I made a dataset for finetuning embedding models

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I made a STSB alternatives, but with dialog/assistant samples. I couldn't find anything similar online (!), so I built it. The reason I did it was because I needed a very small model that would work well with my React component, and none of the existing 17M models performed adequately. The one I created with this dataset does. Embedding models, like other types of models, can be task-specific, and I didn't have any officially recognized task for my needs. The closest is the "sentence similarity" task, but one of the most recognized benchmark for it is STSB and I find STSB to be quite strange. Here is a 5 out of 5 scored example from STSB: "A person cuts an onion." and "A person is cutting an onion." Here is a 1 out of 5 scored example from STSB: "A man is playing the flute" and "A man is playing the guitar". STSB isn't what I need for my "real world" task. What I need is a way to find best paragraphs that are answers for the question the user asks. This is why I made that dataset and this is why I fine-tuned an embedding model. It was a fun experience and the model works really well! :)

Visit product View on Hacker NewsApr 28, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, user, models · Missing: mac, agents, macos

82%82% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: created, para · Missing: supports, reddit linkedin, podcasting

80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, io · Missing: https docs, excited, just released

61%61% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

57%57% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: answers, way, para · Missing: mobile apps, ios, personal

55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: real world · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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