NiharJani2002
commented
6 months ago Request: To Assign Me This Issue
--> Full Name: Nihar Mahesh Jani --> Github Profile Link: https://github.com/NiharJani2002 --> Email id: nihar.j@ahduni.edu.in --> Participant id: https://quine.sh/user/NiharJani2002
--> Approach for this Project :
- Exploratory Data Analysis (EDA):
Load and clean the data: Address missing values or inconsistencies. Verify data quality and integrity. Understand the distribution of features: Visualize word frequencies, token lengths, and other relevant metrics. Explore potential correlations or patterns. Identify potential biases or limitations: Assess domain specificity or distributional biases. Consider ethical implications and responsible use.
- Model Selection and Implementation:
Choose 3-4 suitable algorithms: Recurrent Neural Networks (RNNs): LSTM or GRU architectures for sequential modeling. Transformer-based models: BERT, DistilBERT, or ALBERT for masked language modeling. Contextual embedding models: Word2Vec or GloVe for word-level representations. Other potential options: Attention-based RNNs, CNN-LSTM hybrids. Implement each model: Leverage libraries like TensorFlow, PyTorch, or Hugging Face Transformers. Carefully consider hyperparameter tuning and optimization strategies.
- Model Training and Evaluation:
Split the dataset: Training set for model learning. Validation set for hyperparameter tuning and model selection. Test set for final evaluation. Train each model: Monitor training progress and adjust hyperparameters as needed. Evaluate performance: Use accuracy scores as a primary metric. Consider additional metrics like precision, recall, F1-score, perplexity, or ROUGE scores based on specific analysis goals. Analyze error patterns to identify areas for improvement.
- Comparison and Selection:
Compare accuracy scores and other relevant metrics across models. Consider model complexity, training time, and interpretability. Select the best-performing model based on a comprehensive assessment.
- Additional Considerations:
Experiment with different hyperparameters and model architectures. Explore techniques like transfer learning or fine-tuning to leverage pre-trained models. Consider using attention mechanisms for better interpretability. Visualize model outputs and attention weights for qualitative insights. Incorporate error analysis and feedback loops to improve model performance iteratively. Document the process, findings, and conclusions rigorously for reproducibility and knowledge sharing.
-->What is your participant role? (Mention the Open Source program): SWOC S4
abhisheks008
AgrawalTitiksha
khushi-igupta
Deep Learning Simplified Repository (Proposing new issue)
:red_circle: Project Title : MLM Analysis of ChatGPT using NLP :red_circle: Aim : The aim of this project is to analyze the MLM model using NLP methods. :red_circle: Dataset : https://www.kaggle.com/datasets/pe4eniks/chatgpt-for-mlm :red_circle: Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.
📍 Follow the Guidelines to Contribute in the Project :
requirements.txt- This file will contain the required packages/libraries to run the project in other machines.Modelfolder, theREADME.mdfile must be filled up properly, with proper visualizations and conclusions.:red_circle::yellow_circle: Points to Note :
:white_check_mark: To be Mentioned while taking the issue :
Happy Contributing 🚀
All the best. Enjoy your open source journey ahead. 😎