facebookresearch/pytextArchived

Features

• Natural Language Processing - An extensible PyTorch-based framework for building, training, and deploying custom NLP models like text classifiers and sequence taggers.
• Joint Intent-Slot Models - Creates joint models that predict both intent and slot labels from a single utterance.
• Distributed Training Accelerators - Scales NLP model training across multiple GPUs or nodes using PyTorch's distributed data parallel primitives.
• Distributed Training Scaling Utilities - Distributes training across multiple GPUs or nodes for faster iteration on NLP models.
• Slot Extraction Systems - Predicts user intent and extracts slot values from spoken utterances for natural language understanding applications.
• Distributed Training - Scales model training across multiple GPUs or machines using a distributed backend.
• Task-Specific Heads - Ships pre-built neural network heads for classification, sequence tagging, and intent-slot prediction.
• Pluggable Registries - Provides pluggable registries for model architectures, data formats, and tensorizers that can be extended without modifying core code.
• Structured Experiment Configurations - Provides a YAML-driven configuration system for specifying model hyperparameters, data pipelines, and training settings.
• Production Inference Exports - Exports trained NLP models into optimized TorchScript graphs for low-latency production serving.
• NLP Training Toolkits - Provides a modular PyTorch-based pipeline for training text classifiers, sequence taggers, and intent-slot models.
• Modular Assembly Toolkits - Provides a modular toolkit for assembling text classifiers, sequence taggers, and intent-slot models using PyTorch components.
• Sequence Tagger Training - Trains models that label each token in a sequence, such as for named entity recognition.
• Sequence Tagging Frameworks - Provides a framework for labeling each token in text sequences with tags such as named entities using neural network architectures.
• Text Classification Frameworks - Provides a framework for training deep-learning text classifiers with convolutional or self-attentive architectures on labeled utterances.
• Configurable Text Classifiers - Trains deep-learning text classifiers from labeled utterances using configurable architectures.
• Text Classifier Training - Trains deep-learning models to sort text into predefined categories using configurable architectures.
• PyTorch Training Loops - Leverages PyTorch's autograd and distributed data parallel primitives to orchestrate single-node and multi-GPU training.
• NLP Model Assemblers - Assembles new text classifiers, sequence taggers, or intent-slot models by extending modular components.
• Modular Model Assemblers - Assembles text classifiers, sequence taggers, or intent-slot models using modular components.
• Mixed Precision Training - Accelerates NLP model training and reduces memory usage via automatic mixed precision with PyTorch AMP.
• Modular Component Extensions - Adds new model components, data formats, and tensorizers through modular interfaces.
• Model Predictions - Runs inference on raw text input using an exported model to output predicted class labels.
• Model Exporting - Converts trained NLP models into optimized execution graphs for low-latency production inference.
• Static Graph Exports - Transforms trained PyTorch models into static execution graphs via TorchScript for low-latency inference.
• Frameworks And Libraries - Deep learning-based NLP modeling framework built for scale.
• Natural Language Processing - Framework for natural language modeling.
• NLP - NLP modeling framework from Facebook.
• PyTorch Utilities - Listed in the “PyTorch Utilities” section of the The Incredible Pytorch awesome list.

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