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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 18 new columns ({'public_training_code', 'public_training_data', 'framework', 'loader', 'license', 'open_weights', 'zero_shot_benchmarks', 'name', 'revision', 'n_parameters', 'embed_dim', 'max_tokens', 'release_date', 'reference', 'similarity_fn_name', 'use_instructions', 'memory_usage', 'languages'}) and 5 missing columns ({'evaluation_time', 'mteb_version', 'scores', 'dataset_revision', 'task_name'}).
This happened while the json dataset builder was generating data using
hf://datasets/morteza20/mteb_leaderboard/results/NLPArtisan__qwen-1.8b-retrieval-test/external/model_meta.json (at revision 3a7c664609bebabdbad5017611c533236c0adb2b)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1870, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 622, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2292, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2240, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
name: string
revision: string
release_date: timestamp[s]
languages: list<item: null>
child 0, item: null
loader: null
n_parameters: null
memory_usage: null
max_tokens: null
embed_dim: null
license: null
open_weights: bool
public_training_data: null
public_training_code: null
framework: list<item: null>
child 0, item: null
reference: null
similarity_fn_name: null
use_instructions: null
zero_shot_benchmarks: null
to
{'dataset_revision': Value(dtype='string', id=None), 'task_name': Value(dtype='string', id=None), 'evaluation_time': Value(dtype='null', id=None), 'mteb_version': Value(dtype='null', id=None), 'scores': {'dev': [{'hf_subset': Value(dtype='string', id=None), 'languages': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'map_at_1': Value(dtype='float64', id=None), 'map_at_10': Value(dtype='float64', id=None), 'map_at_100': Value(dtype='float64', id=None), 'map_at_1000': Value(dtype='float64', id=None), 'map_at_3': Value(dtype='float64', id=None), 'map_at_5': Value(dtype='float64', id=None), 'mrr_at_1': Value(dtype='float64', id=None), 'mrr_at_10': Value(dtype='float64', id=None), 'mrr_at_100': Value(dtype='float64', id=None), 'mrr_at_1000': Value(dtype='float64', id=None), 'mrr_at_3': Value(dtype='float64', id=None), 'mrr_at_5': Value(dtype='float64', id=None), 'ndcg_at_1': Value(dtype='float64', id=None), 'ndcg_at_10': Value(dtype='float64', id=None), 'ndcg_at_100': Value(dtype='float64', id=None), 'ndcg_at_1000': Value(dtype='float64', id=None), 'ndcg_at_3': Value(dtype='float64', id=None), 'ndcg_at_5': Value(dtype='float64', id=None), 'precision_at_1': Value(dtype='float64', id=None), 'precision_at_10': Value(dtype='float64', id=None), 'precision_at_100': Value(dtype='float64', id=None), 'precision_at_1000': Value(dtype='float64', id=None), 'precision_at_3': Value(dtype='float64', id=None), 'precision_at_5': Value(dtype='float64', id=None), 'recall_at_1': Value(dtype='float64', id=None), 'recall_at_10': Value(dtype='float64', id=None), 'recall_at_100': Value(dtype='float64', id=None), 'recall_at_1000': Value(dtype='float64', id=None), 'recall_at_3': Value(dtype='float64', id=None), 'recall_at_5': Value(dtype='float64', id=None), 'main_score': Value(dtype='float64', id=None)}]}}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1417, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1049, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 924, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1000, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1741, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1872, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 18 new columns ({'public_training_code', 'public_training_data', 'framework', 'loader', 'license', 'open_weights', 'zero_shot_benchmarks', 'name', 'revision', 'n_parameters', 'embed_dim', 'max_tokens', 'release_date', 'reference', 'similarity_fn_name', 'use_instructions', 'memory_usage', 'languages'}) and 5 missing columns ({'evaluation_time', 'mteb_version', 'scores', 'dataset_revision', 'task_name'}).
