text2vec-base-multilingual
Maintainer: shibing624
46
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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 text2vec-base-multilingual model is a CoSENT (Cosine Sentence) model developed by shibing624. It maps sentences to a 384 dimensional dense vector space and can be used for tasks like sentence embeddings, text matching or semantic search. The model was fine-tuned on a large dataset of multilingual natural language inference data.
Similar models developed by shibing624 include the text2vec-base-chinese-sentence and text2vec-base-chinese-paraphrase models, which map sentences to 768 dimensional vector spaces. These models use the nghuyong/ernie-3.0-base-zh base model.
Model inputs and outputs
Inputs
- Text: The model takes in text sequences up to 256 word pieces in length.
Outputs
- Sentence embeddings: The model outputs a 384 dimensional vector representation of the input text, capturing its semantic meaning.
Capabilities
The text2vec-base-multilingual model can be used for a variety of NLP tasks that benefit from semantic text representations, such as information retrieval, text clustering, and sentence similarity. It is particularly well-suited for multilingual applications, as it supports 9 languages including Chinese, English, French, and German.
What can I use it for?
The sentence embeddings produced by this model can be used as inputs to downstream machine learning models for tasks like text classification, question answering, and semantic search. For example, you could use the embeddings to find semantically similar documents in a large corpus, or to cluster sentences based on their content.
Things to try
One interesting aspect of this model is its use of the CoSENT (Cosine Sentence) architecture, which aims to map semantically similar sentences to nearby points in the vector space. You could experiment with using the model's embeddings to measure sentence similarity, and see how well it performs on tasks like paraphrase detection or textual entailment.
You could also try fine-tuning the model on a specific domain or task, such as customer service chat logs or scientific abstracts, to see if you can improve its performance on that particular application.
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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