{"id":25511280,"url":"https://github.com/skrishna-7/nextword-predictor","last_synced_at":"2026-05-04T07:31:16.578Z","repository":{"id":277704761,"uuid":"933252460","full_name":"SKrishna-7/nextword-Predictor","owner":"SKrishna-7","description":"Simple Next Word predictor with LSTM","archived":false,"fork":false,"pushed_at":"2025-02-15T14:36:41.000Z","size":13272,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-15T15:31:52.119Z","etag":null,"topics":["deep-learning","deeplearning-projects","lstm","project","streamlit"],"latest_commit_sha":null,"homepage":"https://skrishna-7-nextword-predictor-app-mk9wgi.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/SKrishna-7.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":"2025-02-15T14:20:17.000Z","updated_at":"2025-02-15T14:37:40.000Z","dependencies_parsed_at":"2025-02-15T15:41:55.648Z","dependency_job_id":null,"html_url":"https://github.com/SKrishna-7/nextword-Predictor","commit_stats":null,"previous_names":["skrishna-7/nextword-predictor"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SKrishna-7%2Fnextword-Predictor","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SKrishna-7%2Fnextword-Predictor/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SKrishna-7%2Fnextword-Predictor/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SKrishna-7%2Fnextword-Predictor/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/SKrishna-7","download_url":"https://codeload.github.com/SKrishna-7/nextword-Predictor/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":239640314,"owners_count":19672995,"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":["deep-learning","deeplearning-projects","lstm","project","streamlit"],"created_at":"2025-02-19T10:31:43.663Z","updated_at":"2025-11-27T08:30:14.222Z","avatar_url":"https://github.com/SKrishna-7.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Next-Word Predictor using LSTM\n\n## Overview\nThis project implements a next-word prediction model using Long Short-Term Memory (LSTM) networks. The dataset used for training is *The Tragedie of Hamlet by William Shakespeare (1599)*. The model takes a sequence of words as input and predicts the most probable next word.\n\n## Dataset\nThe dataset consists of the text from *Hamlet*, preprocessed to remove unnecessary characters and formatted into sequences for training the LSTM model.\n\n## Technologies Used\n- **Python**\n- **TensorFlow \u0026 Keras**\n- **Natural Language Processing (NLP)**\n- **LSTM Neural Networks**\n- **Streamlit** (for deployment)\n\n## Workflow\n\n* 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* 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* 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* 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* Model Evaluation: The model is evaluated using a set of example sentences to test its ability to predict the next word accurately.\n* 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\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fskrishna-7%2Fnextword-predictor","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fskrishna-7%2Fnextword-predictor","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fskrishna-7%2Fnextword-predictor/lists"}