Search Results for "dataset"

...It distills the GPT architecture into a few hundred lines of Python code, making it far easier to understand than large, production-scale implementations. The repo is organized with a training pipeline (dataset preprocessing, model definition, optimizer, training loop) and inference script so you can train a small GPT on text datasets like Shakespeare or custom corpora. It emphasizes readability and clarity: the training loop is cleanly written, and the code avoids heavy abstractions, letting students follow the architecture step by step. ...

...The code includes different stages: a supervised baseline (i.e. standard summarization training), the reward modeling component, and the reinforcement learning (or preference-based fine-tuning) phase. The repo also includes utilities for dataset handling, modeling architectures, inference, and evaluation. Because the codebase is experimental, parts of it may not run out-of-box depending on dependencies or environment, but it remains a canonical reference for how to implement summarization via human feedback.

Datasets in Education and convenient interface for downloading and preprocessing dataset in education. The CLI tools to quickly convert the "raw" data of the dataset into "mature" data for knowledge tracing task. The "mature" data is in json sequence format and can be modeled by XKT and TKT(TBA) The analysis dataset tool only supports the json sequence format. To check the following statical indexes of the dataset.

Tensorflow 2017 Tutorials is a structured set of tutorials that introduce developers to TensorFlow, starting with basic neural network constructs and progressing to sophisticated model architectures and training techniques. This repository covers essential building blocks like sessions (for older TF versions), placeholders, variables, activation functions, and optimizers, before guiding learners through building end-to-end models for regression, classification, and data pipelines. Beyond the...

The Campaign Simulator is an attempt to statistically model and predict the outcome of an American presidential election; several users assume the roles of competitive candidates and inflict the repercussions of certain decisions on a GSS based dataset.