instructor-xl

Maintainer: hkunlp

Total Score

528

Last updated 5/28/2024

🌐

PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

The instructor-xl model is an instruction-finetuned text embedding model developed by hkunlp. It can generate text embeddings tailored to any task or domain by simply providing the task instruction, without any finetuning. The model achieves state-of-the-art performance on 70 diverse embedding tasks, and can be used with a customized sentence-transformer library.

Similar models include Mistral-7B-Instruct-v0.1, all-mpnet-base-v2, Falcon-7B-Instruct, and Mistral-7B-Instruct-v0.2. These models also provide instruction-based text embeddings, with varying architectures and training approaches.

Model inputs and outputs

Inputs

  • Text instructions: The instructor-xl model takes text instructions as input, which specify the task, domain, and objective for the desired text embeddings.

Outputs

  • Task-specific text embeddings: The model outputs text embeddings tailored to the provided instruction, which can be used for a variety of downstream tasks such as classification, retrieval, clustering, and text evaluation.

Capabilities

The instructor-xl model can generate high-quality text embeddings for a wide range of tasks and domains, simply by providing a task instruction. This allows for rapid customization and deployment of text embedding models without the need for finetuning. The model's strong performance on 70 diverse embedding tasks showcases its versatility and robustness.

What can I use it for?

The instructor-xl model can be used in a variety of applications that require text embeddings, such as information retrieval, content classification, and text clustering. By providing task-specific instructions, users can easily generate embeddings tailored to their particular use case, without the need for extensive finetuning or model retraining.

For example, you could use the model to generate domain-specific embeddings for scientific articles, financial reports, or medical records. This could enable more accurate clustering, recommendation, or search functionality for these specialized text corpora.

Things to try

One interesting aspect of the instructor-xl model is its ability to generate text embeddings without any finetuning. This allows for rapid prototyping and experimentation with different tasks and domains. You could try providing instructions for a wide variety of use cases, such as "Represent the Finance topic for classification" or "Encode the Medical document for retrieval", and see how the model performs on your specific needs.

Additionally, you could explore the model's capabilities by providing more complex or open-ended instructions, such as "Represent the key points of the given text for summarization" or "Encode the text to capture the author's sentiment and tone." Observing how the model responds to these types of instructions can provide valuable insights into its strengths and limitations.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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