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README_EN.md

EmoLLM's datasets

  • Category of dataset: General and Role-play
  • Type of data: QA and Conversation
  • Summary: General(6 datasets), Role-play(5 datasets)

Category

  • General: generic dataset, including psychological Knowledge, counseling technology, etc.
  • Role-play: role-playing dataset, including character-specific conversation style data, etc.

Type

  • QA: question-and-answer pair
  • Conversation: multi-turn consultation dialogue

Summary

Category Dataset Type Total
General data Conversation 5600+
General data_pro Conversation 36,500+
General multi_turn_dataset_1 Conversation 36,000+
General multi_turn_dataset_2 Conversation 27,000+
General single_turn_dataset_1 QA 14,000+
General single_turn_dataset_2 QA 18,300+
Role-play aiwei Conversation 4000+
Role-play SoulStar QA 11,200+
Role-play tiangou Conversation 3900+
Role-play mother Conversation 40,300+
Role-play scientist Conversation 28,400+
…… …… …… ……

Source

General：

  • dataset data from this repo
  • dataset data_pro from this repo
  • dataset multi_turn_dataset_1 from Smile
  • dataset multi_turn_dataset_2 from CPsyCounD
  • dataset single_turn_dataset_1 from this repo
  • dataset single_turn_dataset_2 from this repo

Role-play：

  • dataset aiwei from this repo
  • dataset tiangou from this repo
  • dataset SoulStar from SoulStar
  • dataset mother from this repo
  • dataset scientist from this repo

Dataset Deduplication： Combine absolute matching with fuzzy matching (Simhash) algorithms to deduplicate the dataset, thereby enhancing the effectiveness of the fine-tuning model. While ensuring the high quality of the dataset, the risk of losing important data due to incorrect matches can be reduced by adjusting the threshold.