A guided series of tutorials/notebooks to build a PDF to Podcast workflow using Llama models for text processing, transcript writing, dramatization, and text-to-speech conversion.
OpenLogParser, an unsupervised log parsing approach using open-source LLMs, improves accuracy, privacy, and cost-efficiency in large-scale data processing.
Approach:
- Log grouping: Clusters logs based on shared syntactic features.
- Unsupervised LLM-based parsing: Uses retrieval-augmented approach to separate static and dynamic components.
- Log template memory: Stores parsed templates for future use, minimizing LLM queries.
Results:
- Processes logs 2.7 times faster than other LLM-based parsers.
- Improves average parsing accuracy by 25% over existing parsers.
- Handles over 50 million logs from the LogHub-2.0 dataset.
- Achieves high grouping accuracy (87.2%) and parsing accuracy (85.4%).
- Outperforms other state-of-the-art parsers like LILAC and LLMParserT5Base in processing speed and accuracy.
Approach:
- Log grouping: Clusters logs based on shared syntactic features.
- Unsupervised LLM-based parsing: Uses retrieval-augmented approach to separate static and dynamic components.
- Log template memory: Stores parsed templates for future use, minimizing LLM queries.
Results:
- Processes logs 2.7 times faster than other LLM-based parsers.
- Improves average parsing accuracy by 25% over existing parsers.
- Handles over 50 million logs from the LogHub-2.0 dataset.
- Achieves high grouping accuracy (87.2%) and parsing accuracy (85.4%).
- Outperforms other state-of-the-art parsers like LILAC and LLMParserT5Base in processing speed and accuracy.
The TextWrapper class provides functionality for wrapping long pieces of text into multiple shorter lines while preserving the initial and subsequent indents.
