Open Source Pre-training Model Framework in PyTorch & Pre-trained Model Zoo
Las características principales de dbiir/uer-py son: Pre-trained Language Models, Transformer Implementations, Corpus and Datasets.
Las alternativas de código abierto para dbiir/uer-py incluyen: codertimo/bert-pytorch. huggingface/transformers — Transformers is a comprehensive library for machine learning that provides a unified interface for training,… morizeyao/gpt2-chinese — GPT2-Chinese is a Chinese language model implementation based on the GPT-2 architecture. It provides a causal language… nielsrogge/transformers-tutorials — This is a collection of tutorials and practical demonstrations for implementing machine learning tasks using the… huggingface/course — This project is an educational course and learning curriculum for implementing and fine-tuning transformer models… morvanzhou/tutorials — This repository is a comprehensive collection of instructional guides and practical examples for Python development,…
Transformers is a comprehensive library for machine learning that provides a unified interface for training, fine-tuning, and deploying transformer-based models. It supports a wide range of tasks, including text classification, language modeling, question answering, and sequence-to-sequence translation, while offering specialized architectures for both text and vision processing. The framework includes tools for managing the entire model lifecycle, from data preprocessing and tokenization to distributed training and inference. The library features extensive support for model optimization and
GPT2-Chinese is a Chinese language model implementation based on the GPT-2 architecture. It provides a causal language model trainer and a natural language generation tool designed for training and generating human-like Chinese text sequences. The system integrates a BERT tokenizer to process Chinese corpora into manageable units for machine learning. It enables the development of predictive text models that can generate specific patterns, such as news or poetry, through prompt-based text completion. The project covers a full workflow including text tokenization, model training using a trans
This is a collection of tutorials and practical demonstrations for implementing machine learning tasks using the HuggingFace Transformers library. It serves as a guide for applying transformer architectures across computer vision, natural language processing, and audio analysis. The repository provides implementation examples for multimodal model deployment, including the combination of text, image, and audio inputs. It includes resources for optimizing pre-trained models through fine-tuning on custom datasets and provides examples for preparing PyTorch datasets by converting raw files into t
