Intfloat
Models by this creator
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multilingual-e5-large
594
The multilingual-e5-large model is a large-scale multilingual text embedding model developed by the researcher intfloat. It is based on the XLM-RoBERTa-large model and has been continually trained on a mixture of multilingual datasets. The model supports 100 languages but may see performance degradation on low-resource languages. Model inputs and outputs Inputs Text**: The input can be a query or a passage, denoted by the prefixes "query:" and "passage:" respectively. Even for non-English text, the prefixes should be used. Outputs Embeddings**: The model outputs 768-dimensional text embeddings that capture the semantic information of the input text. The embeddings can be used for tasks like information retrieval, clustering, and similarity search. Capabilities The multilingual-e5-large model is capable of encoding text in 100 different languages. It can be used to generate high-quality text embeddings that preserve the semantic information of the input, making it useful for a variety of natural language processing tasks. What can I use it for? The multilingual-e5-large model can be used for tasks that require understanding and comparing text in multiple languages, such as: Information retrieval**: The text embeddings can be used to find relevant documents or passages for a given query, even across languages. Semantic search**: The embeddings can be used to identify similar text, enabling applications like recommendation systems or clustering. Multilingual text analysis**: The model can be used to analyze and compare text in different languages, for use cases like market research or cross-cultural studies. Things to try One interesting aspect of the multilingual-e5-large model is its ability to handle low-resource languages. While the model supports 100 languages, it may see some performance degradation on less commonly-used languages. Developers could experiment with using the model for tasks in these low-resource languages and observe its effectiveness compared to other multilingual models.
Updated 5/28/2024
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e5-mistral-7b-instruct
408
The e5-mistral-7b-instruct model is a large language model developed by the researcher intfloat. It is based on the E5 text embedding model and has been instruct fine-tuned, giving it the ability to understand and respond to natural language instructions. This model is similar to other instruct-tuned models like the multilingual-e5-large and multilingual-e5-base models, also developed by intfloat. These models leverage large pretraining datasets and fine-tuning on various text tasks to create powerful text understanding and generation capabilities. Model Inputs and Outputs The e5-mistral-7b-instruct model takes in text prompts and generates relevant text responses. The input prompts can include instructions, questions, or other natural language text. The model outputs are coherent, contextually appropriate text continuations. Inputs Freeform text prompts**: The model accepts any natural language text as input, such as instructions, questions, or descriptions. Outputs Generated text**: The model produces relevant, coherent text responses based on the input prompts. The output text can range from short phrases to multi-sentence paragraphs. Capabilities The e5-mistral-7b-instruct model excels at understanding and responding to natural language instructions. It can handle a wide variety of tasks, from answering questions to generating creative writing. Some example capabilities of the model include: Answering questions and providing factual information Generating summaries and abstracting key points from text Proposing solutions to open-ended problems Engaging in freeform dialogue and maintaining context Providing step-by-step instructions for completing tasks The model's broad knowledge base and language understanding make it a versatile tool for many text-based applications. What Can I Use It For? The e5-mistral-7b-instruct model could be leveraged in a variety of projects and applications, such as: Virtual assistants**: The model's conversational and instructional capabilities make it well-suited for building intelligent virtual assistants that can engage in natural language interactions. Content generation**: The model can be fine-tuned or prompted to generate high-quality text for applications like article writing, creative storytelling, and summarization. Educational tools**: The model's ability to provide step-by-step instructions and explanations could be useful for developing interactive learning experiences and online tutoring systems. Research and analysis**: Researchers could leverage the model's text understanding abilities to build tools for text mining, topic modeling, and information extraction. To get started, you can find example code for using the e5-mistral-7b-instruct model in the intfloat/e5-mistral-7b-instruct model page. Things to Try One interesting aspect of the e5-mistral-7b-instruct model is its ability to engage in open-ended dialogue and adapt its responses to the context of the conversation. You could try prompting the model with a series of back-and-forth exchanges, observing how it maintains coherence and builds upon the previous context. Another interesting experiment would be to evaluate the model's performance on specific tasks, such as question answering or instructions following, and compare it to other language models. This could help you understand the unique strengths and limitations of the e5-mistral-7b-instruct model. Overall, the e5-mistral-7b-instruct model represents a powerful and versatile tool for working with natural language text. Its combination of broad knowledge and instructional capabilities makes it a compelling option for a wide range of applications.
