Update README.md

README.md

@@ -9,7 +9,7 @@ library_name: sentence-transformers
 
 # SentenceTransformer
 
-This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+This is a trained [Chem-MRL](https://github.com/emapco/chem-mrl) [sentence-transformers](https://www.SBERT.net) model. It maps SMILES to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, database indexing, molecular classification, clustering, and more.
 
 ## Model Details
 
@@ -25,15 +25,13 @@ This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps
 
 ### Model Sources
 
-- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
-- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
-- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+- **Repository:** [Chem-MRL on GitHub](https://github.com/emapco/chem-mrl)
 
 ### Full Model Architecture
 
 ```
 SentenceTransformer(
-  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: RobertaModel 
+  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: RobertaModel (ChemBERTa)
   (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
   (2): Normalize()
 )
@@ -54,12 +52,12 @@ Then you can load this model and run inference.
 from sentence_transformers import SentenceTransformer
 
 # Download from the 🤗 Hub
-model = SentenceTransformer("sentence_transformers_model_id")
+model = SentenceTransformer("Derify/ChemMRL-alpha")
 # Run inference
 sentences = [
-    'The weather is lovely today.',
-    "It's so sunny outside!",
-    'He drove to the stadium.',
+    'CCO',
+    "CC(C)O",
+    'CC(=O)O',
 ]
 embeddings = model.encode(sentences)
 print(embeddings.shape)
@@ -119,23 +117,19 @@ You can finetune this model on your own dataset.
 - Tokenizers: 0.21.0
 
 ## Citation
+- Chithrananda, Seyone, et al. "ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction." _arXiv [Cs.LG]_, 2020. [Link](http://arxiv.org/abs/2010.09885).
+- Ahmad, Walid, et al. "ChemBERTa-2: Towards Chemical Foundation Models." _arXiv [Cs.LG]_, 2022. [Link](http://arxiv.org/abs/2209.01712).
+- Kusupati, Aditya, et al. "Matryoshka Representation Learning." _arXiv [Cs.LG]_, 2022. [Link](https://arxiv.org/abs/2205.13147).
+- Li, Xianming, et al. "2D Matryoshka Sentence Embeddings." _arXiv [Cs.CL]_, 2024. [Link](http://arxiv.org/abs/2402.14776).
+- Bajusz, Dávid, et al. "Why is the Tanimoto Index an Appropriate Choice for Fingerprint-Based Similarity Calculations?" _J Cheminform_, 7, 20 (2015). [Link](https://doi.org/10.1186/s13321-015-0069-3).
+- Li, Xiaoya, et al. "Dice Loss for Data-imbalanced NLP Tasks." _arXiv [Cs.CL]_, 2020. [Link](https://arxiv.org/abs/1911.02855)
+- Reimers, Nils, and Gurevych, Iryna. "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks." _Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing_, 2019. [Link](https://arxiv.org/abs/1908.10084).
 
-### BibTeX
 
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