| # 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 |