Updated 5/28/2024
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multilingual-e5-base
193
The multilingual-e5-base is a text embedding model developed by researcher intfloat. It is a 12-layer model with an embedding size of 768, initialized from the xlm-roberta-base model and further trained on a mixture of multilingual datasets. This model supports 100 languages, although performance may degrade for low-resource languages. The model was trained in two stages. In the first stage, it underwent contrastive pre-training with weak supervision, using a 1 billion text pair dataset filtered from the mC4 corpus. In the second stage, it was fine-tuned on various labeled datasets, including MS MARCO, NQ, Trivia QA, NLI from SimCSE, ELI5, DuReader Retrieval, KILT Fever, KILT HotpotQA, SQuAD, Quora, and multilingual datasets like Mr. TyDi and MIRACL. Similar models include the multilingual-e5-large model, which has 24 layers and a 1024 embedding size, as well as the xlm-roberta-base model, a multilingual BERT model pre-trained on 2.5TB of filtered CommonCrawl data. Model Inputs and Outputs Inputs Text**: The model accepts text inputs, which should start with either "query: " or "passage: " prefixes, even for non-English texts. For tasks other than retrieval, you can simply use the "query: " prefix. Outputs Text embeddings**: The model outputs 768-dimensional text embeddings that capture the semantic information of the input text. These embeddings can be used for a variety of downstream tasks, such as text retrieval, semantic similarity, and classification. Capabilities The multilingual-e5-base model can be used for a wide range of text-to-text tasks, thanks to its multilingual and robust text encoding capabilities. It has shown strong performance on benchmark tasks like passage ranking, as evidenced by its high MRR@10 scores on the Mr. TyDi dataset, outperforming baselines like BM25 and mDPR. What can I use it for? The multilingual-e5-base model can be used for a variety of applications, such as: Information Retrieval**: The model can be used to encode queries and passages for passage ranking tasks, enabling cross-lingual and multilingual information retrieval. Semantic Similarity**: The text embeddings produced by the model can be used to compute semantic similarity between text inputs, which can be useful for tasks like duplicate detection, paraphrase identification, and clustering. Text Classification**: The model's text embeddings can be used as features for training text classification models, such as topic classification or sentiment analysis. Things to try One interesting aspect of the multilingual-e5-base model is its ability to handle non-English texts. Try experimenting with inputs in various languages and observe how the model performs. You can also explore the model's performance on different downstream tasks, such as cross-lingual question answering or multilingual document retrieval, to better understand its capabilities. Another interesting experiment would be to compare the performance of the multilingual-e5-base model to the larger multilingual-e5-large model, or to the xlm-roberta-base model, to see how the model size and training data impact the results on your specific use case.
Updated 5/28/2024
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e5-large-v2
192
The e5-large-v2 model is a text embedding model developed by intfloat. It is part of the E5 family of text embedding models, which are designed for tasks like passage retrieval, semantic similarity, and paraphrase detection. The e5-large-v2 model has 24 layers and an embedding size of 1024, making it a larger and more powerful version compared to the e5-base-v2 and e5-small-v2 models. The model was pre-trained using a weakly-supervised contrastive learning approach on a variety of datasets, including filtered mC4, CC News, NLLB, Wikipedia, Reddit, S2ORC, Stackexchange, and xP3. It was then fine-tuned on supervised datasets like MS MARCO, NQ, Trivia QA, and others. This combination of pre-training and fine-tuning helps the model capture both general and task-specific text understanding capabilities. Compared to the similar e5-large model, the e5-large-v2 has been updated with better performance. Users are recommended to switch to the e5-large-v2 model going forward. Model inputs and outputs Inputs Text**: The model accepts text inputs that should be prefixed with either "query: " or "passage: " depending on the task. For tasks other than retrieval, the "query: " prefix can be used. Outputs Text embeddings**: The model outputs fixed-size vector representations (embeddings) of the input text. These embeddings can be used for a variety of downstream tasks like text retrieval, semantic similarity, and clustering. Capabilities The e5-large-v2 model is capable of generating high-quality text embeddings that capture the semantic meaning of the input text. These embeddings can be used for tasks like passage retrieval, where the model can find the most relevant passages given a query, or for semantic similarity, where the model can identify texts with similar meanings. The model's performance has been benchmarked on datasets like BEIR and MTEB, where it has shown strong results. What can I use it for? The e5-large-v2 model can be used for a variety of natural language processing tasks that involve text understanding and representation. Some potential use cases include: Information retrieval**: Use the model to find the most relevant passages or documents given a query, for applications like open-domain question answering or enterprise search. Semantic similarity**: Leverage the model's text embeddings to identify similar texts, for applications like paraphrase detection or document clustering. Text classification**: Use the model's embeddings as features for training custom text classification models, for applications like sentiment analysis or topic classification. Things to try One interesting aspect of the e5-large-v2 model is the way it handles the input text prefixes. The model is specifically trained to expect "query: " and "passage: " prefixes, even for non-retrieval tasks. This can help the model better capture the relationship between the query and passage, leading to improved performance. You can experiment with different ways of using these prefixes, such as using "query: " for symmetric tasks like semantic similarity, or using the prefixes even when using the embeddings as features for other downstream models. The model's performance may vary depending on the specific task and dataset, so it's worth trying out different approaches to see what works best.