This happened while the json dataset builder was generating data using
hf://datasets/morteza20/mteb_leaderboard/results/NLPArtisan__qwen-1.8b-retrieval-test/external/model_meta.json (at revision 3a7c664609bebabdbad5017611c533236c0adb2b)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
dataset_revision
string | task_name
string | evaluation_time
null | mteb_version
null | scores
dict |
|---|---|---|---|---|
None
|
CmedqaRetrieval
| null | null |
{
"dev": [
{
"hf_subset": "default",
"languages": [
"cmn-Hans"
],
"map_at_1": 0.23190999999999998,
"map_at_10": 0.34273,
"map_at_100": 0.36101,
"map_at_1000": 0.36231,
"map_at_3": 0.30495,
"map_at_5": 0.32539999999999997,
"mrr_at_1": 0.35434,
"mrr_at_10": 0.4315,
"mrr_at_100": 0.44155,
"mrr_at_1000": 0.44211,
"mrr_at_3": 0.40735,
"mrr_at_5": 0.42052,
"ndcg_at_1": 0.35434,
"ndcg_at_10": 0.40572,
"ndcg_at_100": 0.47920999999999997,
"ndcg_at_1000": 0.50314,
"ndcg_at_3": 0.35670999999999997,
"ndcg_at_5": 0.3763500000000001,
"precision_at_1": 0.35434,
"precision_at_10": 0.09067,
"precision_at_100": 0.01506,
"precision_at_1000": 0.00181,
"precision_at_3": 0.20163,
"precision_at_5": 0.14624,
"recall_at_1": 0.23190999999999998,
"recall_at_10": 0.50318,
"recall_at_100": 0.80958,
"recall_at_1000": 0.9716799999999999,
"recall_at_3": 0.3557,
"recall_at_5": 0.41776,
"main_score": 0.40572
}
]
}
|
None
|
CovidRetrieval
| null | null |
{
"dev": [
{
"hf_subset": "default",
"languages": [
"cmn-Hans"
],
"map_at_1": 0.64015,
"map_at_10": 0.7198300000000001,
"map_at_100": 0.7243200000000001,
"map_at_1000": 0.72441,
"map_at_3": 0.69924,
"map_at_5": 0.71177,
"mrr_at_1": 0.64173,
"mrr_at_10": 0.71985,
"mrr_at_100": 0.72425,
"mrr_at_1000": 0.72434,
"mrr_at_3": 0.6996800000000001,
"mrr_at_5": 0.71222,
"ndcg_at_1": 0.64173,
"ndcg_at_10": 0.75929,
"ndcg_at_100": 0.77961,
"ndcg_at_1000": 0.78223,
"ndcg_at_3": 0.71828,
"ndcg_at_5": 0.74066,
"precision_at_1": 0.64173,
"precision_at_10": 0.08925,
"precision_at_100": 0.00985,
"precision_at_1000": 0.00101,
"precision_at_3": 0.25887,
"precision_at_5": 0.1667,
"recall_at_1": 0.64015,
"recall_at_10": 0.88251,
"recall_at_100": 0.9747100000000001,
"recall_at_1000": 0.99579,
"recall_at_3": 0.77292,
"recall_at_5": 0.82666,
"main_score": 0.75929
}
]
}
|
None
|
DuRetrieval
| null | null |
{
"dev": [
{
"hf_subset": "default",
"languages": [
"cmn-Hans"
],
"map_at_1": 0.23983999999999997,
"map_at_10": 0.7517499999999999,
"map_at_100": 0.7827300000000001,
"map_at_1000": 0.78322,
"map_at_3": 0.51215,
"map_at_5": 0.64892,
"mrr_at_1": 0.839,
"mrr_at_10": 0.89563,
"mrr_at_100": 0.8965,
"mrr_at_1000": 0.89654,
"mrr_at_3": 0.89167,
"mrr_at_5": 0.89492,
"ndcg_at_1": 0.839,
"ndcg_at_10": 0.8372800000000001,
"ndcg_at_100": 0.87064,
"ndcg_at_1000": 0.87504,
"ndcg_at_3": 0.81318,
