e5-large-v2
Maintainer: intfloat
192
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
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.
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 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.
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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.
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