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fix: Add training data annotations to uderver-bloom models #2210

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Merged
isaac-chung merged 3 commits into from
Mar 3, 2025
Merged

fix: Add training data annotations to uderver-bloom models #2210

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f8c30d8
fix: Add training data annotations to uderver-bloom models
KennethEnevoldsen Mar 2, 2025
f7c93c7
fix: add mixedbread
KennethEnevoldsen Mar 2, 2025
d049336
Merge branch 'main' into fix-annot
x-tabdeveloping Mar 3, 2025
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255 changes: 63 additions & 192 deletions mteb/models/misc_models.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1087,57 +1087,66 @@
adapted_from="sdadas/polish-roberta-large-v2",
superseded_by=None,
)

udever_dataset = { # discussed here: https://github.com/embeddings-benchmark/mteb/issues/2193
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@x-tabdeveloping x-tabdeveloping Mar 3, 2025

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datasets perchance?

"MSMARCO": [],
# SNLI
# MultiNLI
}

udever_langauges = [
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@x-tabdeveloping x-tabdeveloping Mar 3, 2025

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You have misspelled language :D

"aka_Latn",
"ara_Arab",
"asm_Beng",
"bam_Latn",
"ben_Beng",
"cat_Latn",
"eng_Latn",
"spa_Latn",
"eus_Latn",
"fon_Latn",
"fra_Latn",
"guj_Gujr",
"hin_Deva",
"ind_Latn",
"ibo_Latn",
"kik_Latn",
"kan_Knda",
"lug_Latn",
"lin_Latn",
"mal_Mlym",
"mar_Deva",
"nep_Deva",
"nso_Latn",
"nya_Latn",
"ori_Orya",
"pan_Guru",
"por_Latn",
"run_Latn",
"kin_Latn",
"sna_Latn",
"sot_Latn",
"swa_Latn",
"tam_Taml",
"tel_Telu",
"tsn_Latn",
"tso_Latn",
"tum_Latn",
"twi_Latn",
"urd_Arab",
"vie_Latn",
"wol_Latn",
"xho_Latn",
"yor_Latn",
"zho_Hans",
"zul_Latn",
]

