MTEB#
This framework supports MTEB and CMTEB, with the following details:
MTEB (Massive Text Embedding Benchmark) is a large-scale benchmark designed to measure the performance of text embedding models across diverse embedding tasks. MTEB includes 56 datasets covering 8 tasks and supports over 112 different languages. The goal of this benchmark is to assist developers in finding the best text embedding models suitable for various tasks.
C-MTEB (Chinese Massive Text Embedding Benchmark) is a dedicated evaluation benchmark for Chinese text vectors, built on MTEB, aimed at assessing the performance of Chinese text vector models. C-MTEB collects 35 public datasets and is divided into 6 categories of evaluation tasks, including retrieval, re-ranking, semantic text similarity (STS), classification, pair classification, and clustering.
Supported Datasets#
Here is an overview of the available tasks and datasets in C-MTEB:
Name |
Hub Link |
Description |
Type |
Category |
Number of Test Samples |
|---|---|---|---|---|---|
T2Ranking: A large-scale Chinese paragraph ranking benchmark |
Retrieval |
s2p |
24,832 |
||
mMARCO is the multilingual version of the MS MARCO paragraph ranking dataset |
Retrieval |
s2p |
7,437 |
||
A large-scale Chinese web search engine paragraph retrieval benchmark |
Retrieval |
s2p |
4,000 |
||
COVID-19 news articles |
Retrieval |
s2p |
949 |
||
Online medical consultation texts |
Retrieval |
s2p |
3,999 |
||
Paragraph retrieval dataset collected from Alibaba e-commerce search engine systems |
Retrieval |
s2p |
1,000 |
||
Paragraph retrieval dataset collected from Alibaba medical search engine systems |
Retrieval |
s2p |
1,000 |
||
Paragraph retrieval dataset collected from Alibaba video search engine systems |
Retrieval |
s2p |
1,000 |
||
T2Ranking: A large-scale Chinese paragraph ranking benchmark |
Re-ranking |
s2p |
24,382 |
||
mMARCO is the multilingual version of the MS MARCO paragraph ranking dataset |
Re-ranking |
s2p |
7,437 |
||
Chinese community medical Q&A |
Re-ranking |
s2p |
2,000 |
||
Chinese community medical Q&A |
Re-ranking |
s2p |
4,000 |
||
Original Chinese natural language inference dataset |
Pair Classification |
s2s |
3,000 |
||
Chinese multi-class natural language inference |
Pair Classification |
s2s |
139,000 |
||
Clustering titles from the CLS dataset. Clustering based on 13 sets of main categories. |
Clustering |
s2s |
10,000 |
||
Clustering titles + abstracts from the CLS dataset. Clustering based on 13 sets of main categories. |
Clustering |
p2p |
10,000 |
||
Clustering titles from the THUCNews dataset |
Clustering |
s2s |
10,000 |
||
Clustering titles + abstracts from the THUCNews dataset |
Clustering |
p2p |
10,000 |
||
ATEC NLP Sentence Pair Similarity Competition |
STS |
s2s |
20,000 |
||
Banking Question Semantic Similarity |
STS |
s2s |
10,000 |
||
Large-scale Chinese Question Matching Corpus |
STS |
s2s |
12,500 |
||
Translated PAWS evaluation pairs |
STS |
s2s |
2,000 |
||
Translated STS-B into Chinese |
STS |
s2s |
1,360 |
||
Ant Financial Question Matching Corpus |
STS |
s2s |
3,861 |
||
QQ Browser Query Title Corpus |
STS |
s2s |
5,000 |
||
News Short Text Classification |
Classification |
s2s |
10,000 |
||
Long Text Classification of Application Descriptions |
Classification |
s2s |
2,600 |
||
Sentiment Analysis of User Reviews on Food Delivery Platforms |
Classification |
s2s |
1,000 |
||
Sentiment Analysis of User Reviews on Online Shopping Websites |
Classification |
s2s |
1,000 |
||
A set of multilingual sentiment datasets grouped into three categories: positive, neutral, negative |
Classification |
s2s |
3,000 |
||
Reviews of iPhone |
Classification |
s2s |
533 |
For retrieval tasks, a sample of 100,000 candidates (including the ground truth) is drawn from the entire corpus to reduce inference costs.
