# Text Classification

A multilingual sentiment analysis model based on DistilBERT, suitable for text sentiment classification tasks in multiple languages.

This is a fine-tuned BERT-based model for English quality-related text classification tasks, part of the Multi2ConvAI project.

This is a fine-tuned MBert model optimized for German, focusing on the quality domain, developed by the Multi2ConvAI project.

A sentiment analysis model fine-tuned based on distilbert-base-uncased for text sentiment classification tasks

This is a sentiment analysis model fine-tuned on distilbert-base-uncased, achieving 93.2% accuracy on the evaluation set.

This is a multilingual detection model based on XLM-RoBERTa, supporting recognition of 20 languages.

A sentiment analysis model fine-tuned based on distilbert-base-uncased, achieving 87.67% accuracy on the evaluation set

A lightweight text classification model based on DistilBERT for location classification tasks

A DistilBERT fine-tuned model for malicious URL detection, capable of accurately identifying harmful links such as phishing or malware

A text classification model fine-tuned based on distilbert-base-uncased, achieving an F1 score of 0.6147

A text classification model fine-tuned based on distilbert-base-uncased, trained for specific tasks.

This model is a fine-tuned AI-generated text detector based on ModernBERT-base, capable of distinguishing between AI-generated text (such as ChatGPT, DeepSeek, Claude, etc.) and human-written text.

A DistilBERT-based vulnerability severity classification model for automatically determining severity levels based on vulnerability descriptions

A sentiment analysis model fine-tuned based on distilbert-base-uncased, achieving an accuracy of 85.67% on the evaluation set

A 107M-parameter multilingual embedding model developed by IBM, supporting 12 languages with excellent performance on the MTEB benchmark

A lightweight text sentiment analysis model based on DistilBERT, specifically fine-tuned for book review data

A lightweight sentence embedding model based on DistilBERT, suitable for various text processing tasks

A text classification model supporting continual learning and dynamic category expansion

VISION-1 is a content moderation and safety classification model based on transformers, focusing on text classification tasks.

A sentiment analysis model fine-tuned on distilbert-base-uncased, achieving 94% accuracy on the evaluation set

A sentiment analysis model based on DistilBERT, fine-tuned on an unknown dataset with an accuracy of 56.16%

A small BERT model for detecting saturation-type jailbreak attacks, not suitable for independently defending against other types of jailbreak attacks.

A BERT-based pre-trained model for Arabic, suitable for Arabic text classification tasks

Multilingual E5 large instruction model supporting text embedding and classification tasks in over 100 languages

A text classification model based on PyTorch, which can be used for text classification-related tasks

This is a Korean-optimized sentence embedding model based on the multilingual-e5-base model, supporting multilingual text similarity computation and feature extraction.

A Russian spam detection model based on the Deberta architecture, capable of classifying text as spam or non-spam.

A sentiment analysis model fine-tuned on distilbert-base-uncased, achieving 87.67% accuracy on the evaluation set

A log analysis model based on DistilBert for processing and analyzing log data

This model is a fine-tuned version of neuralmind/bert-large-portuguese-cased on the PtBrVId dataset, primarily used for Portuguese text processing tasks.

Arabic sentiment analysis model fine-tuned on AraBERT, achieving 80.03% accuracy on the evaluation set

A classification model based on the DistilBERT architecture, fine-tuned using the TextAttack framework, achieving 90.04% accuracy on the evaluation set.

This is a text classification model fine-tuned on the GLUE MNLI dataset based on google/switch-base-8, primarily used for natural language inference tasks.

A text classification model fine-tuned on distilbert-base-uncased for complaint content classification, achieving 94.2% accuracy

A detection model supporting 45 languages, fine-tuned based on multilingual-e5-base with an accuracy rate of 98.37%

A multilingual detection model based on XLM-RoBERTa, capable of identifying the language category of text.

A French sentiment classification model fine-tuned on camembert-base, achieving 89.32% accuracy on the evaluation set

A text classification model fine-tuned on emotion datasets based on DistilBERT, used to identify emotional categories in text.

A text classification model fine-tuned based on bert-tiny, demonstrating good precision and accuracy on the evaluation set

This is the ONNX format conversion of the papluca/xlm-roberta-base-language-detection model, designed for multilingual text classification tasks, supporting detection in 20 languages.