---
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            # Required. Example: automatic-speech-recognition
      name: STS             # Optional. Example: Speech Recognition
    dataset:
      type: Similarity          # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
      name: STS12           # Required. A pretty name for the dataset. Example: Common Voice (French)
    metrics:
      - type: spearmanr         # Required. Example: wer. Use metric id from https://hf.co/metrics
        value: 78.83       # Required. Example: 20.90
        name: Test spearmanr          # Optional. Example: Test WER
        verified: False              # Optional. If true, indicates that evaluation was generated by Hugging Face (vs. self-reported).

- name: mrp/SCT_BERT_Base        
  results:
  - task:
      type: STS            # Required. Example: automatic-speech-recognition
      name: STS             # Optional. Example: Speech Recognition
    dataset:
      type: Similarity          # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
      name: STS13           # Required. A pretty name for the dataset. Example: Common Voice (French)
    metrics:
      - type: spearmanr         # Required. Example: wer. Use metric id from https://hf.co/metrics
        value: 78.02       # Required. Example: 20.90
        name: Test spearmanr          # Optional. Example: Test WER
        verified: False              # Optional. If true, indicates that evaluation was generated by Hugging Face (vs. self-reported).
---


# mrp/SCT_BERT_Base

This is a [SCT](https://github.com/mrpeerat/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](https://github.com/mrpeerat/SCT) installed:

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

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('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](https://github.com/mrpeerat/SCT#main-results---sts)


## Citing & Authors

```bibtex 
@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",
}
```