sn-xlm-roberta-base-snli-mnli-anli-xnli
Maintainer: symanto
61
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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 sn-xlm-roberta-base-snli-mnli-anli-xnli model is a Siamese network model trained for zero-shot and few-shot text classification. It is based on the xlm-roberta-base model and was trained on SNLI, MNLI, ANLI, and XNLI datasets. This model maps sentences and paragraphs to a 768 dimensional dense vector space, making it useful for tasks like clustering or semantic search.
Similar models include paraphrase-xlm-r-multilingual-v1, paraphrase-multilingual-mpnet-base-v2, all-mpnet-base-v2, and paraphrase-multilingual-MiniLM-L12-v2, all developed by the sentence-transformers team.
Model inputs and outputs
Inputs
- Sentences or paragraphs of text
Outputs
- 768-dimensional dense vector representations of the input text
Capabilities
The sn-xlm-roberta-base-snli-mnli-anli-xnli model can be used for a variety of text-related tasks, such as text classification, clustering, and semantic search. Its ability to map text to a dense vector space allows for efficient comparison and retrieval of semantically similar content.
What can I use it for?
This model can be particularly useful for applications that require understanding the semantic relationship between text, such as:
- Information retrieval: Find relevant documents or passages based on user queries
- Text clustering: Group similar text documents together
- Recommendation systems: Suggest related content based on user interests
The provided maintainer profile offers additional insights into the creators and potential use cases for this model.
Things to try
One interesting aspect of this model is its ability to perform well on zero-shot and few-shot text classification tasks. This means that the model can be applied to new classification problems with minimal additional training, making it a versatile tool for rapidly developing text-based applications.
Researchers and developers can experiment with fine-tuning the model on domain-specific datasets or combining it with other NLP techniques to explore novel applications and push the boundaries of what's possible with transformer-based sentence embeddings.
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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