Create README.md

README.md

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+---
+pipeline_tag: sentence-similarity
+license: apache-2.0
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers
+---
+
+# kornwtp/ConGen-BERT-Small
+
+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_Distillation_BERT_Tiny')
+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",
+}
+```