3 个仓库
Loads pretrained language models and fine-tunes them on labeled text data to classify documents by sentiment or category.
Distinct from Text Classifiers: Distinct from Text Classifiers: focuses on the fine-tuning process for text classification, not the resulting classifier models themselves.
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This is a structured deep learning curriculum for programmers, delivered as a collection of Jupyter notebooks. It teaches the fundamentals of training neural networks for computer vision, natural language processing, tabular data analysis, and collaborative filtering using PyTorch and the fastai library. The course is designed to be hands-on, guiding learners from building a training loop from scratch to fine-tuning pretrained models for a variety of practical tasks. The curriculum distinguishes itself by covering the full lifecycle of a deep learning project, from data preparation and augmen
Provides a fine-tuning workflow for text classification using pretrained language models on labeled datasets.
Routes queries to specialized models based on academic or professional domains using a fine-tuned classifier.
This project is a technical resource and implementation guide for building transformer-based language model architectures and training pipelines from scratch. It focuses on the design of models capable of natural language processing, including the integration of pretrained weights and the creation of foundational model frameworks. The project specifically emphasizes logical reasoning and mathematical problem solving. It provides a framework for optimizing these capabilities through reinforcement learning and the use of automated verifiers to evaluate and reward correct reasoning paths. The r
Fine-tunes pretrained language models on labeled data to classify text into specific categories.