metadata
license: apache-2.0
language:
- en
- pt
library_name: model2vec
base_model:
- nomic-ai/nomic-embed-text-v2-moe
pipeline_tag: feature-extraction
tags:
- mteb
model-index:
- name: cnmoro/static-nomic-large
results:
- dataset:
config: default
name: MTEB Assin2STS (default)
revision: 0ff9c86779e06855536d8775ce5550550e1e5a2d
split: test
type: nilc-nlp/assin2
metrics:
- type: pearson
value: 64.5329
- type: spearman
value: 58.7463
- type: cosine_pearson
value: 64.5329
- type: cosine_spearman
value: 58.7462
- type: manhattan_pearson
value: 62.2038
- type: manhattan_spearman
value: 58.8366
- type: euclidean_pearson
value: 62.0719
- type: euclidean_spearman
value: 58.7463
- type: main_score
value: 58.7462
task:
type: STS
- dataset:
config: default
name: MTEB BIOSSES (default)
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
split: test
type: mteb/biosses-sts
metrics:
- type: pearson
value: 69.0557
- type: spearman
value: 68.7811
- type: cosine_pearson
value: 69.0557
- type: cosine_spearman
value: 68.7811
- type: manhattan_pearson
value: 68.0266
- type: manhattan_spearman
value: 68.4931
- type: euclidean_pearson
value: 67.8127
- type: euclidean_spearman
value: 68.7811
- type: main_score
value: 68.7811
task:
type: STS
- dataset:
config: default
name: MTEB SICK-BR-STS (default)
revision: 0cdfb1d51ef339011c067688a3b75b82f927c097
split: test
type: eduagarcia/sick-br
metrics:
- type: pearson
value: 69.4947
- type: spearman
value: 62.73950000000001
- type: cosine_pearson
value: 69.4947
- type: cosine_spearman
value: 62.739599999999996
- type: manhattan_pearson
value: 66.1733
- type: manhattan_spearman
value: 62.8382
- type: euclidean_pearson
value: 66.0829
- type: euclidean_spearman
value: 62.739599999999996
- type: main_score
value: 62.739599999999996
task:
type: STS
- dataset:
config: default
name: MTEB SICK-R (default)
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
split: test
type: mteb/sickr-sts
metrics:
- type: pearson
value: 64.34349999999999
- type: spearman
value: 58.133
- type: cosine_pearson
value: 64.34349999999999
- type: cosine_spearman
value: 58.1329
- type: manhattan_pearson
value: 58.9803
- type: manhattan_spearman
value: 57.7487
- type: euclidean_pearson
value: 59.47280000000001
- type: euclidean_spearman
value: 58.133
- type: main_score
value: 58.1329
task:
type: STS
- dataset:
config: default
name: MTEB STS12 (default)
revision: a0d554a64d88156834ff5ae9920b964011b16384
split: test
type: mteb/sts12-sts
metrics:
- type: pearson
value: 64.1057
- type: spearman
value: 56.583099999999995
- type: cosine_pearson
value: 64.1057
- type: cosine_spearman
value: 56.5833
- type: manhattan_pearson
value: 60.131299999999996
- type: manhattan_spearman
value: 56.4581
- type: euclidean_pearson
value: 60.3895
- type: euclidean_spearman
value: 56.5847
- type: main_score
value: 56.5833
task:
type: STS
- dataset:
config: default
name: MTEB STS13 (default)
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
split: test
type: mteb/sts13-sts
metrics:
- type: pearson
value: 64.35300000000001
- type: spearman
value: 64.21679999999999
- type: cosine_pearson
value: 64.35300000000001
- type: cosine_spearman
value: 64.21679999999999
- type: manhattan_pearson
value: 64.95779999999999
- type: manhattan_spearman
value: 63.9915
- type: euclidean_pearson
value: 65.1861
- type: euclidean_spearman
value: 64.2166
- type: main_score
value: 64.21679999999999
task:
type: STS
- dataset:
config: default
name: MTEB STS14 (default)
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
split: test
type: mteb/sts14-sts
metrics:
- type: pearson
value: 64.445
- type: spearman
value: 62.6363
- type: cosine_pearson
value: 64.445
- type: cosine_spearman
value: 62.6364
- type: manhattan_pearson
value: 62.79280000000001
- type: manhattan_spearman
value: 62.363800000000005
- type: euclidean_pearson
value: 63.1601
- type: euclidean_spearman
value: 62.6364
- type: main_score
value: 62.6364
task:
type: STS
- dataset:
config: default
name: MTEB STS15 (default)
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
split: test
type: mteb/sts15-sts
metrics:
- type: pearson
value: 74.6721
- type: spearman
value: 74.7446