"ndcg_at_5": 0.80667,
"precision_at_1": 0.839,
"precision_at_10": 0.407,
"precision_at_100": 0.04778,
"precision_at_1000": 0.00488,
"precision_at_3": 0.7331699999999999,
"precision_at_5": 0.6213,
"recall_at_1": 0.23983999999999997,
"recall_at_10": 0.8641200000000001,
"recall_at_100": 0.96882,
"recall_at_1000": 0.9922,
"recall_at_3": 0.5477,
"recall_at_5": 0.71663,
"main_score": 0.8372800000000001
}
]
}
|
None
|
EcomRetrieval
| null | null |
{
"dev": [
{
"hf_subset": "default",
"languages": [
"cmn-Hans"
],
"map_at_1": 0.516,
"map_at_10": 0.61209,
"map_at_100": 0.61734,
"map_at_1000": 0.6175,
"map_at_3": 0.588,
"map_at_5": 0.60165,
"mrr_at_1": 0.516,
"mrr_at_10": 0.61209,
"mrr_at_100": 0.61734,
"mrr_at_1000": 0.6175,
"mrr_at_3": 0.588,
"mrr_at_5": 0.60165,
"ndcg_at_1": 0.516,
"ndcg_at_10": 0.6613900000000001,
"ndcg_at_100": 0.6865400000000002,
"ndcg_at_1000": 0.69057,
"ndcg_at_3": 0.61185,
"ndcg_at_5": 0.63651,
"precision_at_1": 0.516,
"precision_at_10": 0.0817,
"precision_at_100": 0.00934,
"precision_at_1000": 0.00097,
"precision_at_3": 0.22699999999999998,
"precision_at_5": 0.1482,
"recall_at_1": 0.516,
"recall_at_10": 0.8169999999999998,
"recall_at_100": 0.934,
"recall_at_1000": 0.966,
"recall_at_3": 0.681,
"recall_at_5": 0.741,
"main_score": 0.6613900000000001
}
]
}
|
None
|
MMarcoRetrieval
| null | null |
{
"dev": [
{
"hf_subset": "default",
"languages": [
"cmn-Hans"
],
"map_at_1": 0.69546,
"map_at_10": 0.7847700000000001,
"map_at_100": 0.78743,
"map_at_1000": 0.78751,
"map_at_3": 0.7676900000000001,
"map_at_5": 0.77854,
"mrr_at_1": 0.71819,
"mrr_at_10": 0.79008,
"mrr_at_100": 0.7924,
"mrr_at_1000": 0.79247,
"mrr_at_3": 0.7755300000000002,
"mrr_at_5": 0.7847700000000001,
"ndcg_at_1": 0.71819,
"ndcg_at_10": 0.81947,
"ndcg_at_100": 0.83112,
"ndcg_at_1000": 0.83325,
"ndcg_at_3": 0.78758,
"ndcg_at_5": 0.8056300000000001,
"precision_at_1": 0.71819,
"precision_at_10": 0.09792,
"precision_at_100": 0.01037,
"precision_at_1000": 0.00105,
"precision_at_3": 0.29479,
"precision_at_5": 0.18658999999999998,
"recall_at_1": 0.69546,
"recall_at_10": 0.92053,
"recall_at_100": 0.97254,
"recall_at_1000": 0.98926,
"recall_at_3": 0.83682,
"recall_at_5": 0.87944,
"main_score": 0.81947
}
]
}
|
None
|
MedicalRetrieval
| null | null |
{
"dev": [
{
"hf_subset": "default",
"languages": [
"cmn-Hans"
],
"map_at_1": 0.503,
"map_at_10": 0.55824,
"map_at_100": 0.5638,
"map_at_1000": 0.56441,
"map_at_3": 0.544,
"map_at_5": 0.55235,
"mrr_at_1": 0.504,
"mrr_at_10": 0.5589,
"mrr_at_100": 0.56447,
"mrr_at_1000": 0.56508,
"mrr_at_3": 0.54467,
"mrr_at_5": 0.55302,
"ndcg_at_1": 0.503,
"ndcg_at_10": 0.58578,
"ndcg_at_100": 0.61491,
"ndcg_at_1000": 0.63161,
"ndcg_at_3": 0.5564,
"ndcg_at_5": 0.57134,
"precision_at_1": 0.503,
"precision_at_10": 0.0673,
