readme: ---
  pipeline_tag: sentence-similarity
  tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  language:
  - es
  datasets:
  - hackathon-pln-es/nli-es
  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."
  ---
  
  # bertin-roberta-base-finetuning-esnli
  
  This is a [sentence-transformers](https://www.SBERT.net) model trained on a
  collection of NLI tasks for Spanish. It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
  
  Based around the siamese networks approach from [this paper](https://arxiv.org/pdf/1908.10084.pdf).
  <!--- Describe your model here -->
  
  You can see a demo for this model [here](https://huggingface.co/spaces/hackathon-pln-es/Sentence-Embedding-Bertin).
  
  You can find our other model, **paraphrase-spanish-distilroberta** [here](https://huggingface.co/hackathon-pln-es/paraphrase-spanish-distilroberta) and its demo [here](https://huggingface.co/spaces/hackathon-pln-es/Paraphrase-Bertin).
  
  ## 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/bertin-roberta-base-finetuning-esnli')
  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
  
  
  #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('hackathon-pln-es/bertin-roberta-base-finetuning-esnli')
  model = AutoModel.from_pretrained('hackathon-pln-es/bertin-roberta-base-finetuning-esnli')
  
  # 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, mean pooling.
  sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
  
  print("Sentence embeddings:")
  print(sentence_embeddings)
  ```
  
  
  ## Evaluation Results
  
  <!--- Describe how your model was evaluated -->
  Our model was evaluated on the task of Semantic Textual Similarity using the [SemEval-2015 Task](https://alt.qcri.org/semeval2015/task2/) for [Spanish](http://alt.qcri.org/semeval2015/task2/data/uploads/sts2015-es-test.zip). We measure 
  
  |                    | [BETO STS](https://huggingface.co/espejelomar/sentece-embeddings-BETO) | BERTIN STS (this model) | Relative improvement |
  |-------------------:|---------:|-----------:|---------------------:|
  |   cosine_pearson   | 0.609803 | 0.683188   | +12.03               |
  |   cosine_spearman  | 0.528776 | 0.615916   | +16.48               |
  |  euclidean_pearson | 0.590613 | 0.672601   | +13.88               |
  | euclidean_spearman | 0.526529 | 0.611539   | +16.15               |
  |  manhattan_pearson | 0.589108 | 0.672040   | +14.08               |
  | manhattan_spearman | 0.525910 | 0.610517   | +16.09               |
  |     dot_pearson    | 0.544078 | 0.600517   | +10.37               |
  |    dot_spearman    | 0.460427 | 0.521260   | +13.21               |
  
  
  ## Training
  The model was trained with the parameters:
  
  **Dataset**
  
  We used a collection of datasets of Natural Language Inference as training data:
   - [ESXNLI](https://raw.githubusercontent.com/artetxem/esxnli/master/esxnli.tsv), only the part in spanish
   - [SNLI](https://nlp.stanford.edu/projects/snli/), automatically translated
   - [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/), automatically translated
  
  The whole dataset used is available [here](https://huggingface.co/datasets/hackathon-pln-es/nli-es).
  
  Here we leave the trick we used to increase the amount of data for training here:
  ```
   