MTEB ==== `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 `_, 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