Update README.md

README.md

@@ -7,6 +7,16 @@ tags:
 - transformers
 language:
 - multilingual
+- hi
+- mr
+- kn
+- ta
+- te
+- ml
+- gu
+- or
+- pa
+- bn
 widget:
 - source_sentence: "दिवाळी आपण मोठ्या उत्साहाने साजरी करतो"
   sentences:
@@ -41,12 +51,23 @@ widget:
   example_title: "Cross-lingual 2"
 ---
 
-# {MODEL_NAME}
+# IndicSBERT-STS
 
-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.
+This is a IndicSBERT model (l3cube-pune/indic-sentence-bert-nli) trained on the STS dataset of ten major Indian Languages. <br>
+The single model works for Hindi, Marathi, Kannada, Tamil, Telugu, Gujarati, Oriya, Punjabi, Malayalam, and Bengali.
+The model also has cross-lingual capabilities. <br>
+Released as a part of project MahaNLP: https://github.com/l3cube-pune/MarathiNLP <br>
 
-<!--- Describe your model here -->
+More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.org/abs/2211.11187) 
 
+```
+@article{joshi2022l3cubemahasbert,
+  title={L3Cube-MahaSBERT and HindSBERT: Sentence BERT Models and Benchmarking BERT Sentence Representations for Hindi and Marathi},
+  author={Joshi, Ananya and Kajale, Aditi and Gadre, Janhavi and Deode, Samruddhi and Joshi, Raviraj},
+  journal={arXiv preprint arXiv:2211.11187},
+  year={2022}
+}
+```
 ## Usage (Sentence-Transformers)
 
 Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
@@ -103,57 +124,3 @@ sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']
 print("Sentence embeddings:")
 print(sentence_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 7187 with parameters:
-```
-{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
-```
-
-**Loss**:
-
-`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` 
-
-Parameters of the fit()-Method:
-```
-{
-    "epochs": 4,
-    "evaluation_steps": 0,
-    "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
-    "max_grad_norm": 1,
-    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
-    "optimizer_params": {
-        "lr": 2e-05
-    },
-    "scheduler": "WarmupLinear",
-    "steps_per_epoch": null,
-    "warmup_steps": 2874,
-    "weight_decay": 0.01
-}
-```
-
-
-## Full Model Architecture
-```
-SentenceTransformer(
-  (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
-
-<!--- Describe where people can find more information -->