readme: ---
  
  language:
  - en
  - el
  
  tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  - transformers
  
  
  widget:
  - source_sentence: "Το κινητό έπεσε και έσπασε."
    sentences: [
  				"H πτώση κατέστρεψε τη συσκευή.",
  				"Το αυτοκίνητο έσπασε στα δυο.",
  				"Ο υπουργός έπεσε και έσπασε το πόδι του."
  			]
  
  pipeline_tag: sentence-similarity
  
  license: apache-2.0
  ---
  
  # Semantic Textual Similarity for the Greek language using Transformers and Transfer Learning
  ### By the Hellenic Army Academy (SSE) and the Technical University of Crete (TUC)
  
  
  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.
  
  We follow a Teacher-Student transfer learning approach described [here](https://www.sbert.net/examples/training/multilingual/README.html) to train an XLM-Roberta-base model on STS using parallel EN-EL sentence pairs.
  
  ## Usage (Sentence-Transformers)
  
  Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
  
  ```
  pip install -U sentence-transformers
  ```
  
  Then you can use the model like this:
  
  ```python
  from sentence_transformers import SentenceTransformer
  model = SentenceTransformer('{MODEL_NAME}')
  
  sentences1 = ['Το κινητό έπεσε και έσπασε.',
               'Το κινητό έπεσε και έσπασε.',
               'Το κινητό έπεσε και έσπασε.']
  
  sentences2 = ["H πτώση κατέστρεψε τη συσκευή.",
               "Το αυτοκίνητο έσπασε στα δυο.",
               "Ο υπουργός έπεσε και έσπασε το πόδι του."]
  
  
  embeddings1 = model.encode(sentences1, convert_to_tensor=True)
  embeddings2 = model.encode(sentences2, convert_to_tensor=True)
  
  #Compute cosine-similarities (clone repo for util functions)
  from sentence_transformers import util
  cosine_scores = util.pytorch_cos_sim(embeddings1, embeddings2)
  
  #Output the pairs with their score
  for i in range(len(sentences1)):
      print("{} 		 {} 		 Score: {:.4f}".format(sentences1[i], sentences2[i], cosine_scores[i][i]))
      
  #Outputs:
  #Το κινητό έπεσε και έσπασε. 		 H πτώση κατέστρεψε τη συσκευή. 		 Score: 0.6741
  #Το κινητό έπεσε και έσπασε. 		 Το αυτοκίνητο έσπασε στα δυο. 		 Score: 0.5067
  #Το κινητό έπεσε και έσπασε. 		 Ο υπουργός έπεσε και έσπασε το πόδι του. 		 Score: 0.4548
  
  ```
  
  
  
  ## 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
  
  
  #Mean Pooling - Take attention mask into account for correct averaging
  def mean_pooling(model_output, attention_mask):
      token_embeddings = model_output[0] #First element of model_output contains all token embeddings
      input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
      return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
  
  
  # Sentences we want sentence embeddings for
  sentences = ['This is an example sentence', 'Each sentence is converted']
  
  # Load model from HuggingFace Hub
  tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
  model = AutoModel.from_pretrained(
  
  # Tokenize sentences
  encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
  
  # Compute token embeddings
  with torch.no_grad():
      model_output = model(**encoded_input)
  
  # Perform pooling. In this case, max pooling.
  sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
  
  print("Sentence embeddings:")
  print(sentence_embeddings)
  ```
  
  
  
  ## Evaluation Results
  
  #### Similarity Evaluation on STS.en-el.txt (translated manually for evaluation purposes)
  We measure the semantic textual similarity (STS) between sentence pairs in different languages:
  
  | cosine_pearson | cosine_spearman | euclidean_pearson | euclidean_spearman | manhattan_pearson | manhattan_spearman | dot_pearson | dot_spearman |
  | ----------- | ----------- | ----------- | ----------- | ----------- | ----------- | ----------- | ----------- |
  0.834474802920369 | 0.845687403828107 | 0.815895882192263 | 0.81084300966291 | 0.816333562677654 | 0.813879742416394 | 0.7945167996031 | 0.802604238383742 |
  
  #### Translation
  We measure the translation accuracy. Given a list with source sentences, for example, 1000 English sentences. And a list with matching target (translated) sentences, for example, 1000 Greek sentences. For each sentence pair, we check if their embeddings are the closest using cosine similarity. I.e., for each src_sentences[i] we check if trg_sentences[i] has the highest similarity out of all target sentences. If this is the case, we have a hit, otherwise an error. This evaluator reports accuracy (higher = better).
  
  | src2trg | trg2src |
  | ----------- | ----------- |
  | 0.981 |	0.9775 | 
  
  ## Training
  