"precision_at_100": 0.00814,
"precision_at_1000": 0.00095,
"precision_at_3": 0.19733,
"precision_at_5": 0.1256,
"recall_at_1": 0.503,
"recall_at_10": 0.6730000000000002,
"recall_at_100": 0.814,
"recall_at_1000": 0.9469999999999998,
"recall_at_3": 0.592,
"recall_at_5": 0.628,
"main_score": 0.58578
}
]
}
|
None
|
T2Retrieval
| null | null |
{
"dev": [
{
"hf_subset": "default",
"languages": [
"cmn-Hans"
],
"map_at_1": 0.27293,
"map_at_10": 0.76618,
"map_at_100": 0.8022500000000001,
"map_at_1000": 0.80292,
"map_at_3": 0.53856,
"map_at_5": 0.6615800000000001,
"mrr_at_1": 0.8965900000000001,
"mrr_at_10": 0.92121,
"mrr_at_100": 0.92214,
"mrr_at_1000": 0.92218,
"mrr_at_3": 0.9167000000000001,
"mrr_at_5": 0.91955,
"ndcg_at_1": 0.8965900000000001,
"ndcg_at_10": 0.84172,
"ndcg_at_100": 0.87767,
"ndcg_at_1000": 0.8841899999999999,
"ndcg_at_3": 0.85628,
"ndcg_at_5": 0.84155,
"precision_at_1": 0.8965900000000001,
"precision_at_10": 0.41914,
"precision_at_100": 0.04996,
"precision_at_1000": 0.00515,
"precision_at_3": 0.7495499999999999,
"precision_at_5": 0.62771,
"recall_at_1": 0.27293,
"recall_at_10": 0.83004,
"recall_at_100": 0.9482300000000001,
"recall_at_1000": 0.9815,
"recall_at_3": 0.55455,
"recall_at_5": 0.69422,
"main_score": 0.84172
}
]
}
|
None
|
VideoRetrieval
| null | null |
{
"dev": [
{
"hf_subset": "default",
"languages": [
"cmn-Hans"
],
"map_at_1": 0.596,
"map_at_10": 0.6944399999999998,
"map_at_100": 0.69798,
"map_at_1000": 0.6981,
"map_at_3": 0.67467,
"map_at_5": 0.68692,
"mrr_at_1": 0.596,
"mrr_at_10": 0.6944399999999998,
"mrr_at_100": 0.69798,
"mrr_at_1000": 0.6981,
"mrr_at_3": 0.67467,
"mrr_at_5": 0.68692,
"ndcg_at_1": 0.596,
"ndcg_at_10": 0.73936,
"ndcg_at_100": 0.75688,
"ndcg_at_1000": 0.75942,
"ndcg_at_3": 0.69924,
"ndcg_at_5": 0.7214,
"precision_at_1": 0.596,
"precision_at_10": 0.0879,
"precision_at_100": 0.00961,
"precision_at_1000": 0.00098,
"precision_at_3": 0.25667,
"precision_at_5": 0.1648,
"recall_at_1": 0.596,
"recall_at_10": 0.879,
"recall_at_100": 0.961,
"recall_at_1000": 0.98,
"recall_at_3": 0.77,
"recall_at_5": 0.824,
"main_score": 0.73936
}
]
}
|
null | null | null | null | null |
Previously it was possible to submit models results to MTEB by adding the results to the model metadata. This is no longer an option as we want to ensure high quality metadata.
This repository contain the results of the embedding benchmark evaluated using the package mteb.
| Reference | |
|---|---|
| 🦾 Leaderboard | An up to date leaderboard of embedding models |
| 📚 mteb | Guides and instructions on how to use mteb, including running, submitting scores, etc. |
| 🙋 Questions | Questions about the results |
| 🙋 Issues | Issues or bugs you have found |
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