51 lines
2.3 KiB
Markdown
51 lines
2.3 KiB
Markdown
# EmoLLM's datasets
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* Category of dataset: **General** and **Role-play**
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* Type of data: **QA** and **Conversation**
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* Summary: General(**6 datasets**), Role-play(**5 datasets**)
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## Category
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* **General**: generic dataset, including psychological Knowledge, counseling technology, etc.
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* **Role-play**: role-playing dataset, including character-specific conversation style data, etc.
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## Type
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* **QA**: question-and-answer pair
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* **Conversation**: multi-turn consultation dialogue
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## Summary
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| Category | Dataset | Type | Total |
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| :---------: | :-------------------: | :----------: | :-----: |
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| *General* | data | Conversation | 5600+ |
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| *General* | data_pro | Conversation | 36,500+ |
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| *General* | multi_turn_dataset_1 | Conversation | 36,000+ |
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| *General* | multi_turn_dataset_2 | Conversation | 27,000+ |
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| *General* | single_turn_dataset_1 | QA | 14,000+ |
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| *General* | single_turn_dataset_2 | QA | 18,300+ |
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| *Role-play* | aiwei | Conversation | 4000+ |
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| *Role-play* | SoulStar | QA | 11,200+ |
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| *Role-play* | tiangou | Conversation | 3900+ |
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| *Role-play* | mother | Conversation | 40,300+ |
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| *Role-play* | scientist | Conversation | 28,400+ |
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| …… | …… | …… | …… |
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## Source
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**General**：
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* dataset `data` from this repo
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* dataset `data_pro` from this repo
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* dataset `multi_turn_dataset_1` from [Smile](https://github.com/qiuhuachuan/smile)
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* dataset `multi_turn_dataset_2` from [CPsyCounD](https://github.com/CAS-SIAT-XinHai/CPsyCoun)
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* dataset `single_turn_dataset_1` from this repo
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* dataset `single_turn_dataset_2` from this repo
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**Role-play**：
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* dataset `aiwei` from this repo
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* dataset `tiangou` from this repo
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* dataset `SoulStar` from [SoulStar](https://github.com/Nobody-ML/SoulStar)
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* dataset `mother` from this repo
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* dataset `scientist` from this repo
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**Dataset Deduplication**：
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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.
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