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
bert4keras is a lightweight reimplementation of the BERT transformer architecture for the Keras deep learning framework. It serves as a natural language processing toolkit and transformer model library used for text classification, sequence labeling, and semantic embedding extraction. The framework includes a sequence-to-sequence model system for question answering and text generation, as well as a model inference server to deploy trained transformers as web APIs for real-time predictions. Capabilities cover a broad range of natural language understanding tasks, including reading comprehensi
PyTorch implementation of BERT in "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding" (https://arxiv.org/abs/1810.04805)
The main features of dreamgonfly/bert-pytorch are: Transformer Implementations.
Open-source alternatives to dreamgonfly/bert-pytorch include: nielsrogge/transformers-tutorials — This is a collection of tutorials and practical demonstrations for implementing machine learning tasks using the… codertimo/bert-pytorch. cyberzhg/keras-bert — \[中文|English\]. dbiir/uer-py — Open Source Pre-training Model Framework in PyTorch & Pre-trained Model Zoo. deepset-ai/farm — :house_with_garden: Fast & easy transfer learning for NLP. Harvesting language models for the industry. Focus on… bojone/bert4keras — bert4keras is a lightweight reimplementation of the BERT transformer architecture for the Keras deep learning…
