logitorch 0.0.0

LogiTorch is a pytorch-based library for logical reasoning in natural language

LogiTorch

LogiTorch is a PyTorch-based library for logical reasoning in natural language, it consists of:

• Textual logical reasoning datasets
• Implementations of different logical reasoning neural architectures
• A simple and clean API that can be used with PyTorch Lightning

📦 Installation

📖 Documentation

🖥️ Features

📋 Datasets

Datasets implemented in LogiTorch:

• AR-LSAT
• ConTRoL
• LogiQA
• ReClor
• RuleTaker
• ProofWriter
• SNLI
• MultiNLI
• RTE
• Negated SNLI
• Negated MultiNLI
• Negated RTE
• PARARULES Plus
• AbductionRules

🤖 Models

Models implemented in LogiTorch:

• RuleTaker
• ProofWriter
• BERTNOT
• PRover
• FaiRR
• LReasoner
• DAGN
• Focal Reasoner
• AdaLoGN
• Logiformer
• LogiGAN

🧪 Example Usage

Training Example

import pytorch_lightning as pl
from logitorch.data_collators.ruletaker_collator import RuleTakerCollator
from logitorch.datasets.qa.ruletaker_dataset import RuleTakerDataset
from logitorch.pl_models.ruletaker import PLRuleTaker
from pytorch_lightning.callbacks import ModelCheckpoint
from torch.utils.data.dataloader import DataLoader

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

ruletaker_collate_fn = RuleTakerCollator()
train_dataloader = DataLoader(
    train_dataset, batch_size=32, collate_fn=ruletaker_collate_fn
)

val_dataloader = DataLoader(
    train_dataset, batch_size=32, collate_fn=ruletaker_collate_fn
)

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

checkpoint_callback = ModelCheckpoint(
    save_top_k=1,
    monitor="val_loss",
    mode="min",
    dirpath="models/",
    filename="best_ruletaker.ckpt",
)

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

Testing Example

from logitorch.pl_models.ruletaker import PLRuleTaker
from logitorch.datasets.qa.ruletaker_dataset import RULETAKER_ID_TO_LABEL
import pytorch_lightning as pl

model = PLRuleTaker.load_from_checkpoint("best_ruletaker.ckpt")

context = "Bob is smart. If someone is smart then he is kind"
question = "Bob is kind"

pred = model.predict(context, question)
print(RULETAKER_ID_TO_LABEL[pred])