Environment Setup#
Install dependencies
pip install mteb
Configure Evaluation Parameters#
The framework supports two evaluation modes: single-stage evaluation and two-stage evaluation:
Single-Stage Evaluation: Directly use the model for prediction and compute metrics. Supports tasks such as retrieval, re-ranking, and classification for embedding models.
Two-Stage Evaluation: Use the model for retrieval first, then use the model for re-ranking, and compute metrics. Supports re-ranking models.
Single-stage Evaluation#
Example configuration file:
one_stage_task_cfg = {
"work_dir": "outputs",
"eval_backend": "RAGEval",
"eval_config": {
"tool": "MTEB",
"model": [
{
"model_name_or_path": "AI-ModelScope/m3e-base",
"pooling_mode": None,
"max_seq_length": 512,
"prompt": "",
"model_kwargs": {"torch_dtype": "auto"},
"encode_kwargs": {
"batch_size": 128,
},
}
],
"eval": {
"tasks": [
"TNews",
"CLSClusteringS2S",
"T2Reranking",
"T2Retrieval",
"ATEC",
],
"verbosity": 2,
"overwrite_results": True,
"topk": 10,
"limits": 500,
},
},
}
Evaluation of API Model Services#
When using a remote API model service, the configuration file example is as follows:
from evalscope import TaskConfig
task_cfg = TaskConfig(
eval_backend='RAGEval', # Specifies the evaluation backend to use
eval_config={
'tool': 'MTEB', # The evaluation tool to be used
'model': [
{
'model_name': 'text-embedding-v3', # Name of the model
'api_base': 'https://dashscope.aliyuncs.com/compatible-mode/v1', # Base URL for the API service
'api_key': env.get('DASHSCOPE_API_KEY', 'EMPTY'), # API key for authentication
'dimensions': 1024, # Dimensionality of the model's output
'encode_kwargs': { # Encoding arguments
'batch_size': 10, # Size of the batch to process at once
},
}
],
'eval': {
'tasks': [
'T2Retrieval', # Task or tasks to evaluate
],
'verbosity': 2, # Level of detail in evaluation output
'overwrite_results': True, # Whether to overwrite existing results
'limits': 30, # Limit on the number of items to evaluate
},
},
)
Two-stage Evaluation#
Example configuration file: first perform retrieval, then reranking:
two_stage_task_cfg = {
"work_dir": "outputs",
"eval_backend": "RAGEval",
"eval_config": {
"tool": "MTEB",
"model": [
{
"model_name_or_path": "AI-ModelScope/m3e-base",
"is_cross_encoder": False,
"max_seq_length": 512,
"model_kwargs": {"torch_dtype": "auto"},
"encode_kwargs": {
"batch_size": 64,
},
},
{
"model_name_or_path": "OpenBMB/MiniCPM-Reranker",
"is_cross_encoder": True,
"max_seq_length": 512,
"prompt": "Generate a retrieval representation for this question",
"model_kwargs": {"torch_dtype": "auto"},
"encode_kwargs": {
"batch_size": 32,
},
},
],
"eval": {
"tasks": ["T2Retrieval"],
"verbosity": 2,
"overwrite_results": True,
"topk": 5,
"limits": 100,
},
},
}
Parameter Explanation#
eval_backend: Default value isRAGEval, indicating the use of the RAGEval evaluation backend.eval_config: A dictionary containing the following fields:tool: Evaluation tool, usingMTEB.model: List of model configurations. For single-stage evaluation, only one model can be placed; for two-stage evaluation, two models are passed in, with the first model used for retrieval and the second model used for reranking, including the following fields:For locally loaded model support:
model_name_or_path:str: Model name or path, supports automatic model download from the ModelScope repository.is_cross_encoder:bool: Whether the model is a cross encoder, default is False; for reranking models, set toTrue.pooling_mode:Optional[str]: Pooling mode, default ismean. Options are: “cls”, “lasttoken”, “max”, “mean”, “mean_sqrt_len_tokens”, or “weightedmean”. Forbgeseries models, set to “cls”.max_seq_length:int: Maximum sequence length, default is 512.prompt:str: Prompt used in front of the model for retrieval tasks, default is an empty string.model_kwargs:dict: Model keyword arguments, default value is{"torch_dtype": "auto"}.config_kwargs:Dict[str, Any]: Configuration keyword arguments, default is an empty dictionary.encode_kwargs:dict: Encoding keyword arguments, default is:{ "show_progress_bar": True, "batch_size": 32 }hub:str: Model source, can be “modelscope” or “huggingface”.
