Update model files

README.md

@@ -5,69 +5,15 @@ tags:
 - feature-extraction
 - sentence-similarity
 - transformers
-language:
-- multilingual
-- hi
-- mr
-- kn
-- ta
-- te
-- ml
-- gu
-- or
-- pa
-- bn
-widget:
-- source_sentence: "दिवाळी आपण मोठ्या उत्साहाने साजरी करतो"
-  sentences:
-    - "दिवाळी आपण आनंदाने साजरी करतो"
-    - "दिवाळी हा दिव्यांचा सण आहे"
-  example_title: "Monolingual- Marathi"
-
-- source_sentence: "हम दीपावली उत्साह के साथ मनाते हैं"
-  sentences:
-    - "हम दीपावली खुशियों से मनाते हैं"
-    - "दिवाली रोशनी का त्योहार है"
-  example_title: "Monolingual- Hindi"
-
-- source_sentence: "અમે ઉત્સાહથી દિવાળી ઉજવીએ છીએ"
-  sentences:
-    - "દિવાળી આપણે ખુશીઓથી ઉજવીએ છીએ"
-    - "દિવાળી એ રોશનીનો તહેવાર છે"
-  example_title: "Monolingual- Gujarati"
-  
-- source_sentence: "आम्हाला भारतीय असल्याचा अभिमान आहे"
-  sentences:
-    - "हमें भारतीय होने पर गर्व है"
-    - "భారతీయులమైనందుకు గర్విస్తున్నాం"
-    - "અમને ભારતીય હોવાનો ગર્વ છે"
-  example_title: "Cross-lingual 1"
-
-- source_sentence: "ਬਾਰਿਸ਼ ਤੋਂ ਬਾਅਦ ਬਗੀਚਾ ਸੁੰਦਰ ਦਿਖਾਈ ਦਿੰਦਾ ਹੈ"
-  sentences:
-    - "മഴയ്ക്ക് ശേഷം പൂന്തോട്ടം മനോഹരമായി കാണപ്പെടുന്നു"
-    - "ବର୍ଷା ପରେ ବଗିଚା ସୁନ୍ଦର ଦେଖାଯାଏ |" 
-    - "बारिश के बाद बगीचा सुंदर दिखता है"
-  example_title: "Cross-lingual 2"
+
 ---
 
-# IndicSBERT-STS
+# {MODEL_NAME}
 
-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>
+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.
 
-More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.org/abs/2211.11187) 
+<!--- Describe your model here -->
 
-```
-@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:
@@ -124,3 +70,57 @@ 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 1977 with parameters:
+```
+{'batch_size': 32, '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": 790,
+    "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 -->

config_sentence_transformers.json

@@ -1,7 +1,7 @@
 {
   "__version__": {
     "sentence_transformers": "2.2.2",
-    "transformers": "4.25.1",
-    "pytorch": "1.13.0+cu116"
+    "transformers": "4.26.1",
+    "pytorch": "1.13.1+cu116"
   }
 }

pytorch_model.bin

@@ -1,3 +1,3 @@
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