Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- Nuo97/Dolphin-DPO
+language:
+- zh
+metrics:
+- bleu
+pipeline_tag: conversational
+---
+
+# COMEDY: COmpressive Memory-Enhanced Dialogue sYstems framework.
+
+Github: https://github.com/nuochenpku/COMEDY
+
+
+### Task: Long-Term Conversation Dialogue Generation
+
+Different from previous retrieval-based methods, COMEDY doesn't rely on any **retrieval module or database**. 
+
+Instead, COMEDY adopts a groundbreaking ''**One-for-All**'' approach, utilizing a single, unified model to manage the entire process from memory generation, compression to final response generation for long-term memory dialogue generation.
+
+
+ - COMEDY firstly involves distilling session-specific memory from past dialogues, encompassing fine-grained session summaries, including event recaps, and detailed user and bot portraits; 
+ 
+ - In a break from traditional systems, COMEDY eschews the use of a memory database for storing these insights. Instead, it reprocesses and condenses memories from all past interactions, forming a *Compressive Memory*: The first part is the **concise events** that have occurred throughout all the conversations, creating a historical narrative that the system can draw upon. The second and third parts consist of a **detailed user profile** and the **dynamic relationship changes** between the user and chatbot across sessions, both derived from past conversational events.
+
+- Finally, COMEDY skillfully integrates this compressive memory into ongoing conversations, enabling contextually memory-enhanced interactions.
+
+
+### Training Dataset 
+
+**Dolphin**, the biggest Chinese long-term conversation dataset, from actual online user-chatbot interactions. 
+
+This dataset contains three tasks: 
+
+**Session-Level Memory Summarization**;
+
+**Memory Compression**;
+
+**Memory-Grounded Response Generation**, 
+
+comprising an extensive collection of 100k samples.
+
+Dolphin is available at [**Dolphin**](https://huggingface.co/datasets/Nuo97/Dolphin-DPO)
+
+### Training Strategy
+
+Our training strategies include two stages: Mixed-task training and DPO Alignment.
+
+<br>
+<div align="center">
+  <img src="training_strategy.png" width="90%" title="Introduction Figure">
+</div>
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