Hkunlp

Models by this creator

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528

instructor-xl

hkunlp

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.

Updated 5/28/2024

Text-to-Text

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459

instructor-large

hkunlp

The instructor-large model is an instruction-finetuned text embedding model developed by hkunlp. It can generate text embeddings tailored to any task (e.g., classification, retrieval, clustering, text evaluation) and domains (e.g., science, finance) by simply providing the task instruction, without any finetuning. The model achieves state-of-the-art performance on 70 diverse embedding tasks according to the MTEB leaderboard. Similar models include the instructor-xl and the Mistral-7B-Instruct-v0.1, Mistral-7B-Instruct-v0.2, and Mixtral-8x22B-Instruct-v0.1 models from Mistral AI. These models also leverage instruction-based finetuning to generate task-specific and domain-specific text embeddings. Model Inputs and Outputs The instructor-large model takes in a combination of an instruction and a sentence or paragraph of text. The instruction specifies the task, domain, and objective for the text embedding. The model then outputs a 768-dimensional vector representing the text, tailored to the provided instruction. Inputs Instruction**: A natural language instruction that specifies the task, domain, and objective for the text embedding. For example: "Represent the Science title: 3D ActionSLAM: wearable person tracking in multi-floor environments" Text**: A sentence or paragraph of text to be encoded. Outputs Text Embedding**: A 768-dimensional vector representing the input text, tailored to the provided instruction. Capabilities The instructor-large model can generate high-quality, task-specific and domain-specific text embeddings without any additional finetuning. This makes it a powerful tool for a variety of NLP applications, such as information retrieval, text classification, and clustering. For example, you could use the model to generate embeddings for science paper titles that are optimized for a retrieval task, or to generate embeddings for financial statements that are optimized for a sentiment analysis task. What Can I Use It For? The instructor-large model's ability to generate customized text embeddings on-the-fly makes it a versatile tool for a wide range of NLP projects. Some potential use cases include: Information Retrieval**: Use the model to generate embeddings for your corpus and query texts, then perform efficient semantic search and document retrieval. Text Classification**: Generate domain-specific and task-specific embeddings to train high-performing text classification models. Clustering and Segmentation**: Use the model's embeddings to group related documents or identify coherent segments within longer texts. Text Evaluation**: Generate embeddings tailored to specific evaluation metrics, such as coherence or sentiment, to assess the quality of generated text. Things to Try One interesting aspect of the instructor-large model is its ability to generate embeddings that are tailored to specific tasks and domains. This allows you to leverage the model's sophisticated language understanding capabilities for a wide variety of applications, without the need for extensive finetuning. For example, you could try using the model to generate embeddings for scientific papers that are optimized for retrieving relevant background information, or to generate embeddings for financial reports that are optimized for detecting anomalies or trends. By crafting the instruction carefully, you can unlock the model's potential to extract the most relevant information for your specific use case. Another interesting direction to explore would be using the instructor-large model as a starting point for further finetuning. Since the model has already been trained on a large and diverse set of text data, it may be able to achieve strong performance on your specific task with only a modest amount of additional finetuning.

Updated 5/28/2024

Text-to-Text

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107

instructor-base

hkunlp

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.

Updated 5/23/2024

Text-to-Text