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PyTorch original implementation of Cross-lingual Language Model Pretraining.
The main features of facebookresearch/xlm are: Language Modeling, Memory Systems, Natural Language Processing, Pre-trained Language Models, General NLP.
Projects with overlapping indexed features include: huggingface/transformers — Transformers is a comprehensive library for machine learning that provides a unified interface for training,… codertimo/bert-pytorch. openai/gpt-2 — This project is a transformer-based language model and autoregressive text generator designed to predict the next… freewym/espresso. facebookresearch/muse — A library for Multilingual Unsupervised or Supervised word Embeddings. asyml/texar — Toolkit for Machine Learning, Natural Language Processing, and Text Generation, in TensorFlow. This is part of the…
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 a transformer-based language model and autoregressive text generator designed to predict the next token in a sequence to produce human-like prose and synthetic text. It functions as a large language model that utilizes a transformer architecture to learn linguistic patterns from large datasets for unsupervised multitask learning. The repository provides a distribution of pre-trained weights, enabling natural language processing tasks without requiring additional training. This allows the model to perform zero-shot task generalization by applying learned patterns to new tasks.
