Metadata-Version: 2.1
Name: sentence-similarity
Version: 1.0.0
Summary: Package to calculate the similarity score of two sentences
Home-page: https://github.com/Susheel-1999/Sentence_Similarity
Author: Susheel
Author-email: susheelnagesh@gmail.com
License: UNKNOWN
Description: # Sentence Similarity
        Package to calculate the similarity score between two sentences
        ## Examples
        ### Using Transformers
        ```python
        from sentence_similarity import sentence_similarity
        sentence_a = "paris is a beautiful city"
        sentence_b = "paris is a grogeous city"
        ```
        #### Supported Models
        You can access some of the official model through the `sentence_similarity` class. However, you can directly type the HuggingFace's model name such as `bert-base-uncased` or `distilbert-base-uncased` when instantiating a `sentence_similarity`.
        
        > See all the available models at [huggingface.co/models](https://huggingface.co/transformers/pretrained_models.html).
        ```python
        model=sentence_similarity(model_name='distilbert-base-uncased',embedding_type='cls_token_embedding')
        ```
        BERT is bidirectional, the [CLS] is encoded including all representative information of all tokens through the multi-layer encoding procedure. The representation of [CLS] is individual in different sentences. 
        Set embedding_type to `cls_token_embedding`, To compute the similarity score between two sentences based on [CLS] token. 
        > paper link (https://arxiv.org/pdf/1810.04805.pdf)
        
        ```python
        score=model.get_score(sentence_a,sentence_b,metric="cosine")
        print(score)
        ```
        Available metric are euclidean, manhattan, minkowski, cosine score.
        
        ### Using Sentence Transformers
        ```python
        from sentence_similarity import sentence_similarity
        sentence_a = "paris is a beautiful city"
        sentence_b = "paris is a grogeous city"
        ```
        #### Supported Models
        You can access all the pretrained models of `Sentence-Transformers`
        
        > See all the available models at [sbert/models](https://www.sbert.net/docs/pretrained_models.html#sentence-embedding-models).
        ```python
        model=sentence_similarity(model_name='distilbert-base-uncased',embedding_type='sentence_embedding')
        ```
        Sentence-BERT (SBERT), a modification of the pretrained BERT network that use siamese and triplet network structures to derive semantically meaningful sentence embeddings that can be compared using cosine-similarity.
        Set embedding_type to `sentence_embedding` (default embedding_type), To compute the similarity score between two sentences based on sbert. 
        > paper link (https://arxiv.org/pdf/1908.10084.pdf)
        ```python
        score=model.get_score(sentence_a,sentence_b,metric="cosine")
        print(score)
        ```
        Available metric are euclidean, manhattan, minkowski, cosine score.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