Updated 5/27/2024
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multilingual-e5-large-instruct
119
The multilingual-e5-large-instruct model is a large-scale multilingual text embedding model developed by the team at intfloat. This model is an extension of the multilingual-e5-large model, with additional fine-tuning on instructional datasets to enable more versatile text understanding and generation capabilities. The model has 24 layers and an embedding size of 1024, and is initialized from the xlm-roberta-large model. It is then continuously trained on a diverse set of multilingual datasets, including web content, news, translated text, and task-oriented data, to develop robust cross-lingual text representations. Compared to the base multilingual-e5-large model, the multilingual-e5-large-instruct version incorporates additional fine-tuning on instructional datasets, allowing it to better understand and generate task-oriented text. This makes the model well-suited for applications that require natural language understanding and generation, such as open-domain question answering, task-oriented dialogue, and content summarization. Model inputs and outputs Inputs Query text**: The model accepts text inputs in the format "query: [your query]", which can be used for a variety of tasks such as passage retrieval, semantic similarity, and text generation. Passage text**: The model can also accept text in the format "passage: [your passage]", which is useful for tasks like passage ranking and document retrieval. Outputs The primary output of the multilingual-e5-large-instruct model is text embeddings, which are high-dimensional vector representations of the input text. These embeddings capture the semantic and contextual meaning of the text, and can be used for a wide range of downstream applications, such as: Text similarity**: Calculating the similarity between two pieces of text by comparing their embeddings. Information retrieval**: Ranking and retrieving the most relevant passages or documents for a given query. Text classification**: Using the embeddings as features for training machine learning models on text classification tasks. Text generation**: Generating relevant and coherent text based on the input prompt. Capabilities The multilingual-e5-large-instruct model excels at understanding and generating high-quality text in over 100 languages, making it a powerful tool for multilingual applications. Its instructional fine-tuning also allows it to perform well on a variety of task-oriented language understanding and generation tasks, such as question answering, dialogue, and summarization. Some key capabilities of the model include: Multilingual text understanding**: The model can comprehend and represent text in over 100 languages, including low-resource languages. Instructional language understanding**: The model can understand and follow natural language instructions, making it useful for interactive applications and task-oriented dialogue. Semantic text similarity**: The model can accurately measure the semantic similarity between text inputs, which is valuable for applications like information retrieval and document clustering. Text generation**: The model can generate relevant and coherent text based on input prompts, which can be useful for applications like content creation and dialogue systems. What can I use it for? The multilingual-e5-large-instruct model can be used for a wide range of natural language processing applications, especially those that require multilingual and task-oriented capabilities. Some potential use cases include: Multilingual information retrieval**: Use the model's text embeddings to rank and retrieve relevant documents or passages in response to queries in different languages. Multilingual question answering**: Fine-tune the model on question-answering datasets to enable open-domain question answering in multiple languages. Multilingual dialogue systems**: Leverage the model's instructional understanding to build task-oriented dialogue systems that can converse with users in various languages. Multilingual text summarization**: Fine-tune the model on summarization datasets to generate concise and informative summaries of multilingual text. Multilingual content creation**: Use the model's text generation capabilities to assist in the creation of high-quality content in multiple languages. Things to try One interesting aspect of the multilingual-e5-large-instruct model is its ability to understand and follow natural language instructions. This can be leveraged to create interactive applications that allow users to provide instructions in their preferred language and receive relevant responses. For example, you could try using the model to build a multilingual virtual assistant that can understand and respond to user queries and instructions across a variety of domains, such as information lookup, task planning, and content creation. By utilizing the model's instructional understanding and multilingual capabilities, you could create a versatile and user-friendly application that caters to a global audience. Another interesting application could be multilingual text summarization. You could fine-tune the model on summarization datasets in multiple languages to enable the generation of concise and informative summaries of long-form content, such as news articles or research papers, in a variety of languages. This could be particularly useful for users who need to quickly digest information from sources in languages they may not be fluent in. Overall, the multilingual-e5-large-instruct model provides a powerful foundation for building a wide range of multilingual natural language processing applications that require both high-quality text understanding and generation capabilities.
