使用基础模型加速 NLP 应用程序开发：Pixability 案例研究
技术
- 分析与建模 - 机器学习
- 分析与建模 - 自然语言处理 (NLP)
适用行业
- 水泥
- 教育
适用功能
- 产品研发
- 仓库和库存管理
用例
- 对话机器人
- 虚拟培训
服务
- 数据科学服务
- 培训
关于客户
Pixability 是一家数据和技术公司，帮助广告商准确定位 YouTube 上的正确内容和受众。他们使用机器学习自动识别 YouTube 内容并对其进行分类，帮助广告商通过合适的内容最大限度地扩大覆盖范围并优化广告支出。 Pixability 的服务对于希望在 YouTube 上扩大影响力的品牌至关重要，YouTube 是一个观看者每天观看超过 7 亿小时内容的平台。通过提供有关内容是否适合品牌定位的详细见解，Pixability 可以帮助广告商确保他们的广告被正确的受众看到，从而提高视频广告支出的回报。
挑战
Pixability 是一家数据和技术公司，为广告商提供准确定位 YouTube 上的内容和受众的能力。然而，由于每天观看的 YouTube 内容超过 7 亿小时，Pixability 面临着持续准确地对数十亿视频进行分类以确保广告在适合品牌的内容上投放的挑战。他们现有的用于视频分类的自然语言处理 (NLP) 模型的性能不够强。由于依赖需要多次迭代的外部数据标记服务，为机器学习解决方案标记训练数据的过程很慢。由于时间有限，领域专家和数据科学家必须解决不明确的标签，因此协作受到限制。此外，标题、描述、内容和标签中的有价值信息很难标准化。
解决方案
Pixability 采用 Snorkel Flow 以数据为中心的基础模型开发工作流程来构建 NLP 应用程序，所用时间比第三方数据标记服务标记单个数据集所需的时间要短。此工作流程使 Pixability 能够将可分类的类别数量扩大到 600 多个，同时还将模型准确率提高到 90% 以上。该团队使用 Snorkel Flow 的基础模型热启动和零样本学习来快速启动训练数据创建。然后，他们使用 Foundation Model Prompt Builder 来开发和完善提示，以纠正开箱即用的 FM 错误，并从各种 FM 中提取更多特定领域的知识。他们创建了提示，要求 FM 根据描述对视频进行分类。这种使用基础模型中的知识来标记数据的编程方法生成了 500,000 个标记的训练数据点，这些数据点用于训练模型，准确率高达 90%。该团队还能够解锁多标签 NLP 功能，为视频提供更具体的分类。
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