# Short Text Processing

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

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

A Russian Q&A model fine-tuned from cointegrated/rubert-tiny2, suitable for SQuAD-format question answering tasks

This is a sentence similarity calculation model based on the DistilBERT architecture, capable of mapping sentences and paragraphs into a 3-dimensional vector space, suitable for semantic search and clustering tasks.

This model is based on albert-base-v2's masked language model, specifically fine-tuned for electronic product attribute correction tasks

A fine-tuned version of the bert-base-uncased model on a review dataset, suitable for text classification tasks.

This is a lightweight sentence embedding model based on sentence-transformers, capable of mapping text to a 32-dimensional vector space, suitable for semantic similarity calculation and text clustering tasks.

A model for classifying emoji description texts, suitable for social media and instant messaging applications.

An Indonesian sentiment analysis model based on the RoBERTa architecture, capable of identifying emotional tendencies in text.

BERT-Medium is a lightweight version of the BERT model released by Google, featuring fewer network layers and hidden units, making it suitable for resource-constrained environments.

BERT-based fine-tuned Twitter sentiment analysis model achieving 100% validation accuracy on the evaluation set

A lightweight text classification model based on the DistilBERT architecture, fine-tuned on the AG News dataset using the TextAttack framework, suitable for news classification tasks.

This is a multi-class classification model trained using AutoNLP, specifically designed for tweet sentiment analysis.

A text classification model fine-tuned on the GLUE dataset based on the TextAttack framework, mainly used for natural language processing tasks.

This model is a fine-tuned version of Microsoft MiniLM-L12-H384-uncased on a sentiment analysis dataset, primarily used for text sentiment classification tasks.

A classification model fine-tuned on the GLUE dataset, suitable for natural language processing tasks

This is a sentence embedding model based on sentence-transformers, which can map text to a 256-dimensional vector space and is suitable for semantic search and clustering tasks.