Updated 5/28/2024
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multilingual-e5-small
93
The multilingual-e5-small model is a text embedding model developed by intfloat. It is a smaller version of the larger multilingual-e5 models, with 12 layers and an embedding size of 384. The model is based on the Multilingual MiniLM and has been continually trained on a mixture of multilingual datasets to support 100 languages, although low-resource languages may see performance degradation. The multilingual-e5-base and multilingual-e5-large models are larger versions of the multilingual-e5-small model, with 12 and 24 layers respectively, and embedding sizes of 768 and 1024. These larger models leverage the XLM-RoBERTa and XLM-RoBERTa-Large initializations and further training on a variety of multilingual datasets. The multilingual-e5-large-instruct model is an even larger version with 24 layers and a 1024 embedding size. It is initialized from XLM-RoBERTa-Large and fine-tuned on various datasets, including some that provide task-specific instructions to the model. Model inputs and outputs Inputs Text**: The input text should start with either "query: " or "passage: ", even for non-English text. This is how the model was trained, and using the correct prefix is important for optimal performance. Outputs Text embeddings**: The model outputs text embeddings, which are high-dimensional vector representations of the input text. These embeddings can be used for a variety of downstream tasks, such as semantic similarity, information retrieval, and text classification. Capabilities The multilingual-e5 models excel at multilingual text understanding and retrieval tasks. They have been shown to outperform other popular multilingual models like mDPR and BM25 on the Mr. TyDi benchmark, a multilingual question answering and passage retrieval dataset. The multilingual-e5-large-instruct model further extends the capabilities of the multilingual-e5 models by allowing for customization through natural language instructions. This can be useful for tailoring the text embeddings to specific tasks or scenarios. What can I use it for? The multilingual-e5 models are well-suited for a variety of text-based applications that require multilingual support, such as: Information retrieval**: Use the text embeddings for semantic search and ranking of web pages, documents, or passages in response to user queries. Question answering**: Leverage the models for finding relevant passages that answer a given question, across multiple languages. Text classification**: Use the text embeddings as features for training classification models on multilingual datasets. Semantic similarity**: Calculate the similarity between text pairs, such as for paraphrase detection or bitext mining. The multilingual-e5-large-instruct model can be particularly useful for applications that benefit from customized text embeddings, such as specialized search engines, personal assistants, or chatbots. Things to try One interesting aspect of the multilingual-e5 models is the use of a low temperature (0.01) for the InfoNCE contrastive loss during training. This results in the cosine similarity scores of the text embeddings being distributed around 0.7 to 1.0, rather than the more typical range of -1 to 1. While this may seem counterintuitive at first, it's important to note that for tasks like text retrieval or semantic similarity, what matters is the relative order of the scores rather than the absolute values. The low temperature helps to amplify the differences between similar and dissimilar text pairs, which can be beneficial for these types of applications. You can experiment with this behavior and see how it affects the performance of your specific use case.
Updated 5/28/2024
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e5-base-v2
80
The e5-base-v2 model is a text embedding model developed by the researcher intfloat. This model has 12 layers and an embedding size of 768, and was trained using a novel technique called "Text Embeddings by Weakly-Supervised Contrastive Pre-training". The model can be used for a variety of text-related tasks, and compares favorably to similar models like the e5-large and multilingual-e5-base models. Model inputs and outputs The e5-base-v2 model takes in text inputs and outputs text embeddings. The embeddings can be used for a variety of downstream tasks such as passage retrieval, semantic similarity, and text classification. Inputs Text inputs, which can be either "query: " or "passage: " prefixed Outputs Text embeddings, which are 768-dimensional vectors Capabilities The e5-base-v2 model is capable of producing high-quality text embeddings that can be used for a variety of tasks. The model was trained on a large, diverse corpus of text data, and has been shown to perform well on a number of benchmarks, including the BEIR and MTEB benchmarks. What can I use it for? The e5-base-v2 model can be used for a variety of text-related tasks, including: Passage retrieval**: The model can be used to retrieve relevant passages given a query, which can be useful for building search engines or question-answering systems. Semantic similarity**: The model can be used to compute the semantic similarity between two pieces of text, which can be useful for tasks like paraphrase detection or document clustering. Text classification**: The model's embeddings can be used as features for training text classification models, which can be useful for a variety of applications like sentiment analysis or topic modeling. Things to try One interesting thing to try with the e5-base-v2 model is to explore the different training datasets and techniques used to create the model. The paper describing the model provides details on the weakly-supervised contrastive pre-training approach, which is a novel technique that could be worth exploring further. Another interesting avenue to explore is the model's performance on different benchmarks and tasks, particularly in comparison to similar models like the e5-large and multilingual-e5-base models. Understanding the strengths and weaknesses of each model could help inform the choice of which model to use for a particular application.
