from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('kornwtp/ConGen-TinyBERT-L6')
embeddings = model.encode(sentences)
print(embeddings)

Evaluation Results

For an automated evaluation of this model, see the Sentence Embeddings Benchmark: Semantic Textual Similarity

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",
}