# T5 Fine-tuning

PragmaticLM is a T5 fine-tuned model specifically designed for restructuring user prompts to enhance the comprehension of downstream large language models.

This is a T5 text summarization model fine-tuned on the SAMSum dataset, suitable for dialogue and conversation summarization tasks.

A T5-base model fine-tuned on the BoolQ dataset, specifically designed for generating true/false question-answer pairs.

A T5-base fine-tuned email processing model, suitable for text generation tasks

A text processing model fine-tuned from t5-small, suitable for email-related tasks

A legal text summarization model fine-tuned based on T5 architecture, capable of generating both short and long summaries of legal cases

This model is a fine-tuned version of t5-small, optimized for summarizing scientific and medical texts using the PubMed dataset.

A T5-small fine-tuned model for generating scientific paper abstracts, capable of extracting key information from research papers.

A FinTech chatbot fine-tuned based on T5-small architecture, specifically designed for banking inquiries

A text summarization model based on the T5-small architecture, fine-tuned on the multi_news dataset, excelling in generating abstractive summaries

A T5-base fine-tuned model for converting job descriptions into structured resume JSON data

A conversation trajectory prediction model fine-tuned based on T5-small, used to analyze the development direction of dialogue texts

A text paraphrasing model fine-tuned on T5-Base, capable of efficiently rewriting text content while preserving the original meaning and altering sentence structures.

This is a clinical note summarization model fine-tuned based on the T5-small model, primarily used for generating summaries of clinical notes.

A T5-Small fine-tuned text-to-BPMN diagram generation model, specifically designed for business process modeling

A text summarization model fine-tuned from T5-small, specifically designed for summarizing congressional bill contents

A text generation model fine-tuned on the SST dataset based on T5-base

Text summarization model fine-tuned on google/t5-efficient-tiny, trained on the billsum dataset

KeywordGen-v2 is a T5-based model specifically designed for generating keywords from text, particularly suitable for product review analysis.

A financial document summarization model fine-tuned from T5-small, trained on the searde/dataset-financial-documents-3 dataset

A text moderation model fine-tuned based on t5-small, excelling in content filtering and moderation tasks

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

A fine-tuned T5 model specialized for TLDR news article summarization and headline generation

A Korean text summarization model based on the T5 architecture, specifically optimized for Korean text to generate concise and accurate summaries.

An English question-answer pair generation model fine-tuned based on T5-base, used to generate question and answer pairs from text passages.

A document expansion model based on T5-base architecture, trained on the MS MARCO dataset, used to generate potential queries related to document content to enhance retrieval effectiveness

This model is a fine-tuned T5-small model based on the HUPD dataset, specifically designed for patent text summarization tasks.

A Chinese spelling correction model based on the T5 architecture, excelling on the SIGHAN2015 test set, supporting automatic correction of Chinese text.

A dialogue summarization model fine-tuned on t5-small, trained on the samsum dataset

A T5-small fine-tuned model for English question generation and answer extraction, suitable for generating questions from text or extracting answers.

A fine-tuned text-to-SQL conversion model based on T5-3B architecture, significantly improving structured query generation accuracy through PICARD constrained decoding

A text rewriting model fine-tuned on the TaPaCo dataset based on the T5-small architecture, capable of generating semantically equivalent but differently expressed sentences.

An end-to-end question generation model fine-tuned on T5-base, capable of automatically generating relevant questions from given contexts.

This is a fine-tuned version of Google's T5-small model on the WikiSQL dataset, designed to convert English natural language queries into SQL statements.

This is an automatic podcast summary generation model fine-tuned based on the T5-base model, trained using the Spotify podcast dataset.

A T5-base fine-tuned joint model for question generation and answer extraction, supporting English text processing

A T5-small summarization model fine-tuned for Indonesian language, based on huseinzol05's t5-small-bahasa-summarization-cased model and fine-tuned using the indosum dataset.

T5 fine-tuned model for generating natural fallback responses to unanswerable queries

This is a question generation model fine-tuned on the T5-Base architecture, specifically designed to generate relevant questions from given answers and contexts.

T5-based doc2query model for document expansion and training data generation