For remote API model service support:
model_name:str: Model name.api_base:str: Model API service address.api_key:str: Model API key.dimension:int: Model output dimension.encode_kwargs:dict: Encoding keyword arguments, default is:{ "batch_size": 10 }hub:strSource of the model, can be “modelscope” or “huggingface”.
eval: A dictionary containing the following fields:tasks:List[str]Task names, refer to the task listtop_k:intSelect the top K results, for retrieval tasksverbosity:intLevel of detail, ranging from 0-3overwrite_results:boolWhether to overwrite results, default is Truelimits:Optional[int]Limit on the number of samples, default is None; not recommended to set for retrieval taskshub:strSource of the dataset, can be “modelscope” or “huggingface”
Model Evaluation#
from evalscope.run import run_task
from evalscope.utils.logger import get_logger
logger = get_logger()
one_stage_task_cfg = one_stage_task_cfg
# or
# two_stage_task_cfg = two_stage_task_cfg
# Run task
run_task(task_cfg=one_stage_task_cfg)
# or
# run_task(task_cfg=two_stage_task_cfg)
The following is an example of the output:
One-Stage Evaluation
Outputs
{
"dataset_revision": "317f262bf1e6126357bbe89e875451e4b0938fe4",
"evaluation_time": 16.50650382041931,
"kg_co2_emissions": null,
"mteb_version": "1.14.15",
"scores": {
"validation": [
{
"accuracy": 0.4744,
"f1": 0.44562489526640825,
"f1_weighted": 0.47540307398330806,
"hf_subset": "default",
"languages": [
"cmn-Hans"
],
"main_score": 0.4744,
"scores_per_experiment": [
{
"accuracy": 0.48,
"f1": 0.4536376605217497,
"f1_weighted": 0.47800277926811163
},
{
"accuracy": 0.48,
"f1": 0.44713633954639176,
"f1_weighted": 0.4826984434763292
},
{
"accuracy": 0.462,
"f1": 0.433365706955334,
"f1_weighted": 0.4640970055245127
},
{
"accuracy": 0.484,
"f1": 0.4586732839614161,
"f1_weighted": 0.4857359110392786
},
{
"accuracy": 0.462,
"f1": 0.4293797541165097,
"f1_weighted": 0.4632657330831137
},
{
"accuracy": 0.474,
"f1": 0.44775120246296396,
"f1_weighted": 0.4737182842092953
},
{
"accuracy": 0.47,
"f1": 0.4431197566080463,
"f1_weighted": 0.4714830140231783
},
{
"accuracy": 0.472,
"f1": 0.44322381694059326,
"f1_weighted": 0.47100005556357255
},
{
"accuracy": 0.484,
"f1": 0.45454749692062835,
"f1_weighted": 0.4856239367465818
},
{
"accuracy": 0.476,
"f1": 0.44541393463044954,
"f1_weighted": 0.47840557689910646
}
]
}
]
},
"task_name": "TNews"
}
Two-stage Evaluation
first stage
{
"dataset_revision": "8731a845f1bf500a4f111cf1070785c793d10e64",
"evaluation_time": 599.5170171260834,
"kg_co2_emissions": null,
"mteb_version": "1.14.15",
