# High-precision text classification

This model is a fine-tuned version based on microsoft/deberta-v3-base, achieving an outstanding accuracy of 99.87% on the evaluation set.

DistilBERT fine-tuned fake news classification model with approximately 99% accuracy

FactCG is a text classification model based on the DeBERTa-v3-large architecture, specifically designed to detect unsubstantiated hallucinations in content generated by large language models.

An 8-billion-parameter brand safety classification model optimized based on Llama 3.1, specifically designed for Apple chip devices

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

A multilingual sentiment classification model fine-tuned based on XLM-RoBERTa-large, supporting sentiment analysis for over 16 languages

LENS-8000 is a transformer-based text embedding model focused on feature extraction and sentence similarity tasks, excelling in multiple classification and retrieval tasks.

LENS-4000 is a transformer-based text embedding model focused on feature extraction and sentence similarity calculation, excelling in multiple text classification tasks.

Giga Embeddings Instruct is a high-performance embedding model focused on text classification and retrieval tasks, excelling in multiple benchmark tests.

This model is a text classification model fine-tuned on an unknown dataset based on PhoBERT-base-v2. It achieved an accuracy of 99.67% and an F1 score of 99.32% on the evaluation set.

A 7B-parameter sentence embedding model based on the Qwen2 architecture, specializing in sentence similarity tasks with outstanding performance on the MTEB benchmark.

Stella 400M is a medium-scale English text processing model focused on classification and information retrieval tasks.

stella_en_400M_v5_cpu is a model that performs excellently in multiple natural language processing tasks, especially in tasks such as classification, retrieval, clustering, and semantic text similarity.

NV-Embed-v2 is an embedding model based on sentence-transformers, excelling in multiple MTEB benchmarks and suitable for various natural language processing tasks.

A general-purpose text embedding model developed by Salesforce, excelling in various NLP tasks

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 text classification model fine-tuned based on RoBERTa-base for detecting suicidal tendencies in text, with an accuracy of 96.5%

A hate/offensive speech detector based on the lightweight DistilBERT model, achieving efficient recognition through massive text pre-training and fine-tuning with proprietary datasets

A zero-shot classification model based on DeBERTa-v3, supporting multi-task natural language inference (MNLI), FEVER, and ANLI tasks, converted to ONNX format for compatibility with Transformers.js

A website classification model based on DistilBERT, achieving an accuracy of 95.04% on an unknown dataset through fine-tuning.

A Korean sentiment analysis model fine-tuned based on KcELECTRA-base-v2022, which achieved good results on the evaluation set.

A financial tweet topic classification model fine-tuned on the Twitter financial news topic dataset, supporting 20 types of financial topic classifications.

A Korean text classification model based on ELECTRA architecture, designed to determine if comments and chat content contain sensitive information

A text classification model based on the ELECTRA architecture, designed to detect suicidal tendencies in text.

This model is a text classification model fine-tuned on the GLUE SST2 dataset using DeBERTa v3 small, demonstrating excellent performance in sentiment analysis tasks.

This is a FinBERT model fine-tuned for Finnish sentiment analysis tasks, trained and evaluated using the FinnSentiment dataset.

A text classification model fine-tuned on the GLUE MNLI dataset based on the RoBERTa-base model, excelling in natural language inference tasks

A sentiment analysis model fine-tuned on the Naver Sentiment Movie Corpus dataset based on the KoELECTRA-Small-v3 model, used for classifying Korean movie reviews as positive or negative

A text classification model fine-tuned on the dbpedia_14 dataset based on bert-base-uncased, achieving 99.03% accuracy

DeBERTaV3 improves upon DeBERTa with ELECTRA-style pre-training and gradient-disentangled embedding sharing techniques, excelling in natural language understanding tasks

This is a binary classification model based on the XLM-Roberta-base architecture, capable of classifying sentences as facts or opinions. It supports English and Greek, and features zero-shot learning capability.