily used to train BERT models to identify phishing websites.

This is a sentiment analysis model fine-tuned based on BERT, specifically designed to analyze sentiment tendencies (positive or negative) in IMDb movie reviews.

DistilBERT is a lightweight distilled version of BERT, retaining 97% of BERT's performance while being 40% smaller and 60% faster in inference.

A sentiment analysis model fine-tuned on DistilBERT-base-uncased, trained on the IMDB dataset with an accuracy of 93.1%

Financial text sentiment analysis model fine-tuned based on Llama-3-8B, supporting positive/neutral/negative three-class classification

Financial news sentiment analysis model fine-tuned on DeBERTa-v3-small, achieving an F1 score of 0.9940 on the evaluation set

This is the ONNX-converted version of the deepset/deberta-v3-base-injection model for detecting prompt injection attacks.

A lightweight phishing email detection model based on DistilBERT for identifying suspicious email content.

ArguGPT is a model designed to evaluate, understand, and identify argumentative essays generated by GPT models.

A toxic comment classification model fine-tuned based on bert-base-uncased, used to identify toxic content in text.

A depression detection model fine-tuned based on RoBERTa-large, used to analyze the level of depression in English social media posts, categorized into no depression, moderate, or severe.

BERT-based English text sarcasm detection model for identifying sarcastic content in news headlines

This model is based on the RoBERTa architecture and trained using the CrossEncoder class from SentenceTransformers, specifically designed for detecting duplicate questions on Quora.

A text classification model for detecting and categorizing hate speech.

A text classification model fine-tuned on the GLUE COLA dataset based on google/fnet-base, used to evaluate the performance comparison between FNet and BERT architectures

This model is a text classification model fine-tuned on the GLUE dataset's RTE task based on DistilBERT, with an accuracy of 61.73%

A text classification model based on the FNet architecture, fine-tuned on the GLUE COLA dataset for grammatical correctness judgment

A text classification model based on Google's FNet architecture fine-tuned on the SST-2 sentiment analysis dataset

This is an MBert model fine-tuned for the quality domain, specifically designed for English text classification tasks.

A DistilBert sentiment analysis model trained on the IMDB movie review dataset for text sentiment classification tasks.

A text classification model fine-tuned on the GLUE SST2 dataset based on albert-base-v2 for sentiment analysis tasks