Add agent example use case to generate query, positive and negative examples #451
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Description
Add an agent example use case to generate query, positive and negative docs example which can be used by text embedding model (text encoder) contrastive learning.
Motivation and Context
Recently there is a good paper [link] published which uses "agent" to generate tasks and corresponding query, positive and (hard) negative document examples. These document examples can then be used for text embedding model finetuning (contrastive learning). Text encoders use this method achieve quite good text embedding performance on the MTEB leaderboard, including SFR-Embedding-Mistral and e5-mistral-7b-instruct etc.
The whole generation includes two steps.
One is task generation:
Another one is document generation:
Types of changes
What types of changes does your code introduce? Put an
x
in all the boxes that apply:Implemented Tasks
Checklist
Go over all the following points, and put an
x
in all the boxes that apply.If you are unsure about any of these, don't hesitate to ask. We are here to help!