readme: ---
  pipeline_tag: sentence-similarity
  tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  - transformers
  widget:
  - source_sentence: "hum baccho ko online classes ke liye free laptop dete hai"
  - sentences: ["we provide free laptops to kids for online classes", "hum kids ko padhne ke liye free laptop dete h", "aaj bhut accha din hai"]
  
  ---
  
  LaBSE model finetuned on English and Hindi-Latin (transliterated) similarity task.
  
  LaBSE Research paper: https://ai.googleblog.com/2020/08/language-agnostic-bert-sentence.html
  
  ## Usage (Sentence-Transformers)
  Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
  ```
  pip install -U sentence-transformers==1.2.0
  ```
  Then you can use the model like this:
  
  ```python
  from scipy.spatial.distance import cosine
  def cos_sim(u,v):
      return 1 - cosine(u,v)
      
  from sentence_transformers import SentenceTransformer
  #  sentence-transformer version 1.2.0 is used
  
  model = SentenceTransformer("salesken/similarity-eng-hin_latin")
  
  #source sentence (hindi-latin)
  sentence1 = 'hum baccho ko online classes ke liye free laptop dete hai'
  
  #target sentence (english)
  sentence2 = 'we provide free laptops to kids for online classes '
  
  # it works both the ways (sentence1 sentence2 can be interchanged)
  
  embedding_hin_latin  = model.encode(sentence1)
  embedding_eng = model.encode(sentence2)
  
  cos_sim(embedding_hin_latin,embedding_eng)
  ## 0.978
  
  
  ```
  
  ## 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
  from transformers import AutoTokenizer, AutoModel
  import torch
  # Load model from HuggingFace Hub
    
  tokenizer = AutoTokenizer.from_pretrained("salesken/similarity-eng-hin_latin")
  
  model = AutoModel.from_pretrained("salesken/similarity-eng-hin_latin")
  
  
  
  
  ```
  