Sentence-transformers
Models by this creator
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all-MiniLM-L6-v2
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.
Updated 5/28/2024
🤷
all-mpnet-base-v2
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.
Updated 5/27/2024
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paraphrase-multilingual-MiniLM-L12-v2
492
The paraphrase-multilingual-MiniLM-L12-v2 model is a sentence-transformers model that maps sentences and paragraphs to a 384 dimensional dense vector space. It can be used for tasks like clustering or semantic search. This model is similar to other sentence-transformers models like paraphrase-MiniLM-L6-v2, paraphrase-multilingual-mpnet-base-v2, and paraphrase-xlm-r-multilingual-v1, which also map text to dense vector representations. Model inputs and outputs Inputs Text data, such as sentences or paragraphs Outputs A 384 dimensional vector representation of the input text Capabilities The paraphrase-multilingual-MiniLM-L12-v2 model can be used to generate vector representations of text that capture semantic information. These vector representations can then be used for tasks like clustering, semantic search, and other applications that require understanding the meaning of text. For example, you could use this model to find similar documents or articles based on their content, or to group together documents that discuss similar topics. What can I use it for? The paraphrase-multilingual-MiniLM-L12-v2 model can be used for a variety of natural language processing tasks, such as: Information retrieval**: Use the sentence embeddings to find similar documents or articles based on their content. Text clustering**: Group together documents that discuss similar topics by clustering the sentence embeddings. Semantic search**: Use the sentence embeddings to find relevant documents or articles based on the meaning of a query. You could incorporate this model into applications like search engines, recommendation systems, or content management systems to improve the user experience and surface more relevant information. Things to try One interesting thing to try with this model is to use it to generate embeddings for longer passages of text, such as articles or book chapters. The model can handle input up to 256 word pieces, so you could try feeding in larger chunks of text and see how the resulting embeddings capture the overall meaning and themes. You could then use these embeddings for tasks like document similarity or topic modeling. Another thing to try is to finetune the model on a specific domain or task, such as legal documents or medical literature. This could help the model better capture the specialized vocabulary and concepts in that domain, making it more useful for applications like search or knowledge management.
Updated 5/23/2024
⛏️
paraphrase-multilingual-mpnet-base-v2
254
The paraphrase-multilingual-mpnet-base-v2 model is a sentence-transformers model that maps sentences and paragraphs to a 768-dimensional dense vector space. It can be used for a variety of tasks like clustering or semantic search. This model is multilingual and was trained on a large dataset of over 1 billion sentence pairs across languages like English, Chinese, and German. The model is similar to other sentence-transformers models like all-mpnet-base-v2 and jina-embeddings-v2-base-en, which also provide general-purpose text embeddings. Model inputs and outputs Inputs Text input, either a single sentence or a paragraph Outputs A 768-dimensional vector representing the semantic meaning of the input text Capabilities The paraphrase-multilingual-mpnet-base-v2 model is capable of producing high-quality text embeddings that capture the semantic meaning of the input. These embeddings can be used for a variety of natural language processing tasks like text clustering, semantic search, and document retrieval. What can I use it for? The text embeddings produced by this model can be used in many different applications. For example, you could use the embeddings to build a semantic search engine, where users can search for relevant documents by typing in a query. The model would generate embeddings for the query and the documents, and then find the most similar documents based on the cosine similarity between the query and document embeddings. You could also use the embeddings for text clustering, where you group together documents that have similar semantic meanings. This could be useful for organizing large collections of documents or identifying related content. Additionally, the multilingual capabilities of this model make it well-suited for applications that need to handle text in multiple languages, such as international customer support or cross-border e-commerce. Things to try One interesting thing to try with this model is to use it for cross-lingual text retrieval. Since the model produces embeddings in a shared semantic space, you can use it to find relevant documents in a different language than the query. For example, you could search for English documents using a French query, or vice versa. Another interesting application is to use the embeddings as features for downstream machine learning models, such as sentiment analysis or text classification. The rich semantic information captured by the model can help improve the performance of these types of models.
