BEIR

BEIR (Benchmarking-IR) is a heterogeneous evaluation benchmark for information retrieval. It is designed for evaluating the performance of NLP-based retrieval models and widely used by research of modern embedding models.

You can evaluate model’s performance on the BEIR benchmark by running our provided shell script:

chmod +x /examples/evaluation/beir/eval_beir.sh
./examples/evaluation/beir/eval_beir.sh

Or by running:

python -m FlagEmbedding.evaluation.beir \
--eval_name beir \
--dataset_dir ./beir/data \
--dataset_names fiqa arguana cqadupstack \
--splits test dev \
--corpus_embd_save_dir ./beir/corpus_embd \
--output_dir ./beir/search_results \
--search_top_k 1000 \
--rerank_top_k 100 \
--cache_path /root/.cache/huggingface/hub \
--overwrite False \
--k_values 10 100 \
--eval_output_method markdown \
--eval_output_path ./beir/beir_eval_results.md \
--eval_metrics ndcg_at_10 recall_at_100 \
--ignore_identical_ids True \
--embedder_name_or_path BAAI/bge-large-en-v1.5 \
--reranker_name_or_path BAAI/bge-reranker-v2-m3 \
--devices cuda:0 cuda:1 \
--reranker_max_length 1024 \

change the embedder, devices and cache directory to your preference.