| .. | ||
| processed | ||
| aiwei.json | ||
| data_pro.json | ||
| data.json | ||
| deduplicate.py | ||
| multi_turn_dataset_1.json | ||
| multi_turn_dataset_2.json | ||
| README_EN.md | ||
| README.md | ||
| single_turn_dataset_1.json | ||
| single_turn_dataset_2.json | ||
| SoulStar_data.json | ||
| tiangou.json | ||
EmoLLM's datasets
- Category of dataset: General and Role-play
- Type of data: QA and Conversation
- Summary: General(6 datasets), Role-play(3 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 | 36500+ |
| General | multi_turn_dataset_1 | Conversation | 36,000+ |
| General | multi_turn_dataset_2 | Conversation | 27,000+ |
| General | single_turn_dataset_1 | QA | 14000+ |
| General | single_turn_dataset_2 | QA | 18300+ |
| Role-play | aiwei | Conversation | 4000+ |
| Role-play | SoulStar | QA | 11200+ |
| Role-play | tiangou | Conversation | 3900+ |
| …… | …… | …… | …… |
Source
General:
- dataset
datafrom this repo - dataset
data_profrom this repo - dataset
multi_turn_dataset_1from Smile - dataset
multi_turn_dataset_2from CPsyCounD - dataset
single_turn_dataset_1from this repo - dataset
single_turn_dataset_2from this repo
Role-play:
- dataset
aiweifrom this repo - dataset
tiangoufrom this repo - dataset
SoulStarfrom SoulStar
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 via adjusting the threshold.