With all of the tweets circulating every second it is hard to tell whether the sentiment behind a specific tweet will impact a company, or a person's, brand for being viral (positive), or devastate profit because it strikes a negative tone. Capturing sentiment in language is important in these times where decisions and reactions are created and updated in seconds. But, which words actually lead to the sentiment description? In this competition you will need to pick out the part of the tweet (word or phrase) that reflects the sentiment. The project consists in developing a model for the extraction of feelings, competition rules and datasets used in the following link: https://www.kaggle.com/c/tweet-sentiment-extraction
The uploaded data contains csv files of train dataset, test dataset and the submission file https://www.kaggle.com/c/tweet-sentiment-extraction/data
For installing requirements : pip install requirements.txt
- The file used for train and generate model is https://github.com/achrafBenHamou/Deep_Learning/blob/main/Gru_Model/Gru_model.ipynb
- Jupyter notebook file contains final result of pridected selected_text
- Generated model is https://github.com/achrafBenHamou/Deep_Learning/blob/main/Gru_Model/Gru_model.h5
- The file used for train and generate model is https://github.com/achrafBenHamou/Deep_Learning/blob/main/Lstm_Model/Lstm_model.ipynb
- Jupyter notebook file contains final result of pridected selected_text
- Generated model is https://github.com/achrafBenHamou/Deep_Learning/blob/main/Lstm_Model/Lstm_model.h5
- This model is based on RoBERTa pretrained model: A Robustly Optimized BERT Pretraining Approach by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov. It is based on Google’s BERT model released in 2018. https://huggingface.co/transformers/model_doc/roberta.html
- Using Stratified K-Folds cross-validator.It Provides train/test indices to split data in train/test sets. This cross-validation object is a variation of KFold that returns stratified folds. The folds are made by preserving the percentage of samples for each class
- The file used for train, generate and test model is Roberta_model.py
- Roberta_model.py generate 5 models with jaccard similarity nearly 50 %
- the final file producted for submission is the file https://github.com/achrafBenHamou/Deep_Learning/blob/main/Data/submission.csv