readme: ---
  pipeline_tag: sentence-similarity
  tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  - transformers
  language:
  - es
  datasets:
  - hackathon-pln-es/parallel-sentences
  widget:
  - text: "A ver si nos tenemos que poner todos en huelga hasta cobrar lo que queramos."
  - text: "La huelga es el método de lucha más eficaz para conseguir mejoras en el salario."
  - text: "Tendremos que optar por hacer una huelga para cobrar lo que queremos."
  - text: "Queda descartada la huelga aunque no cobremos lo que queramos."
  ---
  
  
  # paraphrase-spanish-distilroberta
  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 to train an `bertin-roberta-base-spanish` model using parallel EN-ES 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
  sentences = ["Este es un ejemplo", "Cada oración es transformada"]
  
  model = SentenceTransformer('hackathon-pln-es/paraphrase-spanish-distilroberta')
  embeddings = model.encode(sentences)
  print(embeddings)
  ```
  
  ## 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
  import torch.nn.functional as F
  
  #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 = ['Este es un ejemplo", "Cada oración es transformada']
  
  # Load model from HuggingFace Hub
  tokenizer = AutoTokenizer.from_pretrained('hackathon-pln-es/paraphrase-spanish-distilroberta')
  model = AutoModel.from_pretrained('hackathon-pln-es/paraphrase-spanish-distilroberta')
  
  # 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
  sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
  
  # Normalize embeddings
  sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
  
  print("Sentence embeddings:")
  print(sentence_embeddings)
  ```
  
  ## Full Model Architecture
  ```
  SentenceTransformer(
    (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel 
    (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})
  )
  ```
  
  ## Evaluation Results
  
  Similarity Evaluation on STS-2017.es-en.txt and STS-2017.es-es.txt (translated manually for evaluation purposes)
  
  We measure the semantic textual similarity (STS) between sentence pairs in different languages:
  
  ### ES-ES
  | cosine_pearson | cosine_spearman | manhattan_pearson | manhattan_spearman | euclidean_pearson | euclidean_spearman | dot_pearson | dot_spearman |
  | ----------- | ----------- | ----------- | ----------- | ----------- | ----------- | ----------- | ----------- |
  0.8495 | 0.8579 | 0.8675 | 0.8474 | 0.8676 | 0.8478 | 0.8277 | 0.8258 |
  
  ### ES-EN
  | cosine_pearson | cosine_spearman | manhattan_pearson | manhattan_spearman | euclidean_pearson | euclidean_spearman | dot_pearson | dot_spearman |
  | ----------- | ----------- | ----------- | ----------- | ----------- | ----------- | ----------- | ----------- |
  0.8344 | 0.8448 | 0.8279 | 0.8168 | 0.8282 | 0.8159 | 0.8083 | 0.8145 |
  
  ------
  
  
  ## Intended uses
  
  Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures 
  the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks.
  
  ## Background
  
  This model is a bilingual Spanish-English model trained according to instructions in the paper [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://arxiv.org/pdf/2004.09813.pdf) and the [documentation](https://www.sbert.net/examples/training/multilingual/README.html) accompanying its companion python package. We have used the strongest available pretrained English Bi