Congen TinyBERT L4

🚀 kornwtp/ConGen-TinyBERT-L4

This model maps sentences to a 312-dimensional dense vector space and is applicable for tasks such as semantic search.

🚀 Quick Start

Using this model becomes easy when you have ConGen installed. First, install the necessary package:

pip install -U git+https://github.com/KornWtp/ConGen.git

💻 Usage Examples

Basic Usage

Then you can use the model with the following code:

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

model = SentenceTransformer('kornwtp/ConGen-TinyBERT-L4')
embeddings = model.encode(sentences)
print(embeddings)

📚 Documentation

Evaluation Results

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

Citing & Authors

If you want to cite this work, use the following BibTeX entry:

@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",
}

📄 License

This model is licensed under the Apache-2.0 license.