"scores": {
"dev": [
{
"hf_subset": "default",
"languages": [
"cmn-Hans"
],
"main_score": 0.73143,
"map_at_1": 0.22347,
"map_at_10": 0.63237,
"map_at_100": 0.67533,
"map_at_1000": 0.67651,
"map_at_20": 0.66282,
"map_at_3": 0.43874,
"map_at_5": 0.54049,
"mrr_at_1": 0.7898912852884447,
"mrr_at_10": 0.8402654617870331,
"mrr_at_100": 0.8421827758769684,
"mrr_at_1000": 0.8422583001072272,
"mrr_at_20": 0.8415411456315557,
"mrr_at_3": 0.8307469752761716,
"mrr_at_5": 0.8368029984218875,
"nauc_map_at_1000_diff1": 0.17749400860890877,
"nauc_map_at_1000_max": 0.42844516520725967,
"nauc_map_at_1000_std": 0.18789871694419072,
"nauc_map_at_100_diff1": 0.17747467084779375,
"nauc_map_at_100_max": 0.42732291785494575,
"nauc_map_at_100_std": 0.18694287087286737,
"nauc_map_at_10_diff1": 0.19976199493034202,
"nauc_map_at_10_max": 0.3374436217668296,
"nauc_map_at_10_std": 0.07951451707732717,
"nauc_map_at_1_diff1": 0.41727578149080663,
"nauc_map_at_1_max": -0.1402656422184478,
"nauc_map_at_1_std": -0.26168722519030313,
"nauc_map_at_20_diff1": 0.1811898211371171,
"nauc_map_at_20_max": 0.40563441466210043,
"nauc_map_at_20_std": 0.15927727170010608,
"nauc_map_at_3_diff1": 0.31255422845809033,
"nauc_map_at_3_max": 0.007523677231905161,
"nauc_map_at_3_std": -0.19578481884353466,
"nauc_map_at_5_diff1": 0.26073699217160473,
"nauc_map_at_5_max": 0.14665611579604088,
"nauc_map_at_5_std": -0.09600383298672226,
"nauc_mrr_at_1000_diff1": 0.3819666309367981,
"nauc_mrr_at_1000_max": 0.6285393024619401,
"nauc_mrr_at_1000_std": 0.3294970299417527,
"nauc_mrr_at_100_diff1": 0.3819436006743644,
"nauc_mrr_at_100_max": 0.6286346262471935,
"nauc_mrr_at_100_std": 0.32963045935037844,
"nauc_mrr_at_10_diff1": 0.3819124721154632,
"nauc_mrr_at_10_max": 0.6292778905762176,
"nauc_mrr_at_10_std": 0.3298187966196067,
"nauc_mrr_at_1_diff1": 0.3862589251033909,
"nauc_mrr_at_1_max": 0.589976680174432,
"nauc_mrr_at_1_std": 0.2780515387897469,
"nauc_mrr_at_20_diff1": 0.38198959771391816,
"nauc_mrr_at_20_max": 0.6290569436652999,
"nauc_mrr_at_20_std": 0.3301570340189363,
"nauc_mrr_at_3_diff1": 0.3825046940733129,
"nauc_mrr_at_3_max": 0.6282507269128365,
"nauc_mrr_at_3_std": 0.3260807934869131,
"nauc_mrr_at_5_diff1": 0.3816317396711923,
"nauc_mrr_at_5_max": 0.6288655177904692,
"nauc_mrr_at_5_std": 0.3298854062538469,
"nauc_ndcg_at_1000_diff1": 0.21319598381916555,
"nauc_ndcg_at_1000_max": 0.5328295949130256,
"nauc_ndcg_at_1000_std": 0.2946773445135694,
"nauc_ndcg_at_100_diff1": 0.2089807772703975,
"nauc_ndcg_at_100_max": 0.5239397690321543,
"nauc_ndcg_at_100_std": 0.29123456982125717,
