Update README.md

README.md

@@ -1,3 +1,32 @@
----
-license: mit
----
+# 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