update README.md

README.md

@@ -1,20 +1,42 @@
 ---
 pipeline_tag: sentence-similarity
 tags:
-- sentence-transformers
-- feature-extraction
-- sentence-similarity
-- transformers
-
+  - sentence-transformers
+  - feature-extraction
+  - sentence-similarity
+  - transformers
+  - dpr
 ---
 
-# {semantic_roBERTa}
+# `semantic_xlmr`
 
-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 [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**.
 
 <!--- Describe your model here -->
 
-## Usage (Sentence-Transformers)
+## Model Details
+
+- Model name: semantic_xlmr
+- Model version: 1.0
+- Architecture: Sentence Transformer
+- Language: Multilingual ( fine-tuned for Bengali Language)
+
+## Training
+
+The model was fine-tuned using  **Multilingual Knowledge Distillation** method. We took `paraphrase-distilroberta-base-v2` as the teacher model and  `xlm-roberta-large` as the student model.
+
+
+
+![image](https://i.ibb.co/8Xrgnfr/sentence-transformer-model.png)
+
+## Intended Use:
+
+- **Primary Use Case:** Semantic similarity, clustering, and semantic searches
+- **Potential Use Cases:** Document retrieval, information retrieval, recommendation systems, chatbot systems , FAQ system
+
+## Usage
+
+### Using Sentence-Transformers
 
 Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
 
@@ -26,16 +48,15 @@ Then you can use the model like this:
 
 ```python
 from sentence_transformers import SentenceTransformer
-sentences = ["This is an example sentence", "Each sentence is converted"]
+sentences = ["I sing in bengali", "আমি বাংলায় গান গাই"]
 
-model = SentenceTransformer('{MODEL_NAME}')
+model = SentenceTransformer('headlesstech/semantic_xlmr')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
 
+### Using HuggingFace Transformers
 
-
-## Usage (HuggingFace Transformers)
 Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
 
 ```python
@@ -51,11 +72,11 @@ def mean_pooling(model_output, attention_mask):
 
 
 # Sentences we want sentence embeddings for
-sentences = ['This is an example sentence', 'Each sentence is converted']
+sentences = ["I sing in bengali", "আমি বাংলায় গান গাই"]
 
 # Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
-model = AutoModel.from_pretrained('{MODEL_NAME}')
+tokenizer = AutoTokenizer.from_pretrained('headlesstech/semantic_xlmr')
+model = AutoModel.from_pretrained('headlesstech/semantic_xlmr')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -71,53 +92,11 @@ 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 15718 with parameters:
-```
-{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
-```
-
-**Loss**:
-
-`sentence_transformers.losses.MSELoss.MSELoss` 
-
-Parameters of the fit()-Method:
-```
-{
-    "epochs": 3,
-    "evaluation_steps": 0,
-    "evaluator": "NoneType",
-    "max_grad_norm": 1,
-    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
-    "optimizer_params": {
-        "eps": 1e-06,
-        "lr": 2e-05
-    },
-    "scheduler": "WarmupLinear",
-    "steps_per_epoch": null,
-    "warmup_steps": 4715,
-    "weight_decay": 0.01
-}
-```
-
-
 ## Full Model Architecture
+
 ```
 SentenceTransformer(
-  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
   (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})
 )
-```
+```