multi-qa-MiniLM-L6-cos-v1

Maintainer: sentence-transformers

Total Score

102

Last updated 5/28/2024

👨‍🏫

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

Get summaries of the top AI models delivered straight to your inbox:

Model overview

The multi-qa-MiniLM-L6-cos-v1 is a sentence-transformers model that maps sentences and paragraphs to a 384-dimensional dense vector space. It was designed for semantic search, and has been trained on 215M (question, answer) pairs from diverse sources. Similar models include multi-qa-mpnet-base-dot-v1, which maps sentences to a 768-dimensional space, and all-MiniLM-L12-v2, a 384-dimensional model trained on over 1 billion sentence pairs.

Model inputs and outputs

Inputs

  • Text input, such as a sentence or paragraph

Outputs

  • A 384-dimensional dense vector representation of the input text

Capabilities

The multi-qa-MiniLM-L6-cos-v1 model is capable of encoding text into a semantic vector space, where documents with similar meanings are placed closer together. This allows it to be used for tasks like semantic search, where the model can find the most relevant documents for a given query.

What can I use it for?

The multi-qa-MiniLM-L6-cos-v1 model is well-suited for building semantic search applications, where users can search for relevant documents or passages based on the meaning of their queries, rather than just keyword matching. For example, you could use this model to build a FAQ search system, where users can find the most relevant answers to their questions.

Things to try

One interesting thing to try with this model is to use it as a feature extractor for other NLP tasks, such as text classification or clustering. The semantic vector representations produced by the model can provide powerful features that capture the meaning of the text, which may improve the performance of downstream models.



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

Related Models

🖼️

multi-qa-mpnet-base-dot-v1

sentence-transformers

Total Score

139

The multi-qa-mpnet-base-dot-v1 model is a sentence-transformers model that maps sentences and paragraphs to a 768-dimensional dense vector space. It was designed for semantic search tasks and trained on 215M (question, answer) pairs from diverse sources. This model can be compared to similar sentence-transformers models like all-mpnet-base-v2 and paraphrase-multilingual-mpnet-base-v2, which also aim to encode text into semantic representations. Model inputs and outputs Inputs Text**: The model takes text input, either a single sentence or a paragraph. Outputs Sentence embedding**: The model outputs a 768-dimensional dense vector representation of the input text that captures its semantic meaning. Capabilities The multi-qa-mpnet-base-dot-v1 model is capable of generating semantic embeddings of text that can be used for tasks like semantic search, clustering, and similarity scoring. The model's training on a large corpus of question-answer pairs gives it strong performance on question answering and retrieval tasks. What can I use it for? The semantic embeddings produced by the multi-qa-mpnet-base-dot-v1 model can be used in a variety of downstream applications. For example, you could use it to build a semantic search engine, where you encode user queries and document content, and then retrieve the most relevant documents based on cosine similarity. You could also use the embeddings as features for text classification or clustering tasks. Things to try One interesting thing to try with this model is to compare its performance on question answering tasks to other similar models like all-mpnet-base-v2 and paraphrase-multilingual-mpnet-base-v2. You could also experiment with different pooling strategies (e.g. mean, max, CLS token) to see how they affect the model's performance on your specific task.

Read more

Updated Invalid Date

🔎

all-MiniLM-L6-v2

sentence-transformers

Total Score

1.8K

The all-MiniLM-L6-v2 is a sentence-transformers model that maps sentences and paragraphs to a 384-dimensional dense vector space. This model can be used for tasks like clustering or semantic search. It was fine-tuned on a large dataset of over 1 billion sentence pairs using a contrastive learning objective. Similar models include the all-MiniLM-L12-v2, which has a deeper 12-layer architecture, and the all-mpnet-base-v2, which has a 768-dimensional output. Model inputs and outputs Inputs Text input, such as a single sentence or short paragraph Outputs A 384-dimensional vector representation of the input text Capabilities The all-MiniLM-L6-v2 model is capable of encoding text into a dense vector space that captures semantic information. This allows it to be used for tasks like semantic search, where you can find relevant documents for a given query, or clustering, where you can group similar text together. What can I use it for? The all-MiniLM-L6-v2 model can be useful for a variety of natural language processing tasks that involve understanding the meaning of text. Some potential use cases include: Semantic search**: Use the model to encode queries and documents, then find the most relevant documents for a given query by computing cosine similarity between the query and document embeddings. Text clustering**: Cluster documents or sentences based on their vector representations to group similar content together. Recommendation systems**: Encode user queries or items (e.g., products, articles) into the vector space and use the distances between them to make personalized recommendations. Data augmentation**: Generate new text samples by finding similar sentences in the vector space and making minor modifications. Things to try Some interesting things to try with the all-MiniLM-L6-v2 model include: Exploring the vector space**: Visualize the vector representations of different text inputs to get a sense of how the model captures semantic relationships. Zero-shot classification**: Use the model to encode text and labels, then classify new inputs by computing cosine similarity between the input and label embeddings. Multilingual applications**: The model can be used for cross-lingual tasks by encoding texts in different languages into the same vector space. Probing the model's capabilities**: Design targeted evaluation tasks to better understand the model's strengths and weaknesses in representing different types of semantic information.

