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--- |
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pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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- feature-extraction |
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- sentence-similarity |
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- transformers |
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- dpr |
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widget: |
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- source_sentence: "আমি বাংলায় গান গাই" |
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sentences: |
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- "I sing in Bangla" |
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- "I sing in Bengali" |
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- "I sing in English" |
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- "আমি গান গাই না " |
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example_title: "Singing" |
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--- |
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# `semantic_xlmr` |
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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**. |
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<!--- Describe your model here --> |
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## Model Details |
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- Model name: semantic_xlmr |
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- Model version: 1.0 |
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- Architecture: Sentence Transformer |
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- Language: Multilingual ( fine-tuned for Bengali Language) |
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## Training |
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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. |
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## Intended Use: |
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- **Primary Use Case:** Semantic similarity, clustering, and semantic searches |
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- **Potential Use Cases:** Document retrieval, information retrieval, recommendation systems, chatbot systems , FAQ system |
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## Usage |
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### Using Sentence-Transformers |
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Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: |
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``` |
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pip install -U sentence-transformers |
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``` |
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Then you can use the model like this: |
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```python |
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from sentence_transformers import SentenceTransformer |
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sentences = ["I sing in bengali", "আমি বাংলায় গান গাই"] |
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model = SentenceTransformer('headlesstech/semantic_xlmr') |
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embeddings = model.encode(sentences) |
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print(embeddings) |
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``` |
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### Using HuggingFace Transformers |
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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. |
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```python |
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from transformers import AutoTokenizer, AutoModel |
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import torch |
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#Mean Pooling - Take attention mask into account for correct averaging |
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def mean_pooling(model_output, attention_mask): |
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token_embeddings = model_output[0] #First element of model_output contains all token embeddings |
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input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() |
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return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) |
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# Sentences we want sentence embeddings for |
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sentences = ["I sing in bengali", "আমি বাংলায় গান গাই"] |
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# Load model from HuggingFace Hub |
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tokenizer = AutoTokenizer.from_pretrained('headlesstech/semantic_xlmr') |
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model = AutoModel.from_pretrained('headlesstech/semantic_xlmr') |
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# Tokenize sentences |
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encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') |
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# Compute token embeddings |
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with torch.no_grad(): |
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model_output = model(**encoded_input) |
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# Perform pooling. In this case, mean pooling. |
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sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) |
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print("Sentence embeddings:") |
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print(sentence_embeddings) |
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``` |
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## Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel |
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(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}) |
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) |
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``` |
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