"nauc_ndcg_at_10_diff1": 0.20555333230027603,
"nauc_ndcg_at_10_max": 0.44316027023003046,
"nauc_ndcg_at_10_std": 0.1921835220940756,
"nauc_ndcg_at_1_diff1": 0.3862589251033909,
"nauc_ndcg_at_1_max": 0.589976680174432,
"nauc_ndcg_at_1_std": 0.2780515387897469,
"nauc_ndcg_at_20_diff1": 0.20754208582741446,
"nauc_ndcg_at_20_max": 0.4786092392092643,
"nauc_ndcg_at_20_std": 0.23536973680564616,
"nauc_ndcg_at_3_diff1": 0.1902823773882388,
"nauc_ndcg_at_3_max": 0.5400466380622567,
"nauc_ndcg_at_3_std": 0.2713874990424778,
"nauc_ndcg_at_5_diff1": 0.18279298790691637,
"nauc_ndcg_at_5_max": 0.4916119327522918,
"nauc_ndcg_at_5_std": 0.2375397192963552,
"nauc_precision_at_1000_diff1": -0.20510380600112582,
"nauc_precision_at_1000_max": 0.4958820760698651,
"nauc_precision_at_1000_std": 0.5402465580496146,
"nauc_precision_at_100_diff1": -0.1994322347949809,
"nauc_precision_at_100_max": 0.5206762748551254,
"nauc_precision_at_100_std": 0.5568154081333078,
"nauc_precision_at_10_diff1": -0.16707155441197413,
"nauc_precision_at_10_max": 0.5600612846655972,
"nauc_precision_at_10_std": 0.49419688804691536,
"nauc_precision_at_1_diff1": 0.3862589251033909,
"nauc_precision_at_1_max": 0.589976680174432,
"nauc_precision_at_1_std": 0.2780515387897469,
"nauc_precision_at_20_diff1": -0.18471041949530417,
"nauc_precision_at_20_max": 0.5458950955439645,
"nauc_precision_at_20_std": 0.5355982267058214,
"nauc_precision_at_3_diff1": -0.03826790088047189,
"nauc_precision_at_3_max": 0.5833083970750171,
"nauc_precision_at_3_std": 0.380196662597275,
"nauc_precision_at_5_diff1": -0.11789367842600275,
"nauc_precision_at_5_max": 0.5708494593335263,
"nauc_precision_at_5_std": 0.42860609671688105,
"nauc_recall_at_1000_diff1": 0.1341309660059583,
"nauc_recall_at_1000_max": 0.5923755841077135,
"nauc_recall_at_1000_std": 0.5980459502693942,
"nauc_recall_at_100_diff1": 0.12181394285840096,
"nauc_recall_at_100_max": 0.47090136790318127,
"nauc_recall_at_100_std": 0.3959369184297595,
"nauc_recall_at_10_diff1": 0.17356300971546512,
"nauc_recall_at_10_max": 0.25475707245853674,
"nauc_recall_at_10_std": 0.041819982320384745,
"nauc_recall_at_1_diff1": 0.41727578149080663,
"nauc_recall_at_1_max": -0.1402656422184478,
"nauc_recall_at_1_std": -0.26168722519030313,
"nauc_recall_at_20_diff1": 0.14273713155999543,
"nauc_recall_at_20_max": 0.36251116771924663,
"nauc_recall_at_20_std": 0.1912123941692314,
"nauc_recall_at_3_diff1": 0.2873719855400218,
"nauc_recall_at_3_max": -0.041198403561830285,
"nauc_recall_at_3_std": -0.21921947922872737,
"nauc_recall_at_5_diff1": 0.23680082643694844,