Read more

Updated Invalid Date

🤯

all-MiniLM-L12-v2

sentence-transformers

Total Score

135

The all-MiniLM-L12-v2 is a sentence-transformers model that maps sentences and paragraphs to a 384 dimensional dense vector space. This model can be used for tasks like clustering or semantic search. Similar models include the all-mpnet-base-v2, a sentence-transformers model that maps sentences & paragraphs to a 768 dimensional dense vector space, and the paraphrase-multilingual-mpnet-base-v2, a multilingual sentence-transformers model. Model inputs and outputs Inputs Sentences or paragraphs of text Outputs 384 dimensional dense vector representations of the input text Capabilities The all-MiniLM-L12-v2 model can be used for a variety of natural language processing tasks that benefit from semantic understanding of text, such as clustering, semantic search, and information retrieval. It can capture the high-level meaning and context of sentences and paragraphs, allowing for more accurate matching and grouping of similar content. What can I use it for? The all-MiniLM-L12-v2 model is well-suited for applications that require semantic understanding of text, such as: Semantic search**: Use the model to encode queries and documents, then perform efficient nearest neighbor search to find the most relevant documents for a given query. Text clustering**: Cluster documents or paragraphs based on their semantic representations to group similar content together. Recommendation systems**: Encode items (e.g., articles, products) and user queries, then use the embeddings to find the most relevant recommendations. Things to try One interesting thing to try with the all-MiniLM-L12-v2 model is to experiment with different pooling methods (e.g., mean pooling, max pooling) to see how they impact the performance on your specific task. The choice of pooling method can significantly affect the quality of the sentence/paragraph representations, so it's worth trying out different approaches. Another idea is to fine-tune the model on your own dataset to further specialize the embeddings for your domain or application. The sentence-transformers library provides convenient tools for fine-tuning the model.

Read more

Updated Invalid Date

🤷

all-mpnet-base-v2

sentence-transformers

Total Score

700

The all-mpnet-base-v2 model is a sentence-transformer model developed by the sentence-transformers team. It maps sentences and paragraphs to a 768-dimensional dense vector space, making it useful for tasks like clustering or semantic search. This model performs well on a variety of language understanding tasks and can be easily used with the sentence-transformers library. It is a variant of the MPNet model, which combines the strengths of BERT and XLNet to capture both bidirectional and autoregressive information. Model inputs and outputs Inputs Text inputs can be individual sentences or paragraphs. Outputs The model produces a 768-dimensional dense vector representation for each input text. These vector embeddings can be used for downstream tasks like semantic search, text clustering, or text similarity measurement. Capabilities The all-mpnet-base-v2 model is capable of producing high-quality sentence embeddings that can capture the semantic meaning of text. These embeddings can be used to perform tasks like finding similar documents, clustering related texts, or retrieving relevant information from a large corpus. The model's performance has been evaluated on a range of benchmark tasks and demonstrates strong results. What can I use it for? The all-mpnet-base-v2 model is well-suited for a variety of natural language processing applications, such as: Semantic search**: Use the text embeddings to find the most relevant documents or passages given a query. Text clustering**: Group similar texts together based on their vector representations. Recommendation systems**: Suggest related content to users based on the similarity of text embeddings. Multi-modal retrieval**: Combine the text embeddings with visual features to build cross-modal retrieval systems. Things to try One key capability of the all-mpnet-base-v2 model is its ability to handle long-form text. Unlike many language models that are limited to short sequences, this model can process and generate embeddings for passages and documents up to 8,192 tokens in length. This makes it well-suited for tasks involving long-form content, such as academic papers, technical reports, or lengthy web pages. Another interesting aspect of this model is its potential for use in low-resource settings. The sentence-transformers team has developed a range of smaller, more efficient versions of the model that can be deployed on less powerful hardware, such as laptops or edge devices. This opens up opportunities to bring high-quality language understanding capabilities to a wider range of applications and users.

Read more

Updated Invalid Date