chore: import upstream snapshot with attribution

docs/source/API/evaluation/mteb.rst

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+MTEB
+====
+
+`MTEB <https://github.com/embeddings-benchmark/mteb>`_ (The Massive Text Embedding Benchmark) is a large-scale evaluation framework designed to assess the performance of text embedding models across a wide variety of NLP tasks. 
+Introduced to standardize and improve the evaluation of text embeddings, MTEB is crucial for assessing how well these models generalize across various real-world applications. 
+It contains a wide range of datasets in eight main NLP tasks and different languages, and provides an easy pipeline for evaluation.
+It also holds the well known MTEB `leaderboard <https://huggingface.co/spaces/mteb/leaderboard>`_, which contains a ranking of the latest first-class embedding models.
+
+You can evaluate model's performance on the whole MTEB benchmark by running our provided shell script:
+
+.. code:: bash
+
+    chmod +x /examples/evaluation/mteb/eval_mteb.sh
+    ./examples/evaluation/mteb/eval_mteb.sh
+
+Or by running:
+
+.. code:: bash
+
+    python -m FlagEmbedding.evaluation.mteb \
+    --eval_name mteb \
+    --output_dir ./mteb/search_results \
+    --languages eng \
+    --tasks NFCorpus BiorxivClusteringS2S SciDocsRR \
+    --eval_output_path ./mteb/mteb_eval_results.json \
+    --embedder_name_or_path BAAI/bge-large-en-v1.5 \
+    --devices cuda:7 \
+    --cache_dir /root/.cache/huggingface/hub
+
+change the embedder, devices and cache directory to your preference.
+
+.. toctree::
+   :hidden:
+
+   mteb/arguments
+   mteb/searcher
+   mteb/runner