Update README.md

README.md

@@ -7,7 +7,7 @@ tags:
 
 ---
 
-# {MODEL_NAME}
+# {CLFE(ConMath)}
 
 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
 
@@ -24,57 +24,11 @@ pip install -U sentence-transformers
 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('{MODEL_NAME}')
-embeddings = model.encode(sentences)
-print(embeddings)
-```
-
-
-
-## Evaluation Results
-
-<!--- Describe how your model was evaluated -->
-
-For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
-
-
-## Training
-The model was trained with the parameters:
-
-**DataLoader**:
-
-`torch.utils.data.dataloader.DataLoader` of length 45861 with parameters:
-```
-{'batch_size': 4, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
+embedding_latex = model.encode([{'latex': latex}])
+embedding_pmml = model.encode([{'mathml': pmml}])
+embedding_cmml = model.encode([{'mathml': cmml}])
 ```
 
-**Loss**:
-
-`sentence_transformers.losses.MultipleNegativesSymmetricRankingLoss.MultipleNegativesSymmetricRankingLoss` with parameters:
-  ```
-  {'scale': 20.0, 'similarity_fct': 'cos_sim'}
-  ```
-
-Parameters of the fit()-Method:
-```
-{
-    "epochs": 1,
-    "evaluation_steps": 4586,
-    "evaluator": "sentence_transformers.evaluation.AlignmentandUniformityEvaluator.AlignmentandUniformityEvaluator",
-    "max_grad_norm": 1,
-    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
-    "optimizer_params": {
-        "lr": 5e-05
-    },
-    "scheduler": "WarmupLinear",
-    "steps_per_epoch": null,
-    "warmup_steps": 4587,
-    "weight_decay": 0.01
-}
-```
 
 
 ## Full Model Architecture
@@ -90,4 +44,12 @@ SentenceTransformer(
 
 ## Citing & Authors
 
-<!--- Describe where people can find more information -->
+<!--- Describe where people can find more information -->
+@inproceedings{wang2023math,
+  title={Math Information Retrieval with Contrastive Learning of Formula Embeddings},
+  author={Wang, Jingyi and Tian, Xuedong},
+  booktitle={International Conference on Web Information Systems Engineering},
+  pages={97--107},
+  year={2023},
+  organization={Springer}
+}