| | --- |
| | pipeline_tag: sentence-similarity |
| | license: apache-2.0 |
| | tags: |
| | - sentence-transformers |
| | - feature-extraction |
| | - sentence-similarity |
| | - transformers |
| | language: |
| | - en |
| | --- |
| | |
| | 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 |
| |
|
| | Using this model becomes easy when you have [SCT](https://github.com/mrpeerat/SCT) installed: |
| |
|
| | ``` |
| | pip install -U git+https://github.com/mrpeerat/SCT |
| | ``` |
| |
|
| | Then you can use the model like this: |
| |
|
| | ```python |
| | from sentence_transformers import SentenceTransformer |
| | sentences = ["This is an example sentence", "Each sentence is converted"] |
| | |
| | model = SentenceTransformer('mrp/SCT_BERT_Large') |
| | embeddings = model.encode(sentences) |
| | print(embeddings) |
| | ``` |
| |
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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 |
| |
|
| | ```bibtex |
| | @article{limkonchotiwat-etal-2023-sct, |
| | title = "An Efficient Self-Supervised Cross-View Training For Sentence Embedding", |
| | author = "Limkonchotiwat, Peerat and |
| | Ponwitayarat, Wuttikorn and |
| | Lowphansirikul, Lalita and |
| | Udomcharoenchaikit, Can and |
| | Chuangsuwanich, Ekapol and |
| | Nutanong, Sarana", |
| | journal = "Transactions of the Association for Computational Linguistics", |
| | year = "2023", |
| | address = "Cambridge, MA", |
| | publisher = "MIT Press", |
| | } |
| | ``` |