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This package provides spaCy components and architectures to use transformer models via Hugging Face's transformers in spaCy. The result is convenient access to state-of-the-art transformer architectures, such as BERT, GPT-2, XLNet, etc.
The main features of explosion/spacy-transformers are: Natural Language Processing, Transformer Implementations.
Projects with overlapping indexed features include: huggingface/transformers — Transformers is a comprehensive library for machine learning that provides a unified interface for training,… nvidia/nemo — NeMo is a multimodal AI framework and toolkit designed for the development, training, and scaling of large language… codertimo/bert-pytorch. google-research/bert — This project is a transformer-based language model and natural language processing toolkit designed to generate deep… kimiyoung/transformer-xl — This project is an implementation of the Transformer-XL language model, a neural network architecture designed for… pytorch/fairseq — Fairseq is a deep learning research toolkit and sequence-to-sequence framework built on PyTorch. It provides a system…
This project is a transformer-based language model and natural language processing toolkit designed to generate deep contextual representations of text. By utilizing a transformer-based encoder architecture, the system processes input sequences through stacked self-attention layers to capture the semantic meaning of tokens based on their surrounding sentence structure. The model distinguishes itself through bidirectional contextual processing, which analyzes text in both directions simultaneously, and masked language modeling, which trains the system by predicting hidden tokens within a seque
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
This project is an implementation of the Transformer-XL language model, a neural network architecture designed for long-context language modeling. It provides frameworks for training and deploying models that capture long-term dependencies and relationships in text sequences that extend beyond a fixed context window. The implementation supports both PyTorch and TensorFlow, allowing for distributed training across multiple GPUs and host nodes. It employs a recurrent mechanism to maintain coherence in extended sequences, utilizing segment-level recurrence and state-based memory reuse. The code
