ranking-utils

This repository provides miscellaneous utilities for ranking models.

Installation

Clone this repository and run:

python -m pip install .

Usage

Dataset Pre-Processing

The dataset pre-processing script reads a dataset and creates training, dev and test sets (hdf5 format) that can be used by the ranking models. Run the script as follows to see available options:

python -m ranking_utils.scripts.create_h5_data

The following datasets are currently supported:

ANTIQUE
FiQA Task 2
InsuranceQA (v2)
MS MARCO (v1) passage and document ranking (TREC-DL test sets)
Any dataset in generic TREC format

The script uses Hydra. Refer to the documentation for detailed instructions on how to configure arguments.

Example

The following pre-processes the ANTIQUE dataset:

python -m ranking_utils.scripts.create_h5_data \
    dataset=antique \
    dataset.root_dir=/path/to/antique/files \
    hydra.run.dir=/path/to/output/files

In order to see all available options for a dataset, run:

python -m ranking_utils.scripts.create_h5_data \
    dataset=antique \
    --help

Ranking

Implementing a ranker requires two components:

A DataProcessor (specific to your model) subclasses ranking_utils.model.data.DataProcessor and implements the following methods:
- get_model_input(self, query: str, doc: str) -> ModelInput: Transforms a query-document pair into an input that is suitable for the model.
- get_model_batch(self, inputs: Iterable[ModelInput]) -> ModelBatch: Creates a model batch from multiple inputs.
The ranking model itself subclasses ranking_utils.model.Ranker and implements the following methods:
- forward(self, batch: ModelBatch) -> torch.Tensor: Computes query-document scores, output shape (batch_size, 1).
- configure_optimizers(self) -> Tuple[List[Any], List[Any]]: Configures optimizers (and schedulers). Refer to the PyTorch Lightning documentation.

You can then train your model using the usual PyTorch Lightning setup. For example:

from pathlib import Path
from pytorch_lightning import Trainer
from ranking_utils.model.data import H5DataModule
from ranking_utils.model import TrainingMode
from my_ranker import MyRanker, MyDataProcessor

data_module = H5DataModule(
    data_processor=MyDataProcessor(...),
    data_dir=Path(...),
    fold_name="fold_0",
    batch_size=32
)
model = MyRanker(...)
data_module.training_mode = model.training_mode = TrainingMode.PAIRWISE
model.pairwise_loss_margin = 0.2
Trainer(...).fit(model=model, datamodule=data_module)

Validation

After each epoch, the ranker automatically computes the following ranking metrics on the validation set:

val_RetrievalMAP: mean average precision
val_RetrievalMRR: mean reciprocal rank
val_RetrievalNormalizedDCG: nDCG

These can be used in combination with callbacks, e.g. early stopping.

Examples

Example implementations of various models using this library can be found here.

Zaker237 / ranking-utils