eval_beir.py

# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

import sys
import argparse
import torch
import logging
import json
import numpy as np
import os

import src.slurm
import src.contriever
import src.beir_utils
import src.utils
import src.dist_utils
import src.contriever

logger = logging.getLogger(__name__)


def main(args):

    src.slurm.init_distributed_mode(args)
    src.slurm.init_signal_handler()

    os.makedirs(args.output_dir, exist_ok=True)

    logger = src.utils.init_logger(args)

    model, tokenizer, _ = src.contriever.load_retriever(args.model_name_or_path)
    model = model.cuda()
    model.eval()
    query_encoder = model
    doc_encoder = model

    logger.info("Start indexing")

    metrics = src.beir_utils.evaluate_model(
        query_encoder=query_encoder,
        doc_encoder=doc_encoder,
        tokenizer=tokenizer,
        dataset=args.dataset,
        batch_size=args.per_gpu_batch_size,
        norm_query=args.norm_query,
        norm_doc=args.norm_doc,
        is_main=src.dist_utils.is_main(),
        split="dev" if args.dataset == "msmarco" else "test",
        score_function=args.score_function,
        beir_dir=args.beir_dir,
        save_results_path=args.save_results_path,
        lower_case=args.lower_case,
        normalize_text=args.normalize_text,
    )

    if src.dist_utils.is_main():
        for key, value in metrics.items():
            logger.info(f"{args.dataset} : {key}: {value:.1f}")


if __name__ == "__main__":
    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)

    parser.add_argument("--dataset", type=str, help="Evaluation dataset from the BEIR benchmark")
    parser.add_argument("--beir_dir", type=str, default="./", help="Directory to save and load beir datasets")
    parser.add_argument("--text_maxlength", type=int, default=512, help="Maximum text length")

    parser.add_argument("--per_gpu_batch_size", default=128, type=int, help="Batch size per GPU/CPU for indexing.")
    parser.add_argument("--output_dir", type=str, default="./my_experiment", help="Output directory")
    parser.add_argument("--model_name_or_path", type=str, help="Model name or path")
    parser.add_argument(
        "--score_function", type=str, default="dot", help="Metric used to compute similarity between two embeddings"
    )
    parser.add_argument("--norm_query", action="store_true", help="Normalize query representation")
    parser.add_argument("--norm_doc", action="store_true", help="Normalize document representation")
    parser.add_argument("--lower_case", action="store_true", help="lowercase query and document text")
    parser.add_argument(
        "--normalize_text", action="store_true", help="Apply function to normalize some common characters"
    )
    parser.add_argument("--save_results_path", type=str, default=None, help="Path to save result object")

    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
    parser.add_argument("--main_port", type=int, default=-1, help="Main port (for multi-node SLURM jobs)")

    args, _ = parser.parse_known_args()
    main(args)