{"id":24418754,"url":"https://github.com/amarnarayandwivedi/lstm_text_predictor","last_synced_at":"2026-04-09T21:46:07.487Z","repository":{"id":272151431,"uuid":"915672224","full_name":"AmarNarayanDwivedi/LSTM_Text_Predictor","owner":"AmarNarayanDwivedi","description":"NextWordPredictor is an advanced natural language processing (NLP) model designed to predict the next word in a sequence of text using a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) cells.","archived":false,"fork":false,"pushed_at":"2025-01-12T14:04:44.000Z","size":13181,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-20T09:13:39.216Z","etag":null,"topics":["lstm-neural-networks","python","rnn-tensorflow","streamlit"],"latest_commit_sha":null,"homepage":"https://rnn-lstm-text-predictor.streamlit.app/","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/AmarNarayanDwivedi.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-01-12T13:58:26.000Z","updated_at":"2025-01-12T14:06:01.000Z","dependencies_parsed_at":"2025-01-12T15:29:42.325Z","dependency_job_id":null,"html_url":"https://github.com/AmarNarayanDwivedi/LSTM_Text_Predictor","commit_stats":null,"previous_names":["amarnarayandwivedi/lstm_text_predictor"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AmarNarayanDwivedi%2FLSTM_Text_Predictor","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AmarNarayanDwivedi%2FLSTM_Text_Predictor/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AmarNarayanDwivedi%2FLSTM_Text_Predictor/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AmarNarayanDwivedi%2FLSTM_Text_Predictor/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/AmarNarayanDwivedi","download_url":"https://codeload.github.com/AmarNarayanDwivedi/LSTM_Text_Predictor/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243410621,"owners_count":20286446,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["lstm-neural-networks","python","rnn-tensorflow","streamlit"],"created_at":"2025-01-20T09:13:31.904Z","updated_at":"2025-12-28T21:59:45.075Z","avatar_url":"https://github.com/AmarNarayanDwivedi.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"## Project Description: Next Word Prediction Using LSTM\n#### Project Overview:\n\nThis project aims to develop a deep learning model for predicting the next word in a given sequence of words. The model is built using Long Short-Term Memory (LSTM) networks, which are well-suited for sequence prediction tasks. The project includes the following steps:\n\n1- Data Collection: We use the text of Shakespeare's \"Hamlet\" as our dataset. This rich, complex text provides a good challenge for our model.\n\n2- Data Preprocessing: The text data is tokenized, converted into sequences, and padded to ensure uniform input lengths. The sequences are then split into training and testing sets.\n\n3- Model Building: An LSTM model is constructed with an embedding layer, two LSTM layers, and a dense output layer with a softmax activation function to predict the probability of the next word.\n\n4- Model Training: The model is trained using the prepared sequences, with early stopping implemented to prevent overfitting. Early stopping monitors the validation loss and stops training when the loss stops improving.\n\n5- Model Evaluation: The model is evaluated using a set of example sentences to test its ability to predict the next word accurately.\n\n6- Deployment: A Streamlit web application is developed to allow users to input a sequence of words and get the predicted next word in real-time.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Famarnarayandwivedi%2Flstm_text_predictor","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Famarnarayandwivedi%2Flstm_text_predictor","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Famarnarayandwivedi%2Flstm_text_predictor/lists"}