instructor base EU AI Act Compliance Profile

hkunlp/instructor-base

We introduce Instructor👨‍🏫, an instruction-finetuned text embedding model that can generate text embeddings tailored to any task (e.g., classification, retrieval, clustering, text evaluation, etc.) and domains (e.g., science, finance, etc.) by simply providing the task instruction, without any finetuning. Instructor👨‍ achieves sota on 70 diverse embedding tasks! The model is easy to use with our customized sentence-transformer library. For more details, check out our paper and project page!

**************************** Updates ****************************

• 01/21: We released a new checkpoint trained with hard negatives, which gives better performance.
• 12/21: We released our paper, code, checkpoint and project page! Check them out!

Quick start

Installation

pip install InstructorEmbedding

Compute your customized embeddings

Then you can use the model like this to calculate domain-specific and task-aware embeddings:

from InstructorEmbedding import INSTRUCTOR
model = INSTRUCTOR('hkunlp/instructor-base')
sentence = "3D ActionSLAM: wearable person tracking in multi-floor environments"
instruction = "Represent the Science title:"
embeddings = model.encode([[instruction,sentence]])
print(embeddings)

Use cases

Calculate embeddings for your customized texts

If you want to calculate customized embeddings for specific sentences, you may follow the unified template to write instructions:

                          Represent the domain `t

Your obligations as a instructor base deployer

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