instructor-embedding
One Embedder, Any Task Instruction-Finetuned Text Embeddings
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Decision brief
instructor-embedding: ACL 2023 solution for generating instruction-finetuned text embeddings suitable for various NLP applications.
Good fit when
- For tasks requiring contextual understanding through instructions, like interactive systems
- In information retrieval scenarios where relevance and context matter
Avoid when
- When simple keyword matching or non-contextual semantic analysis is sufficient
- If the application requires embeddings trained on very specific domain data not covered by generic instruction-finetuning
Observed Jul 14, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Dormant (583d since push)
- As of 1d
- Provenance
- Not a fork · Organization account
- As of 1d
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install instructor-embeddingSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Provides instruction-finetuned text embeddings for various natural language processing tasks including information retrieval and text evaluation.
Capability facts
- Languages
- python
Source: github.language · Aug 22, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 22, 2026)
conda env create -n instructor python=3.7Source link
Tags
README
Installation
It is very easy to use INSTRUCTOR for any text embeddings. You can easily try it out in Colab notebook. In your local machine, we recommend to first create a virtual environment:
conda env create -n instructor python=3.7
git clone https://github.com/HKUNLP/instructor-embedding
pip install -r requirements.txt
That will create the environment instructor we used. To use the embedding tool, first install the InstructorEmbedding package from PyPI
pip install InstructorEmbedding
or directly install it from our code
pip install -e .
Getting Started
First download a pretrained model (See model list for a full list of available models)
from InstructorEmbedding import INSTRUCTOR
model = INSTRUCTOR('hkunlp/instructor-large')
Then provide the sentence and customized instruction to the model.
For agents
This page has a .md twin and JSON over the API.