IDinsight/urgency_detection_maternal_health_synthetic

--- license: mit task_categories: - text-classification - question-answering - zero-shot-classification - text-generation language: - en tags: - urgency detection - maternal health size_categories: - 10K<n<100K source_datasets: - original pretty_name: Urgency Detection for Maternal Health --- # Maternal Health Messages for Urgency Detection This dataset contains ~12.8k synthetic user messages related to [maternal health issues](https://saferbirth.org/aim-resources/aim-cornerstones/urgent-maternal-warning-signs-2/) generated using [Gemini-1.5-Flash](https://deepmind.google/technologies/gemini/flash/) and verified using [Gemini-1.5-Pro](https://deepmind.google/technologies/gemini/pro/). This dataset can be used to train models for detecting urgent messges related to maternal health. One such model is the [gemma-2-2b-it-ud](https://huggingface.co/IDinsight/gemma-2-2b-it-ud) model. The prompts used to generate the dataset are contained in the `prompts_for_generating_user_messages.py` module. ## Motivation Messages related to maternal health often contain sensitive/PII information. Thus, it is not always possible or preferable to send such data to APIs such as OpenAI models for analyses. In addition, maternal health messages from developing countries are difficult to obtain at scale, making training custom models difficult. To address both of these concerns, we created a synthetic dataset of user messages related to maternal health. This was accomplished by analyzing ~4k messages collected by [MomConnect](https://www.health.gov.za/momconnect/) in order to create a pool of user personas and message characteristics (e.g., average length of message, use of slangs/emojis, etc.). We then randomly sampled user personas and message characteristics and asked Gemini-1.5-Flash to generate a user message that aligns with a randomly chosen maternal health issue. Gemini-1.5-Pro was then used to verify that the generated message matches with the selected health issue. If a generated messages fails the verification check, it is excluded from the final dataset.--- license: MIT许可证 task_categories: - 文本分类 - 问答 - 零样本分类(Zero-shot Classification) - 文本生成 language: - 英语 tags: - 紧急情况检测 - 孕产妇健康 size_categories: - 10K<n<100K source_datasets: - 原创数据集 pretty_name: 孕产妇健康紧急情况检测 --- # 孕产妇健康紧急情况检测消息数据集 本数据集包含约1.28万条合成用户消息，相关内容涉及[紧急孕产妇警示信号](https://saferbirth.org/aim-resources/aim-cornerstones/urgent-maternal-warning-signs-2/)，这些消息通过[Gemini-1.5-Flash](https://deepmind.google/technologies/gemini/flash/)生成，并经由[Gemini-1.5-Pro](https://deepmind.google/technologies/gemini/pro/)验证。 本数据集可用于训练检测与孕产妇健康相关的紧急消息的模型，其中一款相关模型为[gemma-2-2b-it-ud](https://huggingface.co/IDinsight/gemma-2-2b-it-ud)。 用于生成本数据集的提示词包含在`prompts_for_generating_user_messages.py`模块中。 ## 研究动机 与孕产妇健康相关的消息通常包含敏感信息或个人可识别信息(Personal Identifiable Information, PII)，因此将此类数据发送至OpenAI模型等API进行分析并非始终可行或恰当。此外，发展中国家的孕产妇健康消息难以大规模获取，这为定制化模型的训练带来了挑战。 为解决上述两大问题，我们构建了一个孕产妇健康相关的用户消息合成数据集。具体实现流程如下：首先分析由[MomConnect](https://www.health.gov.za/momconnect/)收集的约4000条消息，以此构建用户角色与消息特征池（例如消息平均长度、俚语与表情符号使用习惯等）；随后随机采样用户角色与消息特征，要求Gemini-1.5-Flash生成符合随机选定的孕产妇健康问题的用户消息；最后使用Gemini-1.5-Pro验证生成的消息是否与选定的健康问题匹配，未通过验证的消息将被剔除出最终数据集。