Name : Prisha Sawhney

Roll Number : 102116052

Group: 3CS10

Task at Hand

We need to find which, among a given set of pre-trained text-classification models, has the best performance based on different evaluation metrics. For this, we will use the method of TOPSIS - Technique for Order of Preference by Similarity to Ideal Solution

Models used

Here, 4 models based on text-classification are being imported:

1. distilbert/distilbert-base-uncased-finetuned-sst-2-english
2. lxyuan/distilbert-base-multilingual-cased-sentiments-student
3. cardiffnlp/twitter-roberta-base-sentiment-latest
4. siebert/sentiment-roberta-large-english

We have created a sample dataset for the three different genres of the world, namely Education, Sports, Politics and Finance, in order to test the model for different metrics

Result

Hence, we have the following result

Domain	Best Model	Model Name
Education	Model 2	lxyuan/distilbert-base-multilingual-cased-sentiments-student
Sports	Model 4	siebert/sentiment-roberta-large-english
Politics	Model 4	siebert/sentiment-roberta-large-english
Finance	Model 4	siebert/sentiment-roberta-large-english

Dataset is available in "Education.csv", "Sports.csv", "Politics.csv" and "Finance.csv" files
Python Code is available in "Models.ipynb" jupyter notebook