Jev模型在文档处理任务中表现优异
LlamaIndex发布了Jev模型，专为需要快速决策的文档任务设计，在多项基准测试中表现领先。
Jev模型在文档方向检测、语言识别、分类和分割等任务中进行了基准测试。该模型在准确率、成本和延迟三个维度上超越了其他开源模型。测试包括通用分类器和特定文档处理模型的对比。
System one models are extremely useful for a variety of document tasks that require fast decisions: * orientation detection * language detection * classification * splitting We benchmarked Jev with other OSS models (some general classifiers, some document-specific) against a variety of these tasks. We measured accuracy, cost, and latency. Jev tops most of the benchmarks here across the 3 dimensions. We're excited to use it and learn from it to continue advancing the frontiers of doc understanding! YT: youtube.com/watch?v=MLZ1dU… Repo: github.com/run-llama/jev_… Your browser does not support the video tag. 🔗 View on Twitter LlamaIndex 🦙 @llama_index Document parsing requires a lot of on-the-fly decision making. We explored the early approaches to Jev and Jev-like models on several documents tasks like orientation detection, language detection, routing, and more. youtube.com/watch?v=MLZ1dU… 🔗 View Quoted Tweet 💬 0 🔄 1 ❤️ 7 👀 747 📊 1 ⚡
