gte-large

Maintainer: thenlper

Total Score

215

Last updated 5/19/2024

📈

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

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Model overview

The gte-large model is a general text embedding model created by Alibaba DAMO Academy. It is based on the BERT framework and is one of three different model sizes offered, including gte-base and gte-small. The GTE models are trained on a large-scale corpus of relevant text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream text embedding tasks such as information retrieval, semantic textual similarity, and text reranking.

The multilingual-e5-large model is a large multilingual text embedding model created by Microsoft researchers. It is based on the XLM-RoBERTa architecture and supports over 100 languages. The model is pre-trained on a diverse set of datasets including Wikipedia, CCNews, and NLLB, then fine-tuned on tasks like passage retrieval, question answering, and natural language inference.

Both the GTE and E5 models aim to provide high-quality text embeddings that can be used for a variety of language tasks. The GTE models focus on general-purpose text understanding, while the E5 models specialize more in multilingual applications.

Model inputs and outputs

Inputs

  • Text sequences: The model accepts text sequences as input, which can be short queries, long passages, or any other natural language text.

Outputs

  • Text embeddings: The primary output of the model is a dense vector representation (embedding) for each input text sequence. These embeddings capture the semantic meaning and relationships between the input texts.
  • Similarity scores: For tasks like passage retrieval or semantic textual similarity, the model can also output pairwise similarity scores between input text sequences.

Capabilities

The gte-large model excels at a variety of text embedding tasks, as evidenced by its strong performance on the MTEB benchmark. It achieves state-of-the-art results in areas like information retrieval, semantic textual similarity, and text reranking.

The multilingual-e5-large model is particularly adept at multilingual tasks. It demonstrates impressive performance on the Mr. TyDi benchmark, which evaluates passage retrieval across 11 diverse languages. The model's broad language support makes it a useful tool for applications that need to handle text in multiple languages.

Both models can be fine-tuned on domain-specific data to further optimize their performance for particular use cases. The provided fine-tuning examples show how to effectively adapt the models to your own requirements.

What can I use it for?

The gte-large and multilingual-e5-large models are versatile tools that can be applied to a wide range of NLP tasks. Some potential use cases include:

  • Information retrieval: Use the models to find relevant documents or passages given a search query.
  • Semantic search: Leverage the models' text embeddings to build semantic search engines that can understand user intent beyond just keyword matching.
  • Chatbots and virtual assistants: Incorporate the models into conversational AI systems to improve understanding of user queries and provide more relevant responses.
  • Content recommendation: Use the models to identify similar content or recommend relevant items to users based on their interests or browsing history.
  • Multilingual applications: Take advantage of the multilingual-e5-large model's broad language support to build applications that can handle text in multiple languages.

Things to try

One interesting aspect of the gte-large and multilingual-e5-large models is their ability to handle short queries and long passages effectively. For tasks like passage retrieval, you can experiment with adding a simple instruction prefix to the query (e.g., "Represent this sentence for searching relevant passages:") to see if it improves the model's performance.

Another area to explore is the models' robustness to domain-specific terminology or jargon. You can try fine-tuning the models on your own dataset to see if it enhances their ability to understand and relate specialized content.

Finally, the provided fine-tuning examples demonstrate techniques like mining hard negatives, which can be a powerful way to further enhance the models' embedding quality and downstream task performance.



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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