Update model files

README.md

@@ -5,67 +5,14 @@ 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
 
-This is a MuRIL model (google/muril-base-cased) trained on the NLI 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>
+---
 
-A better sentence similarity model (fine-tuned version of this model) is shared here: https://huggingface.co/l3cube-pune/indic-sentence-similarity-sbert <br>
+# {MODEL_NAME}
 
-More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.org/abs/2211.11187) 
+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.
 
-```
-@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}
-}
-```
+<!--- Describe your model here -->
 
 ## Usage (Sentence-Transformers)
 
@@ -122,4 +69,61 @@ 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**:
+
+`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 88058 with parameters:
+```
+{'batch_size': 32}
+```
+
+**Loss**:
+
+`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
+  ```
+  {'scale': 20.0, 'similarity_fct': 'cos_sim'}
+  ```
+
+Parameters of the fit()-Method:
+```
+{
+    "epochs": 1,
+    "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": 8805,
+    "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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