Chinese Medical Dialogue
Deep Learning for Intelligent Healthcare
By Huggingface Hub [source]
About this dataset
This dataset is designed to train a deep learning language model for intelligent healthcare using Chinese medical dialogue. It includes different components such as pretraining, finetuning and reward data which allows the model to learn how to produce more accurate answers in the medical context. The dataset consists of columns containing questions, chosen responses and rejected responses allowing it to view multiple perspectives when constructing a conversation. This makes the model not only more precise but also reinforces its ability to engage with medical dialogue at an advanced level, making it great resource for businesses, researchers or any individual looking into developing their own intelligent healthcare system
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How to use the dataset
This dataset can be used to train an intelligent language model for medical dialogue in Chinese. To use this dataset, one would need to get familiar with the following steps:
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Pretraining - Use the pretraining data provided in the dataset to build and fine-tune a language model. This will help the model understand basic elements of the medical dialogue and acquire general knowledge about Chinese medicine.
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Finetuning - Use the finetune data and apply transfer learning techniques such as distrust learning or multi-task learning to further improve model accuracy on specific tasks such as medical related questions and responses.
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Reward - Make use of rewards from patient or doctor for correct responses, which will help boost performance of AI systems by guiding them with real feedback from experienced healthcare professionals or patients themselves based on their understanding of medicine knowledge in long dialogue flows interviews or discussions .
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Evaluation - After training with pretraining/finetuning/reward datasets, make sure you evaluate your trained models on unseen data using reward_validation file which is provided along with the dataset itself to assess its performance level effectively
Research Ideas
- Utilizing reinforcement learning with the reward data for training a dialogue model that rewards correct responses.
- Employing few-shot learning methods to quickly adapt the pretraining data for new and unseen medical dialogues.
- Exploring transfer learning techniques to apply knowledge learned from one medical domain to another
Acknowledgements
If you use this dataset in your research, please credit the original authors.
Data Source
License
License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.
Columns
File: reward_train.csv
| Column name |
Description |
| question |
The question asked in the medical dialogue. (String) |
| response_chosen |
The response chosen by the model as the correct answer. (String) |
| response_rejected |
The response rejected by the model as the incorrect answer. (String) |
File: reward_test.csv
| Column name |
Description |
| question |
The question asked in the medical dialogue. (String) |
| response_chosen |
The response chosen by the model as the correct answer. (String) |
| response_rejected |
The response rejected by the model as the incorrect answer. (String) |
File: reward_validation.csv
| Column name |
Description |
| question |
The question asked in the medical dialogue. (String) |
| response_chosen |
The response chosen by the model as the correct answer. (String) |
| response_rejected |
The response rejected by the model as the incorrect answer. (String) |
Acknowledgements
If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit Huggingface Hub.