metadata

pipeline_tag: sentence-similarity
license: apache-2.0
tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  - transformers
language:
  - en
datasets:
  - mteb/sts12-sts
  - mteb/sts13-sts
  - mteb/sts14-sts
  - mteb/sts15-sts
  - mteb/sts16-sts
  - mteb/stsbenchmark-sts
  - mteb/sickr-sts
  - mteb/askubuntudupquestions-reranking
  - mteb/scidocs-reranking
  - snli
  - PL-MTEB/sicke-pl-pairclassification
metrics:
  - spearmanr
  - accuracy

kornwtp/ConGen-BERT-Small

This is a SCT model: It maps sentences to a dense vector space and can be used for tasks like semantic search.

Usage

Using this model becomes easy when you have SCT installed:

pip install -U git+https://github.com/mrpeerat/SCT

Then you can use the model like this:

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

model = SentenceTransformer('mrp/SCT_BERT_Base')
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

@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",
}