"nauc_recall_at_5_max": 0.06580524171324151,
"nauc_recall_at_5_std": -0.14104561361502632,
"ndcg_at_1": 0.78989,
"ndcg_at_10": 0.73143,
"ndcg_at_100": 0.78829,
"ndcg_at_1000": 0.80026,
"ndcg_at_20": 0.75787,
"ndcg_at_3": 0.7417,
"ndcg_at_5": 0.72641,
"precision_at_1": 0.78989,
"precision_at_10": 0.37304,
"precision_at_100": 0.04828,
"precision_at_1000": 0.00511,
"precision_at_20": 0.21403,
"precision_at_3": 0.65461,
"precision_at_5": 0.54942,
"recall_at_1": 0.22347,
"recall_at_10": 0.73318,
"recall_at_100": 0.91093,
"recall_at_1000": 0.97197,
"recall_at_20": 0.81286,
"recall_at_3": 0.46573,
"recall_at_5": 0.59383
}
]
},
"task_name": "T2Retrieval"
}
second stage
{
"dataset_revision": "8731a845f1bf500a4f111cf1070785c793d10e64",
"evaluation_time": 332.15709686279297,
"kg_co2_emissions": null,
"mteb_version": "1.14.15",
"scores": {
"dev": [
{
"hf_subset": "default",
"languages": [
"cmn-Hans"
],
"main_score": 0.661,
"map_at_1": 0.24264,
"map_at_10": 0.56291,
"map_at_100": 0.56291,
"map_at_1000": 0.56291,
"map_at_20": 0.56291,
"map_at_3": 0.4714,
"map_at_5": 0.56291,
"mrr_at_1": 0.841969139049623,
"mrr_at_10": 0.8689147524694633,
"mrr_at_100": 0.8689147524694633,
"mrr_at_1000": 0.8689147524694633,
"mrr_at_20": 0.8689147524694633,
"mrr_at_3": 0.8664883979192248,
"mrr_at_5": 0.8689147524694633,
"nauc_map_at_1000_diff1": 0.12071580301051653,
"nauc_map_at_1000_max": 0.2536691069727338,
"nauc_map_at_1000_std": 0.343624832364704,
"nauc_map_at_100_diff1": 0.12071580301051653,
"nauc_map_at_100_max": 0.2536691069727338,
"nauc_map_at_100_std": 0.343624832364704,
"nauc_map_at_10_diff1": 0.12071580301051653,
"nauc_map_at_10_max": 0.2536691069727338,
"nauc_map_at_10_std": 0.343624832364704,
"nauc_map_at_1_diff1": 0.47964980727810325,
"nauc_map_at_1_max": -0.08015044571696166,
"nauc_map_at_1_std": 0.3507257834956417,
"nauc_map_at_20_diff1": 0.12071580301051653,
"nauc_map_at_20_max": 0.2536691069727338,
"nauc_map_at_20_std": 0.343624832364704,
"nauc_map_at_3_diff1": 0.23481937699306626,
"nauc_map_at_3_max": 0.10372745264123306,
"nauc_map_at_3_std": 0.45345158923063256,
"nauc_map_at_5_diff1": 0.12071580301051653,
"nauc_map_at_5_max": 0.2536691069727338,
"nauc_map_at_5_std": 0.343624832364704,
"nauc_mrr_at_1000_diff1": 0.23393918304502795,
"nauc_mrr_at_1000_max": 0.8703379129725659,
"nauc_mrr_at_1000_std": 0.5785333616122065,
"nauc_mrr_at_100_diff1": 0.23393918304502795,
"nauc_mrr_at_100_max": 0.8703379129725659,
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}
]
},
"task_name": "T2Retrieval"
}
Custom Dataset Evaluation#
See also