# 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
{}
transformers pytorch distilbert text-classification autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us 

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: A man inspects the uniform of a figure in some East Asian country., Label: contradiction, Hypothesis: The man is sleeping.
Premise: An older and younger man smiling., Label: neutral, Hypothesis: Two men are smiling and laughing at the cats playing on the floor.
Premise: A soccer game with multiple males playing., Label: entailment, Hypothesis: Some men are playing a sport.


### Stanford NLI (SNLI) corpus


In this labwork, I propose to use the Stanford NLI (SNLI) corpus ( URL ), available in the *Datasets* library by Huggingface.



```
from datasets import load_dataset
snli = load_dataset("snli")
#Removing sentence pairs with no label (-1)
snli = URL(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



### Stanford NLI (SNLI) corpus


In this labwork, I propose to use the Stanford NLI (SNLI) corpus ( URL ), available in the *Datasets* library by Huggingface.



```
from datasets import load_dataset
snli = load_dataset("snli")
TAGS
#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us 
 ### Stanford NLI (SNLI) corpus


In this labwork, I propose to use the Stanford NLI (SNLI) corpus ( URL ), available in the *Datasets* library by Huggingface.



```
from datasets import load_dataset
snli = load_dataset("snli")
passage: TAGS
#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us 
### Stanford NLI (SNLI) corpus


In this labwork, I propose to use the Stanford NLI (SNLI) corpus ( URL ), available in the *Datasets* library by Huggingface.



```
from datasets import load_dataset
snli = load_dataset("snli")