chore: import upstream snapshot with attribution

docs/source/API/evaluation/beir.rst

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+BEIR
+====
+
+`BEIR <https://github.com/beir-cellar/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:
+
+.. code:: bash
+
+    chmod +x /examples/evaluation/beir/eval_beir.sh
+    ./examples/evaluation/beir/eval_beir.sh
+
+Or by running:
+
+.. code:: bash
+
+    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.
+
+.. toctree::
+   :hidden:
+
+   beir/arguments
+   beir/data_loader
+   beir/evaluator
+   beir/runner