NVIDIA / NVIDIA/NeMo-Retriever
[FEA]: Add nightly accuracy testing for text, image, table, and chart extraction
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 3k
- Forks
- 349
- Avg merge
- 1d 23h
- Merged PRs (30d)
- 116
Description
Is this a new feature, an improvement, or a change to existing functionality?
New Feature
How would you describe the priority of this feature request
Currently preventing usage
Please provide a clear description of problem this feature solves
Implement a nightly testing pipeline to evaluate the accuracy and performance of our extraction processes for various document types. The testing should focus on extracting text, images, tables, and charts, and should cover multiple formats including jpeg, png, svg, jpg, docx, pptx, txt, pdf, and subsets of HTML.
Describe the feature, and optionally a solution or implementation and any alternatives
-
Capture Performance Metrics
- Measure and capture time to completion for each document type.
- Record pages per second throughput for each document type.
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Generate Accuracy Metrics
- Calculate F1 scores for text, image, table, and chart extraction for each document type.
- Calculate Recall for text, image, table, and chart extraction for each document type.
- Calculate Precision for text, image, table, and chart extraction for each document type.
Additional context
No response
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
No files, tests, or entry points are named in the issue. Start by locating the existing extraction and testing infrastructure, then determine how the listed document formats and extraction types can be evaluated nightly; done means the pipeline records throughput, completion time, F1, recall, and precision.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- ci-cd, data-engineering, testing
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100