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PaddleNLP is a development library and toolkit for training, fine-tuning, and deploying large and small language models using the PaddlePaddle framework. It provides a comprehensive suite for the entire natural language processing lifecycle, from model development to high-performance inference. The project features a standardized model zoo for loading and managing pre-trained models and tokenizers through a unified interface. It distinguishes itself with a specialized model compression framework that reduces memory footprints via weight precision conversion and lossless size optimization, alo
Dolly is an instruction-tuned large language model designed to follow complex natural language directions. It operates as a causal language model that predicts the next token in a sequence to generate coherent conversational responses and perform tasks such as brainstorming, classification, and question answering. The project focuses on the development of models using open datasets suitable for commercial application. It enables the creation of instruction-following models by utilizing curated collections of human-generated instruction-response pairs. The repository provides capabilities for
This repository provides a collection of deep learning models and neural network architectures built for natural language processing tasks. It functions as a library of pre-trained models designed to process, analyze, and generate human language data using the TensorFlow framework. The project utilizes sequence-to-sequence modeling and layered neural architectures to handle variable-length language data. By employing static dataflow graphing and tensor-based representations, the models execute mathematical operations to transform input features into abstract linguistic meanings. Users can loa
This project is a comprehensive collection of educational examples and reference implementations for building vision and language models using PyTorch. It serves as a deep learning tutorial covering the end-to-end process of developing neural networks, from initial architecture definition to final production deployment. The repository provides detailed guides on implementing a wide range of domain-specific models, including convolutional neural networks for object detection and segmentation, as well as transformer and recurrent architectures for natural language processing. It emphasizes gene
Transformers Tasks is a collection of toolkits and scripts dedicated to language model fine-tuning, natural language processing tasks, and transformer-based pipelines. The project functions as a natural language processing toolkit and transformer pipeline library, providing Python scripts and algorithms designed to adapt foundational language models and route text inputs through modular processing workflows.
The main features of harderthenharder/transformers_tasks are: Natural Language Processing, Language Model Fine-Tuning, Reinforcement Learning Alignment, Named Entity Recognition, Natural Language Generation, Pre-trained Transformer Architectures, Supervised Fine-Tuning, Transformer-Based NLP Libraries.
Open-source alternatives to harderthenharder/transformers_tasks include: paddlepaddle/paddlenlp — PaddleNLP is a development library and toolkit for training, fine-tuning, and deploying large and small language… databrickslabs/dolly — Dolly is an instruction-tuned large language model designed to follow complex natural language directions. It operates… mesolitica/nlp-models-tensorflow — This repository provides a collection of deep learning models and neural network architectures built for natural… tingsongyu/pytorch_tutorial — This project is a comprehensive collection of educational examples and reference implementations for building vision… stanfordnlp/stanza — Stanza is a Python natural language processing library designed for tokenization, lemmatization, and dependency… 01-ai/yi — Yi is a bilingual language model and foundation model designed for natural language processing, reasoning, and reading…