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--- |
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pipeline_tag: sentence-similarity |
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license: apache-2.0 |
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tags: |
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- sentence-transformers |
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- feature-extraction |
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- sentence-similarity |
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- transformers |
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language: |
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- en |
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--- |
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This is a [SCT](https://github.com/mrpeerat/SCT) model: It maps sentences to a dense vector space and can be used for tasks like semantic search. |
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## Usage |
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Using this model becomes easy when you have [SCT](https://github.com/mrpeerat/SCT) installed: |
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``` |
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pip install -U git+https://github.com/mrpeerat/SCT |
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``` |
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Then you can use the model like this: |
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```python |
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from sentence_transformers import SentenceTransformer |
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sentences = ["This is an example sentence", "Each sentence is converted"] |
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model = SentenceTransformer('mrp/SCT_BERT_Mini') |
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embeddings = model.encode(sentences) |
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print(embeddings) |
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``` |
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## Evaluation Results |
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For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [Semantic Textual Similarity](https://github.com/mrpeerat/SCT#main-results---sts) |
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## Citing & Authors |
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```bibtex |
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@article{limkonchotiwat-etal-2023-sct, |
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title = "An Efficient Self-Supervised Cross-View Training For Sentence Embedding", |
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author = "Limkonchotiwat, Peerat and |
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Ponwitayarat, Wuttikorn and |
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Lowphansirikul, Lalita and |
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Udomcharoenchaikit, Can and |
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Chuangsuwanich, Ekapol and |
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Nutanong, Sarana", |
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journal = "Transactions of the Association for Computational Linguistics", |
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year = "2023", |
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address = "Cambridge, MA", |
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publisher = "MIT Press", |
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} |
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``` |
