Welcome to LogiTorch’s documentation!

LogiTorch is a PyTorch-based library for logical reasoning on natural language. It provides:

  • Textual logical reasoning datasets - Access to numerous benchmark datasets for logical reasoning tasks

  • Neural architecture implementations - State-of-the-art models for logical reasoning

  • Clean PyTorch Lightning API - Simple and extensible interface for training and evaluation

Installation

Install LogiTorch using pip:

pip install logitorch

Or install from source:

pip install git+https://github.com/LogiTorch/logitorch.git

Quick Start

Here’s a simple example to get started with LogiTorch:

import pytorch_lightning as pl
from torch.utils.data.dataloader import DataLoader

from logitorch.data_collators.ruletaker_collator import RuleTakerCollator
from logitorch.datasets.qa.ruletaker_dataset import RuleTakerDataset
from logitorch.pl_models.ruletaker import PLRuleTaker

# Load datasets
train_dataset = RuleTakerDataset("depth-5", "train")
val_dataset = RuleTakerDataset("depth-5", "val")

# Create data loaders
collate_fn = RuleTakerCollator()
train_dataloader = DataLoader(train_dataset, batch_size=32, collate_fn=collate_fn)
val_dataloader = DataLoader(val_dataset, batch_size=32, collate_fn=collate_fn)

# Initialize model
model = PLRuleTaker(learning_rate=1e-5, weight_decay=0.1)

# Train
trainer = pl.Trainer(accelerator="gpu", devices=1)
trainer.fit(model, train_dataloader, val_dataloader)

Documentation

API Reference:

Indices and tables