instructor-base

Maintainer: hkunlp

Total Score

107

Last updated 5/23/2024

⛏️

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-base model from hkunlp is an instruction-finetuned text embedding model that can generate text embeddings tailored to any task or domain by simply providing a task instruction, without any finetuning. Compared to similar models like instructor-xl and instructor-large, the instructor-base model is a more compact version that still achieves state-of-the-art performance on 70 diverse embedding tasks.

Model inputs and outputs

The instructor-base model takes in a sentence or paragraph of text and an instruction that describes the desired task or domain. It then outputs a customized text embedding that is optimized for that specific task or domain. This allows users to tailor the embeddings to their needs without having to perform any additional finetuning.

Inputs

  • Text: A sentence or paragraph of text to be encoded
  • Instruction: A natural language instruction that describes the desired task or domain for the text embedding

Outputs

  • Text embedding: A 512-dimensional vector representation of the input text, customized to the provided instruction

Capabilities

The instructor-base model can generate high-quality text embeddings for a wide variety of tasks and domains, including classification, retrieval, clustering, and text evaluation. By simply providing an instruction like "Represent the Science title:", the model can produce embeddings that are optimized for scientific text. This flexibility allows users to adapt the model to their specific needs without any additional training.

What can I use it for?

The instructor-base model can be used in a variety of natural language processing applications that require customized text embeddings. For example, you could use it for information retrieval, where you provide a query instruction like "Represent the Wikipedia question for retrieving supporting documents:" and the model generates embeddings that are well-suited for that task. You could also use it for text clustering, where you provide instructions like "Represent the Medicine sentence for clustering:" to group similar scientific texts together.

Things to try

One interesting thing to try with the instructor-base model is to experiment with different instructions to see how the generated embeddings change. For example, you could compare the embeddings produced for the instructions "Represent the Science title:" and "Represent the Finance statement:" to see how the model captures the semantic differences between scientific and financial text. This can give you insights into the model's capabilities and help you tailor it to your specific use cases.



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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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.

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