readme: ---
  pipeline_tag: sentence-similarity
  license: apache-2.0
  tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  - transformers
  ---
  
  # kornwtp/ConGen-MiniLM-L3
  
  This is a [ConGen](https://github.com/KornWtp/ConGen) model: It maps sentences to a 384 dimensional dense vector space and can be used for tasks like semantic search.
  
  
  
  ## Usage
  
  Using this model becomes easy when you have [ConGen](https://github.com/KornWtp/ConGen) installed:
  
  ```
  pip install -U git+https://github.com/KornWtp/ConGen.git
  ```
  
  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('kornwtp/ConGen-MiniLM-L3')
  embeddings = model.encode(sentences)
  print(embeddings)
  ```
  
  
  
  ## Evaluation Results
  
  
  
  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [Semantic Textual Similarity](https://github.com/KornWtp/ConGen#main-results---sts)
  
  
  ## Citing & Authors
  
  ```bibtex 
  @inproceedings{limkonchotiwat-etal-2022-congen,
      title = "{ConGen}: Unsupervised Control and Generalization Distillation For Sentence Representation",
      author = "Limkonchotiwat, Peerat  and
        Ponwitayarat, Wuttikorn  and
        Lowphansirikul, Lalita and
        Udomcharoenchaikit, Can  and
        Chuangsuwanich, Ekapol  and
        Nutanong, Sarana",
      booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
      year = "2022",
      publisher = "Association for Computational Linguistics",
  }
  ```