Updated 5/27/2024
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e5-large
65
The e5-large model is a large text embedding model developed by the researcher intfloat. It was trained using a method called "Text Embeddings by Weakly-Supervised Contrastive Pre-training", which involves learning text representations from a large corpus of unlabeled text data through a contrastive learning approach. The model has 24 layers and an embedding size of 1024, making it a powerful text embedding model. Similar models developed by intfloat include the multilingual-e5-large and multilingual-e5-base models, which are designed for multilingual text embedding tasks. Model inputs and outputs Inputs Text data in the form of queries and passages, where each input text should start with "query: " or "passage: ". Outputs Text embeddings, which are vector representations of the input text that capture its semantic meaning. These embeddings can be used for a variety of downstream tasks, such as information retrieval, semantic similarity, and text classification. Capabilities The e5-large model has demonstrated strong performance on a variety of text embedding tasks, such as passage retrieval and semantic similarity. It has been shown to outperform other popular text embedding models, such as BERT and RoBERTa, on benchmark evaluations. What can I use it for? The e5-large model can be used for a wide range of applications that involve text understanding and processing, such as: Information retrieval**: The model can be used to encode queries and documents, and then compute the similarity between them to retrieve relevant documents. Semantic search**: The model can be used to encode user queries and product descriptions, and then match them to enable more accurate and relevant search results. Text classification**: The model can be used to encode text data and then feed it into a downstream classification model to perform tasks such as sentiment analysis, topic modeling, and more. Things to try One interesting thing to try with the e5-large model is to compare its performance on different tasks with the performance of the similar models developed by intfloat, such as the multilingual-e5-large and multilingual-e5-base models. This can help you understand the strengths and weaknesses of each model and choose the one that best fits your particular use case.
Updated 5/28/2024
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e5-small-v2
65
The e5-small-v2 model is a text embedding model developed by intfloat. It is a smaller version of the E5 model family, with 12 layers and an embedding size of 384. The E5 models are designed for text embedding tasks through weakly-supervised contrastive pre-training, as described in the paper Text Embeddings by Weakly-Supervised Contrastive Pre-training. The e5-small-v2 model is similar to the e5-base-v2 model, but with a smaller size and potentially reduced performance on some tasks. Both models are part of the E5 family and share the same training approach. Model inputs and outputs Inputs Text inputs should start with either "query: " or "passage: " to indicate the type of text. This is how the model was trained and is required for optimal performance. The model can handle up to 512 tokens per input. Outputs The model outputs text embeddings, which can be used for tasks like text retrieval, semantic similarity, and clustering. The embeddings are normalized to have unit L2 norm, so the cosine similarity between embeddings reflects their semantic similarity. Capabilities The e5-small-v2 model is capable of generating high-quality text embeddings for a variety of natural language processing tasks. Its embedding quality has been evaluated on benchmarks like BEIR and MTEB, showing strong performance compared to other models. What can I use it for? The e5-small-v2 model can be used for any task that requires text embeddings, such as: Information retrieval**: The model can be used to rank documents or passages based on their relevance to a query. Semantic search**: The model can be used to find semantically similar documents or passages to a given query. Text classification**: The model's embeddings can be used as features for training linear classifiers on various text classification tasks. Clustering**: The model's embeddings can be used to cluster related documents or passages together. Things to try One key aspect of the e5-small-v2 model is its use of weak supervision during pre-training, where the model learns to embed text by contrasting related pairs of text. This can result in embeddings that capture nuanced semantic relationships, beyond just lexical similarity. To take advantage of this, you can try using the model's embeddings for tasks like paraphrase detection, where you want to identify semantically similar text that may not have significant lexical overlap. The model's ability to capture subtle semantic connections can make it a powerful tool for these types of tasks.
Updated 5/28/2024