Updated 5/27/2024
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LaBSE
157
LaBSE is a multilingual sentence embedding model developed by the sentence-transformers team. It can map sentences in 109 different languages to a shared vector space, allowing for cross-lingual tasks like clustering or semantic search. Similar models developed by the sentence-transformers team include the paraphrase-multilingual-mpnet-base-v2, paraphrase-multilingual-MiniLM-L12-v2, paraphrase-xlm-r-multilingual-v1, and paraphrase-MiniLM-L6-v2. These models all map text to dense vector representations, enabling applications like semantic search and text clustering. Model inputs and outputs Inputs Sentences or paragraphs**: The model takes in text as input and encodes it into a dense vector representation. Outputs Sentence embeddings**: The model outputs a 768-dimensional vector representation for each input sentence or paragraph. These vectors capture the semantic meaning of the text and can be used for downstream tasks. Capabilities The LaBSE model can be used to encode text in 109 different languages into a shared vector space. This allows for cross-lingual applications, such as finding semantically similar documents across languages or clustering multilingual corpora. The model was trained on a large dataset of over 1 billion sentence pairs, giving it robust performance on a variety of text understanding tasks. What can I use it for? The LaBSE model can be used for a variety of natural language processing tasks that benefit from multilingual sentence embeddings, such as: Semantic search**: Find relevant documents or passages across languages based on the meaning of the query. Text clustering**: Group together similar documents or webpages in a multilingual corpus. Paraphrase identification**: Detect when two sentences in different languages express the same meaning. Machine translation evaluation**: Assess the quality of machine translations by comparing the embeddings of the source and target sentences. Things to try One interesting aspect of the LaBSE model is its ability to encode text from over 100 languages into a shared vector space. This opens up possibilities for cross-lingual applications that wouldn't be possible with monolingual models. For example, you could try using LaBSE to find semantically similar documents across languages. This could be useful for tasks like multilingual information retrieval or machine translation quality evaluation. You could also experiment with using the model's embeddings for multilingual text clustering or classification tasks. Another interesting direction would be to fine-tune the LaBSE model on specialized datasets or tasks to see if you can improve performance on certain domains or applications. The sentence-transformers team has released several other models that build on the base LaBSE architecture, which could serve as inspiration.
Updated 5/27/2024
🖼️
multi-qa-mpnet-base-dot-v1
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.
Updated 5/27/2024
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all-MiniLM-L12-v2
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.
Updated 5/27/2024
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distiluse-base-multilingual-cased-v2
135
The distiluse-base-multilingual-cased-v2 is a sentence-transformers model that maps sentences and paragraphs to a 512-dimensional dense vector space. It can be used for tasks like clustering or semantic search. This model is similar to other sentence-transformers models like distiluse-base-multilingual-cased-v1, paraphrase-multilingual-mpnet-base-v2, paraphrase-multilingual-MiniLM-L12-v2, and paraphrase-xlm-r-multilingual-v1, all of which were developed by the sentence-transformers team. Model inputs and outputs Inputs Text**: The model accepts text inputs, such as sentences or paragraphs. Outputs Sentence embeddings**: The model outputs 512-dimensional dense vector representations of the input text. Capabilities The distiluse-base-multilingual-cased-v2 model can be used to encode text into semantic representations that capture the meaning and context of the input. These sentence embeddings can then be used for a variety of natural language processing tasks, such as information retrieval, text clustering, and semantic similarity analysis. What can I use it for? The sentence embeddings generated by this model can be used in a wide range of applications. For example, you could use the model to build a semantic search engine, where users can search for relevant content by providing a natural language query. The model could also be used to cluster similar documents or paragraphs, which could be useful for organizing large corpora of text data. Things to try One interesting thing to try with this model is to experiment with different pooling strategies for generating the sentence embeddings. The model uses mean pooling by default, but you could also try max pooling or other techniques to see how they affect the performance on your specific task. Additionally, you could try fine-tuning the model on your own dataset to adapt it to your domain-specific needs.
Updated 5/28/2024
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clip-ViT-B-32-multilingual-v1
111
The clip-ViT-B-32-multilingual-v1 model is a multi-lingual version of the OpenAI CLIP-ViT-B32 model, developed by the sentence-transformers team. This model can map text in over 50 languages and images to a shared dense vector space, allowing for tasks like image search and multi-lingual zero-shot image classification. It is similar to other CLIP-based models like clip-vit-base-patch32 that also aim to learn a joint text-image representation. Model inputs and outputs Inputs Text**: The model can take text inputs in over 50 languages. Images**: The model can also take image inputs, which it encodes using the original CLIP-ViT-B-32 image encoder. Outputs Embeddings**: The model outputs dense vector embeddings for both the text and images, which can be used for tasks like semantic search and zero-shot classification. Capabilities The clip-ViT-B-32-multilingual-v1 model is capable of mapping text and images from diverse sources into a shared semantic vector space. This allows it to perform tasks like finding relevant images for a given text query, or classifying images into categories defined by text labels, even for languages the model wasn't explicitly trained on. What can I use it for? The primary use cases for this model are image search and multi-lingual zero-shot image classification. For example, you could use it to search through a large database of images to find the ones most relevant to a text query, or to classify new images into categories defined by text labels, all while supporting multiple languages. Things to try One interesting thing to try with this model is to experiment with the multilingual capabilities. Since it can map text and images from over 50 languages into a shared space, you could explore how well it performs on tasks that involve mixing languages, such as searching for images using queries in a different language than the image captions. This could reveal interesting insights about the model's cross-lingual generalization abilities.
Updated 5/28/2024
👨🏫
multi-qa-MiniLM-L6-cos-v1
102
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.
Updated 5/28/2024