# Rouge Optimization

A fine-tuned version of the google/flan-t5-base model for dialogue summarization tasks, using PEFT for parameter-efficient fine-tuning

This model is a text summarization generation model fine-tuned on the Bodo legal summarization dataset based on facebook/bart-large

A text processing model fine-tuned based on the T5-small architecture, focusing on text generation and transformation for specific tasks

A text summarization generation model fine-tuned based on Falconsai/text_summarization, supporting the extraction of key information from long texts to generate concise summaries.

This is an abstract generation model trained via the AutoTrain platform, based on the BioBART architecture, suitable for text summarization tasks.

This is a financial conversation summarization model trained using AutoTrain, specifically designed to extract key information from financial dialogues and generate concise summaries.

A summary generation model fine-tuned on an unknown dataset based on Google's mT5 model, supporting multilingual text summarization tasks

This is a summary generation model trained on the AutoTrain platform, suitable for financial text summarization tasks.

This model is a fine-tuned version of Salesforce/codet5-base, designed to generate docstrings for Python functions.

This is a T5-Pegasus architecture-based Chinese abstract generation model trained on the AutoTrain platform, suitable for extracting key information from text to generate summaries.

A text summarization model fine-tuned on feedback-based summarization datasets, based on google/flan-t5-large

This model is a fine-tuned version of google/flan-t5-small on the summarize_from_feedback dataset, primarily designed for text summarization tasks.

A Japanese summarization model fine-tuned from google/mt5-small, specifically designed for news story summarization

TGL-3 is a fine-tuned abstract generation model based on t5-small, trained on 23,000 openreview.net abstract data entries, supporting academic text summarization tasks.

A text summarization model fine-tuned on the CNN/DailyMail news dataset based on the T5-small model, capable of generating concise summaries of news articles.

A text summarization generation model fine-tuned on the cnn_dailymail dataset based on the T5-small model

A text summarization model fine-tuned on the cnn_dailymail dataset based on the T5-small architecture, excelling in news summarization tasks

A text generation model based on the T5-small architecture fine-tuned on the SNLI dataset, specializing in contradiction relation recognition and summarization tasks

This model is a text summarization model fine-tuned on the Amazon dataset based on google/mt5-small, supporting English to Traditional Chinese abstract generation tasks.

DistilBART is a distilled version of the BART model, optimized for text summarization tasks, significantly improving inference speed while maintaining high performance.

DistilBART is a distilled version of the BART model, focusing on text summarization tasks, significantly reducing model size and inference time while maintaining high performance.

A text summarization generation model fine-tuned on the distilbart-cnn-12-6 model, trained for 1000 steps with weakly supervised data

DistilBART is a distilled version of the BART model, specifically optimized for summarization tasks, significantly reducing model parameters and inference time while maintaining high performance.

DistilBART is a distilled version of the BART model, focusing on text summarization tasks, significantly reducing model parameters and inference time while maintaining high performance.

This model is a text summarization model fine-tuned on the arXiv computer science paper dataset based on mt5-small, excelling at generating concise summaries of technical content.

A text generation model fine-tuned on the wsj_markets dataset based on the BART-large architecture, excelling in summarization tasks

DistilBART is a distilled version of the BART model, optimized for text summarization tasks, significantly reducing model size and inference time while maintaining high performance.

This model is a fine-tuned version of facebook/bart-base on the pub_med_summarization_dataset, primarily designed for medical literature summarization tasks.

A text generation model fine-tuned on biomedical paper abstract datasets based on the BART-large architecture