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-MiniLM-L3
+
+This is a [ConGen](https://github.com/KornWtp/ConGen) model: It maps sentences to a 384 dimensional dense vector space and can be used for tasks like semantic search.
+
+
+
+## Usage
+
+Using this model becomes easy when you have [ConGen](https://github.com/KornWtp/ConGen) installed:
+
+```
+pip install -U git+https://github.com/KornWtp/ConGen.git
+```
+
+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('kornwtp/ConGen-MiniLM-L3')
+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/KornWtp/ConGen#main-results---sts)
+
+
+## Citing & Authors
+
+```bibtex 
+@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",
+}
+```