# Chinese Text Processing

This is a text classification model based on the SetFit framework, specifically designed for contract clause classification tasks with an accuracy of 94.25%.

This is the ONNX format conversion version of the gyr66/bert-base-chinese-finetuned-ner model, designed for Chinese named entity recognition tasks.

A tool for restoring punctuation marks in Chinese Wikipedia texts, supporting the restoration of 6 common punctuation marks.

A lightweight pre-trained encoder-decoder model based on Transformer architecture, specialized for Chinese news summarization tasks.

This model is a fine-tuned version of ckiplab/bert-base-chinese-ws for Chinese Named Entity Recognition (NER) tasks, demonstrating outstanding performance on evaluation datasets.

This model provides word segmentation functionality for classical Chinese, with training datasets covering four historical periods of Chinese language development.

This is a sentence embedding model based on sentence-transformers, capable of mapping sentences and paragraphs into a 512-dimensional dense vector space, suitable for tasks such as semantic search and clustering.

This is a single-column regression model trained with AutoNLP for text data prediction tasks.

A sequence-to-sequence model based on the BART architecture, fine-tuned on the CNN/DailyMail dataset, suitable for text summarization tasks.