izhx__udever_bloom_1b1 = ModelMeta(
name="izhx/udever-bloom-1b1",
revision="7bf1ee29878cb040b2708a691aa4b61f27eaa252",
release_date="2023-10-24",
languages=[
"aka_Latn",
"ara_Arab",
"asm_Beng",
"bam_Latn",
"ben_Beng",
"cat_Latn",
"eng_Latn",
"spa_Latn",
"eus_Latn",
"fon_Latn",
"fra_Latn",
"guj_Gujr",
"hin_Deva",
"ind_Latn",
"ibo_Latn",
"kik_Latn",
"kan_Knda",
"lug_Latn",
"lin_Latn",
"mal_Mlym",
"mar_Deva",
"nep_Deva",
"nso_Latn",
"nya_Latn",
"ori_Orya",
"pan_Guru",
"por_Latn",
"run_Latn",
"kin_Latn",
"sna_Latn",
"sot_Latn",
"swa_Latn",
"tam_Taml",
"tel_Telu",
"tsn_Latn",
"tso_Latn",
"tum_Latn",
"twi_Latn",
"urd_Arab",
"vie_Latn",
"wol_Latn",
"xho_Latn",
"yor_Latn",
"zho_Hans",
"zul_Latn",
],
languages=udever_langauges,
loader=None,
n_parameters=None,
memory_usage_mb=None,
Expand All @@ -1151,61 +1160,15 @@
reference="https://huggingface.co/izhx/udever-bloom-1b1",
similarity_fn_name="cosine",
use_instructions=None,
training_datasets=None,
training_datasets=udever_dataset,
adapted_from="bigscience/bloom-1b1",
superseded_by=None,
)
izhx__udever_bloom_3b = ModelMeta(
name="izhx/udever-bloom-3b",
revision="4edd8affe80ca89ba0f6b6ba4103fc7f25fc57b2",
release_date="2023-10-24",
languages=[
"aka_Latn",
"ara_Arab",
"asm_Beng",
"bam_Latn",
"ben_Beng",
"cat_Latn",
"eng_Latn",
"spa_Latn",
"eus_Latn",
"fon_Latn",
"fra_Latn",
"guj_Gujr",
"hin_Deva",
"ind_Latn",
"ibo_Latn",
"kik_Latn",
"kan_Knda",
"lug_Latn",
"lin_Latn",
"mal_Mlym",
"mar_Deva",
"nep_Deva",
"nso_Latn",
"nya_Latn",
"ori_Orya",
"pan_Guru",
"por_Latn",
"run_Latn",
"kin_Latn",
"sna_Latn",
"sot_Latn",
"swa_Latn",
"tam_Taml",
"tel_Telu",
"tsn_Latn",
"tso_Latn",
"tum_Latn",
"twi_Latn",
"urd_Arab",
"vie_Latn",
"wol_Latn",
"xho_Latn",
"yor_Latn",
"zho_Hans",
"zul_Latn",
],
languages=udever_langauges,
loader=None,
n_parameters=None,
memory_usage_mb=None,
Expand All @@ -1219,61 +1182,15 @@
reference="https://huggingface.co/izhx/udever-bloom-3b",
similarity_fn_name="cosine",
use_instructions=None,
training_datasets=None,
training_datasets=udever_dataset,
adapted_from="bigscience/bloom-3b",
superseded_by=None,
)
izhx__udever_bloom_560m = ModelMeta(
name="izhx/udever-bloom-560m",
revision="b2a723e355946ec5a5c5fbed3459766627ded2bb",
release_date="2023-10-24",
languages=[
"aka_Latn",
"ara_Arab",
"asm_Beng",
"bam_Latn",
"ben_Beng",
"cat_Latn",
"eng_Latn",
"spa_Latn",
"eus_Latn",
"fon_Latn",
"fra_Latn",
"guj_Gujr",
"hin_Deva",
"ind_Latn",
"ibo_Latn",
"kik_Latn",
"kan_Knda",
"lug_Latn",
"lin_Latn",
"mal_Mlym",
"mar_Deva",
"nep_Deva",
"nso_Latn",
"nya_Latn",
"ori_Orya",
"pan_Guru",
"por_Latn",
"run_Latn",
"kin_Latn",
"sna_Latn",
"sot_Latn",
"swa_Latn",
"tam_Taml",
"tel_Telu",
"tsn_Latn",
"tso_Latn",
"tum_Latn",
"twi_Latn",
"urd_Arab",
"vie_Latn",
"wol_Latn",
"xho_Latn",
"yor_Latn",
"zho_Hans",
"zul_Latn",
],
languages=udever_langauges,
loader=None,
n_parameters=None,
memory_usage_mb=None,
Expand All @@ -1287,61 +1204,15 @@
reference="https://huggingface.co/izhx/udever-bloom-560m",
similarity_fn_name="cosine",
use_instructions=None,
training_datasets=None,
training_datasets=udever_dataset,
adapted_from="bigscience/bloom-560m",
superseded_by=None,
)
izhx__udever_bloom_7b1 = ModelMeta(
name="izhx/udever-bloom-7b1",
revision="18e8d3e6dbd94868584877f2e72a105a17df22ef",
release_date="2023-10-24",
languages=[
"aka_Latn",
"ara_Arab",
"asm_Beng",
"bam_Latn",
"ben_Beng",
"cat_Latn",
"eng_Latn",
"spa_Latn",
"eus_Latn",
"fon_Latn",
"fra_Latn",
"guj_Gujr",
"hin_Deva",
"ind_Latn",
"ibo_Latn",
"kik_Latn",
"kan_Knda",
"lug_Latn",
"lin_Latn",
"mal_Mlym",
"mar_Deva",
"nep_Deva",
"nso_Latn",
"nya_Latn",
"ori_Orya",
"pan_Guru",
"por_Latn",
"run_Latn",
"kin_Latn",
"sna_Latn",
"sot_Latn",
"swa_Latn",
"tam_Taml",
"tel_Telu",
"tsn_Latn",
"tso_Latn",
"tum_Latn",
"twi_Latn",
"urd_Arab",
"vie_Latn",
"wol_Latn",
"xho_Latn",
"yor_Latn",
"zho_Hans",
"zul_Latn",
],
languages=udever_langauges,
loader=None,
n_parameters=None,
memory_usage_mb=None,
Expand All @@ -1355,7 +1226,7 @@
reference="https://huggingface.co/izhx/udever-bloom-7b1",
similarity_fn_name="cosine",
use_instructions=None,
training_datasets=None,
training_datasets=udever_dataset,
adapted_from="bigscience/bloom-7b1",
superseded_by=None,
)
Expand Down
21 changes: 12 additions & 9 deletions mteb/models/mxbai_models.py
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -4,6 +4,15 @@

from mteb.model_meta import ModelMeta, sentence_transformers_loader

mixedbread_training_data = {
# from correspondance:
# as mentioned in our blog post
# (https://www.mixedbread.com/blog/mxbai-embed-large-v1#built-for-rag-and-real-world-use-cases:~:text=During%20the%20whole,related%20use%20cases.)
# We do not train on any data (except the MSMarco training split) of MTEB. We have a strong filtering process to ensure the OOD setting. That's true
# for all of our models. Keep up the good work and let me know if you have any questions.
"MSMARCO": [],
}

mxbai_embed_large_v1 = ModelMeta(
loader=partial( # type: ignore
sentence_transformers_loader,
Expand All @@ -29,9 +38,7 @@
use_instructions=True,
public_training_code=None,
public_training_data=None,
training_datasets={
"MSMARCO": ["train"],
},
training_datasets=mixedbread_training_data,
)

mxbai_embed_2d_large_v1 = ModelMeta(
Expand All @@ -54,9 +61,7 @@
superseded_by=None,
public_training_code=None,
public_training_data=None,
training_datasets={
"MSMARCO": ["train"],
},
training_datasets=mixedbread_training_data,
)


Expand All @@ -80,7 +85,5 @@
superseded_by=None,
public_training_code=None,
public_training_data=None,
training_datasets={
"MSMARCO": ["train"],
},
training_datasets=mixedbread_training_data,
)