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MTEB_2">关于 MTEB

MTEB : Massive Text Embedding Benchmark

• github : https://github.com/embeddings-benchmark/mteb
• huggingface : https://huggingface.co/spaces/mteb/leaderboard
• paper : https://paperswithcode.com/paper/mteb-massive-text-embedding-benchmark

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中文榜单(2024-05-03)

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英文

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MTEB__23">MTEB 任务和数据集概览

多模态标记为紫色。

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MTEB_Pythont__28">使用 MTEB Pythont 库

Installation

pip install mteb

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使用

• Using a python script (see scripts/run_mteb_english.py and mteb/mtebscripts for more):

from mteb import MTEB
from sentence_transformers import SentenceTransformer# Define the sentence-transformers model name
model_name = "average_word_embeddings_komninos"
# or directly from huggingface:
# model_name = "sentence-transformers/all-MiniLM-L6-v2"model = SentenceTransformer(model_name)
evaluation = MTEB(tasks=["Banking77Classification"])
results = evaluation.run(model, output_folder=f"results/{model_name}")

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• 使用命令行

mteb --available_tasksmteb -m sentence-transformers/all-MiniLM-L6-v2 \-t Banking77Classification  \--verbosity 3# if nothing is specified default to saving the results in the results/{model_name} folder

• Using multiple GPUs in parallel can be done by just having a custom encode function that distributes the inputs to multiple GPUs like e.g. here or here.

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伊织 2024-05-03（五）