SAsamcodes·May 3, 2021Submission
Behavioral Testing of NLP Models with CheckList
2 points · Open discussionmarcotcr
checklist
Jupyter Notebook2020–2021
marcotcr / checklist
checklist
Beyond Accuracy: Behavioral Testing of NLP models with CheckList
2Distinct people
2Mentions
0Explicit recommenders
2.1KGitHub stars
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What it is
Beyond Accuracy: Behavioral Testing of NLP models with CheckList
GitHub
Where it is used
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What people are saying
No recommendation phrase was found in the indexed comments.
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Also in these discussions
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This week on Hacker News
Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 1:49:46 PM UTC.
No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.
Mentions over time
HN mentions by week2 dated mentions
Observed mentions: 2020-10-15 06:43:21 UTC to 2021-05-03 02:07:32 UTC. Weeks start Monday, UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| Oct 12 | 2020-10-12 00:00:00 UTC | 2020-10-19 00:00:00 UTC | 1 | 0 | 1 |
| Oct 19 | 2020-10-19 00:00:00 UTC | 2020-10-26 00:00:00 UTC | 0 | 0 | 0 |
| Oct 26 | 2020-10-26 00:00:00 UTC | 2020-11-02 00:00:00 UTC | 0 | 0 | 0 |
| Nov 2 | 2020-11-02 00:00:00 UTC | 2020-11-09 00:00:00 UTC | 0 | 0 | 0 |
| Nov 9 | 2020-11-09 00:00:00 UTC | 2020-11-16 00:00:00 UTC | 0 | 0 | 0 |
| Nov 16 | 2020-11-16 00:00:00 UTC | 2020-11-23 00:00:00 UTC | 0 | 0 | 0 |
| Nov 23 | 2020-11-23 00:00:00 UTC | 2020-11-30 00:00:00 UTC | 0 | 0 | 0 |
| Nov 30 | 2020-11-30 00:00:00 UTC | 2020-12-07 00:00:00 UTC | 0 | 0 | 0 |
| Dec 7 | 2020-12-07 00:00:00 UTC | 2020-12-14 00:00:00 UTC | 0 | 0 | 0 |
| Dec 14 | 2020-12-14 00:00:00 UTC | 2020-12-21 00:00:00 UTC | 0 | 0 | 0 |
| Dec 21 | 2020-12-21 00:00:00 UTC | 2020-12-28 00:00:00 UTC | 0 | 0 | 0 |
| Dec 28 | 2020-12-28 00:00:00 UTC | 2021-01-04 00:00:00 UTC | 0 | 0 | 0 |
| Jan 4 | 2021-01-04 00:00:00 UTC | 2021-01-11 00:00:00 UTC | 0 | 0 | 0 |
| Jan 11 | 2021-01-11 00:00:00 UTC | 2021-01-18 00:00:00 UTC | 0 | 0 | 0 |
| Jan 18 | 2021-01-18 00:00:00 UTC | 2021-01-25 00:00:00 UTC | 0 | 0 | 0 |
| Jan 25 | 2021-01-25 00:00:00 UTC | 2021-02-01 00:00:00 UTC | 0 | 0 | 0 |
| Feb 1 | 2021-02-01 00:00:00 UTC | 2021-02-08 00:00:00 UTC | 0 | 0 | 0 |
| Feb 8 | 2021-02-08 00:00:00 UTC | 2021-02-15 00:00:00 UTC | 0 | 0 | 0 |
| Feb 15 | 2021-02-15 00:00:00 UTC | 2021-02-22 00:00:00 UTC | 0 | 0 | 0 |
| Feb 22 | 2021-02-22 00:00:00 UTC | 2021-03-01 00:00:00 UTC | 0 | 0 | 0 |
| Mar 1 | 2021-03-01 00:00:00 UTC | 2021-03-08 00:00:00 UTC | 0 | 0 | 0 |
| Mar 8 | 2021-03-08 00:00:00 UTC | 2021-03-15 00:00:00 UTC | 0 | 0 | 0 |
| Mar 15 | 2021-03-15 00:00:00 UTC | 2021-03-22 00:00:00 UTC | 0 | 0 | 0 |
| Mar 22 | 2021-03-22 00:00:00 UTC | 2021-03-29 00:00:00 UTC | 0 | 0 | 0 |
| Mar 29 | 2021-03-29 00:00:00 UTC | 2021-04-05 00:00:00 UTC | 0 | 0 | 0 |
| Apr 5 | 2021-04-05 00:00:00 UTC | 2021-04-12 00:00:00 UTC | 0 | 0 | 0 |
| Apr 12 | 2021-04-12 00:00:00 UTC | 2021-04-19 00:00:00 UTC | 0 | 0 | 0 |
| Apr 19 | 2021-04-19 00:00:00 UTC | 2021-04-26 00:00:00 UTC | 0 | 0 | 0 |
| Apr 26 | 2021-04-26 00:00:00 UTC | 2021-05-03 00:00:00 UTC | 0 | 0 | 0 |
| May 3 | 2021-05-03 00:00:00 UTC | 2021-05-10 00:00:00 UTC | 1 | 1 | 0 |
What Hacker News says
2 sourcesIt's sad to see such "to hell with it" naivete (and the comments here with broad supporting evidence that data scientists are poor software developers.
There are also a lot of DS people wrestling with the deeper challenges at the heart of MLOps and beyond just advocating sane team-based workflows, are looking at ways to harden and structure ML specific processes in the planning, design, build, and deploy phases.
Sean Taylor at Lyft on choosing metrics - https://medium.com/@seanjtaylor/designing-and-evaluating-met...
Test coverage of ML models and a good discussion on why and how ML code testing isn't the same as traditional testing - https://www.jeremyjordan.me/testing-ml/
A tool for Behavioral testing of NLP models - https://github.com/marcotcr/checklist
How to be a 10x data scientist (spoiler: have a professional end to end workflow) -
Read the rest
https://towardsdatascience.com/how-to-be-a-10x-data-scientis...
Ml-specific technical debt, how to measure it, some ideas on how to handle it, this paper has spawned a lot of great tools - http://papers.nips.cc/paper/5656-hidden-technical-debt-in-ma...
Adversarial instance and outlier detection - https://github.com/SeldonIO/alibi-detect
And of course a whole suite of tools and research beingn done on data, pipeline, and model management, optimizing deployment and serving, explainability and interpretability, all in the service of applying enterprise-ready, engineering-grade rigor to data science.
And then you read something like this and want to tear your hair out.