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The first part of this project will be the implementation of an information retrieval system using the noclean version of the C4 dataset used by Google to train their T5 model. The second part will be to classify if the a document is reliable

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Dataset_Visualize_Imbalance.py
Dataset_Visualize_Imbalance.py
 
 
Figure_1.png
Figure_1.png
 
 
Figure_2.png
Figure_2.png
 
 
Figure_3.png
Figure_3.png
 
 
Figure_4.png
Figure_4.png
 
 
Figure_Emoji_Average_Estimator.png
Figure_Emoji_Average_Estimator.png
 
 
Figure_HeatMap_Num_Emoji.png
Figure_HeatMap_Num_Emoji.png
 
 
Figure_Num_Bad_Words.png
Figure_Num_Bad_Words.png
 
 
Figure_Num_Bad_Words_Heat_Map.png
Figure_Num_Bad_Words_Heat_Map.png
 
 
Figure_average_bad_words.png
Figure_average_bad_words.png
 
 
FineTuningTransformers.ipynb
FineTuningTransformers.ipynb
 
 
README.md
README.md
 
 
compatibility.py
compatibility.py
 
 
creating_original_dataset.py
creating_original_dataset.py
 
 
creating_queriese.py
creating_queriese.py
 
 
descision_tree_glove.py
descision_tree_glove.py
 
 
descision_tree_tf_idf.py
descision_tree_tf_idf.py
 
 
descision_tree_word2vec.py
descision_tree_word2vec.py
 
 
finding_similar_words_with_word2vec.py
finding_similar_words_with_word2vec.py
 
 
finding_similarity_with_glove.py
finding_similarity_with_glove.py
 
 
lesk_algorithm.py
lesk_algorithm.py
 
 
lesk_algorithm_iplementation.py
lesk_algorithm_iplementation.py
 
 
lesk_algorithm_unit.py
lesk_algorithm_unit.py
 
 
logistic_regresion_word2vec.py
logistic_regresion_word2vec.py
 
 
logistic_regression_glove.py
logistic_regression_glove.py
 
 
logistic_regression_tf_idf.py
logistic_regression_tf_idf.py
 
 
misinfo-2021-topics.xml
misinfo-2021-topics.xml
 
 
misinfo-qrels-graded.usefulness
misinfo-qrels-graded.usefulness
 
 
partB.py
partB.py
 
 
relevalnce_qrels.py
relevalnce_qrels.py
 
 
relevance_qrels.txt
relevance_qrels.txt
 
 
test.csv
test.csv
 
 
test_wordnet.py
test_wordnet.py
 
 
train_glove_to_my_dataset.py
train_glove_to_my_dataset.py
 
 
train_svm_glove.py
train_svm_glove.py
 
 
train_svm_tf_idf.py
train_svm_tf_idf.py
 
 
train_word2vec.py
train_word2vec.py
 
 
training_svm_word2vec.py
training_svm_word2vec.py
 
 
trec_topics.txt
trec_topics.txt
 
 
trec_topics_terrier_with_desc_narr.txt
trec_topics_terrier_with_desc_narr.txt
 
 
trec_topics_terrier_with_lesk_algorithm.txt
trec_topics_terrier_with_lesk_algorithm.txt
 
 
val.csv
val.csv
 
 
visualize_causality_between_emoji_credibility.py
visualize_causality_between_emoji_credibility.py
 
 
visualize_word2vec_embedings.py
visualize_word2vec_embedings.py
 
 

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"# Detecting-Misinformation-on-Internet" The first part of this project will be the implementation of an information retrieval system using the noclean version of the C4 dataset used by Google to train their T5 model. The second part will be to classify if the a document is reliable

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The first part of this project will be the implementation of an information retrieval system using the noclean version of the C4 dataset used by Google to train their T5 model. The second part will be to classify if the a document is reliable

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