# High-precision Text Classification

Stella 400M v5 is an English text embedding model that excels in multiple text classification and retrieval tasks.

A text classification model fine-tuned based on microsoft/Multilingual-MiniLM-L12-H384, used to classify email subjects as spam (SPAM) or non-spam (NOSPAM).

XLM-RoBERTa-based Filipino/Tagalog fake news detection model with 95.46% accuracy

A BERT-based fine-tuned model specifically designed for legal clause classification tasks, achieving an accuracy rate of 88%.

stella-base-en-v2 is an English text embedding model based on sentence-transformers, focusing on sentence similarity and feature extraction tasks.

A sentiment analysis model fine-tuned based on the Turkish BERT model, demonstrating outstanding performance on evaluation datasets

This is a document understanding model specifically designed for analyzing document layout and content, performing token classification tasks at the paragraph level.

A text embedding model based on the T5 architecture, focusing on sentence similarity calculation and text retrieval tasks, with excellent performance in multiple benchmark tests.

A sentiment analysis model fine-tuned on BERT-Large-Uncased, trained on the Stanford Sentiment Treebank (SST2) for text sentiment classification.

A text classification model fine-tuned on the GLUE MNLI dataset based on bert-base-cased, designed for natural language inference tasks

This is a DistilBERT model fine-tuned on the IMDB movie review dataset for text sentiment analysis tasks.

DeBERTa-v3 model trained on the MultiNLI dataset for natural language inference tasks, excelling in zero-shot classification scenarios.