added proper citation to readme

README.md

@@ -13,26 +13,27 @@ metrics:
   - accuracy_manhattan
 model-index:
   - name: st-greek-media-bert-base-uncased
-    results: [
-      {
-        "task": {
-          "name": "STS Benchmark",
-          "type": "sentence-similarity"
+    results:
+      [
+        {
+          "task": { "name": "STS Benchmark", "type": "sentence-similarity" },
+          "metrics":
+            [
+              { "type": "accuracy_cosinus", "value": 0.9563965089445283 },
+              { "type": "accuracy_euclidean", "value": 0.9566394253292384 },
+              { "type": "accuracy_manhattan", "value": 0.9565353183072198 },
+            ],
+          "dataset":
+            {
+              "name": "all_custom_greek_media_triplets",
+              "type": "sentence-pair",
+            },
         },
-        "metrics": [ 
-          { "type": "accuracy_cosinus", "value": 0.9563965089445283 }, 
-          { "type": "accuracy_euclidean", "value": 0.9566394253292384 },         
-          { "type": "accuracy_manhattan", "value": 0.9565353183072198 }
-        ],
-        "dataset": {
-          "name": "all_custom_greek_media_triplets",
-          "type": "sentence-pair"
-        },
-      }
-    ]
+      ]
 ---
 
 # Greek Media SBERT (uncased)
+
 ## Sentence Transformer
 
 This is a [sentence-transformers](https://www.SBERT.net) based on the [Greek Media BERT (uncased)](https://huggingface.co/dimitriz/greek-media-bert-base-uncased) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
@@ -103,8 +104,8 @@ print(sentence_embeddings)
 
 <!--- 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=dimitriz/st-greek-media-bert-base-uncased)
+For an automated evaluation of this model, see the _Sentence Embeddings
+Benchmark_: [https://seb.sbert.net](https://seb.sbert.net?model_name=dimitriz/st-greek-media-bert-base-uncased)
 
 ## Training
 
@@ -132,9 +133,9 @@ The model was trained with the parameters:
 
 `sentence_transformers.losses.TripletLoss.TripletLoss` with parameters:
 
-  ```
-  {'distance_metric': 'TripletDistanceMetric.EUCLIDEAN', 'triplet_margin': 5}
-  ```
+```
+{'distance_metric': 'TripletDistanceMetric.EUCLIDEAN', 'triplet_margin': 5}
+```
 
 Parameters of the fit()-Method:
 
@@ -159,17 +160,37 @@ Parameters of the fit()-Method:
 
 ```
 SentenceTransformer(
-  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
   (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
 )
 ```
 
 ## Citing & Authors
 
-```@inproceedings{...,
-  title={DACL},
-  author={Zaikis et al.},
-  booktitle={...},
-  year={2023}
+The model has been officially released with the article "DACL: A Domain-Adapted Contrastive Learning Approach to Low Resource Language Representations for Document Clustering Tasks".
+Dimitrios Zaikis, Stylianos Kokkas and Ioannis Vlahavas.
+In: Iliadis, L., Maglogiannis, I., Alonso, S., Jayne, C., Pimenidis, E. (eds) Engineering Applications of Neural Networks. EANN 2023. Communications in Computer and Information Science, vol 1826. Springer, Cham".
+
+If you use the model, please cite the following:
+
+```bibtex
+@InProceedings{10.1007/978-3-031-34204-2_47,
+author="Zaikis, Dimitrios
+and Kokkas, Stylianos
+and Vlahavas, Ioannis",
+editor="Iliadis, Lazaros
+and Maglogiannis, Ilias
+and Alonso, Serafin
+and Jayne, Chrisina
+and Pimenidis, Elias",
+title="DACL: A Domain-Adapted Contrastive Learning Approach to Low Resource Language Representations for Document Clustering Tasks",
+booktitle="Engineering Applications of Neural Networks",
+year="2023",
+publisher="Springer Nature Switzerland",
+address="Cham",
+pages="585--598",
+isbn="978-3-031-34204-2"
 }
-```
+
+
+```