README.md

metadata

pipeline_tag: sentence-similarity
license: apache-2.0
tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  - transformers
language:
  - en
datasets:
  - snli
  - multi_nli
metrics:
  - spearmanr
model-index:
  - name: mrp/SCT_BERT_Base
    results:
      - task:
          type: STS
          name: STS
        dataset:
          type: Similarity
          name: STS12
        metrics:
          - type: spearmanr
            value: 78.83
            name: Test spearmanr
            verified: false
  - name: mrp/SCT_BERT_Base
    results:
      - task:
          type: STS
          name: STS
        dataset:
          type: Similarity
          name: STS13
        metrics:
          - type: spearmanr
            value: 78.02
            name: Test spearmanr
            verified: false

mrp/SCT_BERT_Base

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",
}