PCL

This is the implementation repostiory for the completition PCL.

Timeline:

Evaluation Start: January 10, 2022
Evaluation End: January 31, 2022
Paper submissions due: February 23, 2022
Notification to authors: March 31, 2022

Experiment in Progress

Models	Description	Precion	Recall	weighted average F1	Accuracy F1	Remark
GaussianNB	TFIDF Feature	.70	.69	.69	68.7	partial towards class 0
GaussianNB	CV Feature	.75	.21	.30	21.4
MultinomialNB	TFIDF Feature	.67	.82	.73	81.6	Highly biased
MultinomialNB	CV Feature	.74	.76	.75	76.2	1. overall accc,pre,re is stable throughout.2.class 2 doesn,t given any weightage
Logistic	CV Feature	.87	.89	.88	89	CM matrix 0,0 value very in comparison to other three value
Logistic	TFIDF Feature	.87	.90	.86	90	Confusion matrix unevenly distributed
Logistic	CV Feature,weighted	.87	.80	.83	.80	cm well distributed
Logistic	TFIDF, Weighted	.89	.87	.88	87	Cm Well distributed
Logistic	TFIDF, Weighted(W2V)	.89	.88	.88	.88	Cm Well distributed
Logistic	CV, Weighted(W2V)	.88	.79	.83	.79	Cm Well distributed
SVM	CV,Weighted	.88	.84	.85	84	contribution from both classes
SVM	CV,W2V	.87	.83	.85	83	CM well distributed
MLP	CV,W2V	.87	.89	.87	89
MLP	TFIDF,W2V	.88	.90	.88	90
SGD	CV,W2V	.86	.88	.87	88
SGD Classifier
XGBoost

current ToDo's

• Bert MLP weighted.(1)
• Bert Bi-lstm attention(wt) (with Or without weighted)
• Fix dataset Imbalance (Aman)
• Bert, XLM, GPT, Distill- bert, roberta(hugging Face)(2)
• Toxic Bert, News Bert (hugging Face)(4)
• Task-2 data analysis (3)
• Task-2 Model - logistic, MLP, Bi-lstm, Bert (2) note input to the model : sentence + span interval (maybe + span again)**
• Adversial training (Aman)
• B-LSTM (Aman)

To do

• Implement Baseline

MODELS	Ready
Logistic Regression	x
Naive Bayes	x
SVM	x
MLP	x
SGD Classifier	x
XGBoost	x
Bi-LSTM

• Features: countvectorizer, tfidfvectorizer, word2vec (local), gloVe, elmo, BERT

• Discuss data analysis

• List-down main models

MODELS	F1
BERT