Chat-Summarization Open-Source Conversation Summarization Model - Efficiently Generate Conversation Summaries and Easily Grasp Key Points

Chat Summarization

Developed by dnzblgn

This is a dialogue summarization model based on the T5-base architecture, fine-tuned using LoRA technology, specifically designed for efficient dialogue summarization.

Text Generation

Safetensors

EnglishOpen Source License:Apache-2.0 #Dialogue Summarization #LoRA Fine-tuning #Parameter Efficiency

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Release Time : 10/8/2024

Model Overview

The model fine-tunes T5-base with Low-Rank Adaptation (LoRA) technology, focusing on dialogue summarization tasks, suitable for various scenarios such as customer service, instant messaging, and content creation.

Model Features

Efficient Parameter Fine-tuning

Utilizes Low-Rank Adaptation (LoRA) technology to significantly reduce training parameters and improve training efficiency.

High-Quality Summary Generation

Fine-tuned on the samsum dataset, capable of generating accurate and coherent dialogue summaries.

Computational Resource Optimization

Supports mixed-precision training, suitable for running on a single GPU environment, reducing hardware requirements.

Model Capabilities

Dialogue Summarization Generation

Text Compression

Information Extraction

Use Cases

Customer Service

Chat Log Summarization

Summarizes customer service chat logs for quality monitoring or staff training.

Improves service evaluation efficiency and reduces manual review time.

Instant Messaging

Dialogue Summarization Generation

Generates summaries of instant messaging dialogues for users to quickly review important content.

Enhances user experience and saves reading time.

Content Creation

Character Dialogue Summarization

Assists writers in summarizing character dialogues to organize plot or dialogue content.

Improves creative efficiency and maintains plot coherence.

🚀 LoRA Fine-Tuned Model for Dialogue Summarization

This is a Seq2Seq model with Low-Rank Adaptation (LoRA) designed for dialogue summarization, offering efficient and accurate summarization capabilities.

🚀 Quick Start

This section will be updated once there are specific quick - start steps.

✨ Features

Specialized Summarization: Tailored for summarizing dialogues, suitable for various real - world applications.
Efficient Finetuning: Utilizes LoRA for parameter - efficient fine - tuning, reducing computational requirements.
Accurate Evaluation: Evaluated using ROUGE metrics to ensure high - quality summaries.

📦 Installation

No installation steps were provided in the original document.

📚 Documentation

Model Details

Property	Details
Model Type	Seq2Seq with Low - Rank Adaptation (LoRA)
Base Model	`google/t5-base`
Architecture	T5 - base
Finetuning Technique	LoRA (Low - Rank Adaptation)
PEFT Method	Parameter Efficient Fine - Tuning
Training Data	samsumdataset
Evaluation Metrics	ROUGE (ROUGE - 1, ROUGE - 2, ROUGE - L, ROUGE - Lsum)

Intended Use

This model is designed for summarizing dialogues, such as conversations between individuals in a chat or messaging context. It’s suitable for the following applications:

Customer Service: Summarizing chat logs for quality monitoring or training.
Messaging Apps: Generating conversation summaries for user convenience.
Content Creation: Assisting writers by summarizing character dialogues.

Training Process

Optimizer: AdamW with learning rate 3e - 5
Batch Size: 4 (gradient accumulation steps of 2)
Training Epochs: 2
Evaluation Metrics: ROUGE - 1, ROUGE - 2, ROUGE - L, ROUGE - Lsum
Hardware: Trained on a single GPU with mixed precision to optimize performance.

The model was trained using the Seq2SeqTrainer class from transformers, with LoRA parameters applied to selected attention layers to reduce computation without compromising accuracy.

📄 License

This model is licensed under the Apache 2.0 license.

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