readme: # CentraleSupelec - Natural language processing
  # Practical session n°7
  
  ## Natural Language Inferencing (NLI): 
  
  (NLI) is a classical NLP (Natural Language Processing) problem that involves taking two sentences (the premise and the hypothesis ), and deciding how they are related (if the premise *entails* the hypothesis, *contradicts* it, or *neither*).
  
  Ex: 
  
  
  | Premise | Label | Hypothesis |
  | --- | --- | --- |
  | A man inspects the uniform of a figure in some East Asian country. | contradiction | The man is sleeping. |
  | An older and younger man smiling. | neutral | Two men are smiling and laughing at the cats playing on the floor. |
  | A soccer game with multiple males playing. | entailment | Some men are playing a sport. |
  
  ### Stanford NLI (SNLI) corpus
  
  In this labwork, I propose to use the Stanford NLI (SNLI) corpus ( https://nlp.stanford.edu/projects/snli/ ), available in the *Datasets* library by Huggingface.
  
      from datasets import load_dataset
      snli = load_dataset("snli")
      #Removing sentence pairs with no label (-1)
      snli = snli.filter(lambda example: example['label'] != -1) 
  
  ## Quick summary of the model
  
  This is the model from : Youssef Adarrab, Othmane Baziz and Alain Malige
  
  - Fist we import the corpus and do some visualization
  - Second we apply DistilBert for sequence classification
  - We illustrate through our work the code used for training, to obtain better results, one should run the training on more epochs