# Text classification

Model fine-tuned based on ModernBERT-base, achieving an F1 score of 0.3833 on the evaluation set

A machine-generated text detection model based on the ModernBERT-base architecture, designed to prevent language model collapse

This model is a fine-tuned version based on bert-base-uncased, focusing on improving recall performance for specific tasks

A sentiment analysis model fine-tuned from vinai/phobert-base-v2, achieving 93.6% accuracy on the evaluation set

This model is a fine-tuned version of FacebookAI's roberta-base on the Webi-CPC-11 dataset, primarily used for text classification tasks, achieving an accuracy of 84.32% on the evaluation set.

A Transformer-based classifier for determining whether a provided surname is real or fictional.

A BERT-based fine-tuned phishing email detection model that accurately identifies phishing emails versus legitimate emails.

This is a BERT model fine-tuned for spam detection with a validation accuracy of approximately 99%

GPT-2 fine-tuned language identification model supporting text classification in 90 languages

A lightweight Uzbek sentiment analysis model based on DistilBERT for text sentiment classification

A zero-shot classification model based on the Transformers library, supporting English text classification tasks.

A BERT fine-tuned SMS classification model for distinguishing spam from non-spam messages.

An emotion classification model for English text that can identify 20 different emotional states.

fastText is an open-source, lightweight library for text classification and word representation learning. This model is specifically designed for language identification, supporting detection of 217 languages.

A model fine-tuned on 600+ tasks based on DeBERTa-v3-base through multi-task learning, excelling in zero-shot classification and natural language inference

A classification model fine-tuned based on bert-base-uncased for unknown classification tasks

XLM-RoBERTa-based language detection model supporting text classification in 20 languages

A fine-tuned version based on the DistilBERT base model, suitable for text classification tasks

A fine-tuned version of the bert-base-uncased model, suitable for specific task processing

This model is a fine-tuned version based on hfl/chinese-bert-wwm on an unknown dataset, suitable for Chinese text processing tasks.

This model is a fine-tuned version of bert-base-uncased, with no specific fine-tuning task or dataset information provided.

This model is a fine-tuned version based on bert-base-uncased. Specific application scenarios and training dataset information are not provided.

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

The first lightweight ALBERT model for Persian language, trained based on Google ALBERT BASE 2.0 version

DistilBERT is a distilled version of BERT that retains 97% of BERT's performance while being 40% smaller and 60% faster.

A text classification model fine-tuned on the GLUE SST2 dataset based on the bert-base-cased model, with an accuracy of 92.3%.

Multilingual detection model based on XLM-RoBERTa, supporting text classification in 20 languages

Hate speech detection model fine-tuned on distilbert-base-uncased, trained on tweet_eval dataset

KoBERT is a Korean pre-trained language model based on the BERT architecture, suitable for various Korean natural language processing tasks.

I-BERT is a pure integer-quantized version of RoBERTa-large, using INT8 to store parameters and integer operations for inference, achieving up to 4x inference acceleration.

RoBERTa model trained on Korean dataset for sentence intent understanding tasks

A model fine-tuned based on bert-base-uncased, with a training accuracy of 93.75% and validation accuracy of 50%

I-BERT is a pure integer quantized version of RoBERTa, storing parameters in INT8 format and using integer operations for inference, significantly improving inference speed.

A lightweight model based on DistilBERT for detecting hate speech and offensive content in text

A lightweight model based on DistilBERT for detecting hate speech and offensive content in text

Hate speech detection model fine-tuned based on distilbert-base-uncased

This model is a fine-tuned version of distilbert-base-uncased, designed for classifying hate speech and offensive content.

A lightweight model based on DistilBERT, fine-tuned for hate speech and offensive language detection tasks

A lightweight model based on DistilBERT, fine-tuned for hate speech and offensive language detection tasks

A lightweight model based on DistilBERT for detecting hate speech and offensive content in text