- type: cosine_pearson
value: 74.6721
- type: cosine_spearman
value: 74.7445
- type: manhattan_pearson
value: 73.4179
- type: manhattan_spearman
value: 74.51950000000001
- type: euclidean_pearson
value: 73.694
- type: euclidean_spearman
value: 74.7446
- type: main_score
value: 74.7445
task:
type: STS
- dataset:
config: default
name: MTEB STS16 (default)
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
split: test
type: mteb/sts16-sts
metrics:
- type: pearson
value: 64.2321
- type: spearman
value: 64.78059999999999
- type: cosine_pearson
value: 64.2321
- type: cosine_spearman
value: 64.78059999999999
- type: manhattan_pearson
value: 64.5397
- type: manhattan_spearman
value: 64.4554
- type: euclidean_pearson
value: 64.84450000000001
- type: euclidean_spearman
value: 64.78059999999999
- type: main_score
value: 64.78059999999999
task:
type: STS
- dataset:
config: en-en
name: MTEB STS17 (en-en)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: pearson
value: 73.3702
- type: spearman
value: 73.34049999999999
- type: cosine_pearson
value: 73.3702
- type: cosine_spearman
value: 73.34049999999999
- type: manhattan_pearson
value: 73.3631
- type: manhattan_spearman
value: 73.1052
- type: euclidean_pearson
value: 73.4993
- type: euclidean_spearman
value: 73.34049999999999
- type: main_score
value: 73.34049999999999
task:
type: STS
- dataset:
config: en
name: MTEB STS22.v2 (en)
revision: d31f33a128469b20e357535c39b82fb3c3f6f2bd
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: pearson
value: 45.0436
- type: spearman
value: 50.741899999999994
- type: cosine_pearson
value: 45.0436
- type: cosine_spearman
value: 50.741899999999994
- type: manhattan_pearson
value: 48.2923
- type: manhattan_spearman
value: 50.9881
- type: euclidean_pearson
value: 47.903600000000004
- type: euclidean_spearman
value: 50.741899999999994
- type: main_score
value: 50.741899999999994
task:
type: STS
- dataset:
config: default
name: MTEB STSBenchmark (default)
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
split: test
type: mteb/stsbenchmark-sts
metrics:
- type: pearson
value: 60.2024
- type: spearman
value: 58.4387
- type: cosine_pearson
value: 60.2024
- type: cosine_spearman
value: 58.4387
- type: manhattan_pearson
value: 59.1592
- type: manhattan_spearman
value: 58.1857
- type: euclidean_pearson
value: 59.4892
- type: euclidean_spearman
value: 58.4387
- type: main_score
value: 58.4387
task:
type: STS
- dataset:
config: en
name: MTEB STSBenchmarkMultilingualSTS (en)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: dev
type: mteb/stsb_multi_mt
metrics:
- type: pearson
value: 71.8027
- type: spearman
value: 71.6553
- type: cosine_pearson
value: 71.8027
- type: cosine_spearman
value: 71.6549
- type: manhattan_pearson
value: 70.0193
- type: manhattan_spearman
value: 71.2307
- type: euclidean_pearson
value: 70.5146
- type: euclidean_spearman
value: 71.6549
- type: main_score
value: 71.6549
task:
type: STS
- dataset:
config: pt
name: MTEB STSBenchmarkMultilingualSTS (pt)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: dev
type: mteb/stsb_multi_mt
metrics:
- type: pearson
value: 71.5035
- type: spearman
value: 71.2337
- type: cosine_pearson
value: 71.5035
- type: cosine_spearman
value: 71.2337
- type: manhattan_pearson
value: 70.7969
- type: manhattan_spearman
value: 71.27459999999999
- type: euclidean_pearson
value: 70.7449
- type: euclidean_spearman
value: 71.2337
- type: main_score
value: 71.2337
task:
type: STS
- dataset:
config: en
name: MTEB STSBenchmarkMultilingualSTS (en)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: test
type: mteb/stsb_multi_mt
metrics:
- type: pearson
value: 60.2024
- type: spearman
value: 58.4387
- type: cosine_pearson
value: 60.2024
- type: cosine_spearman
value: 58.4387
- type: manhattan_pearson
value: 59.1592
- type: manhattan_spearman
value: 58.1857
- type: euclidean_pearson
value: 59.4892
- type: euclidean_spearman
value: 58.4387
- type: main_score
value: 58.4387
task:
type: STS
- dataset:
config: pt
name: MTEB STSBenchmarkMultilingualSTS (pt)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: test
type: mteb/stsb_multi_mt
metrics:
- type: pearson
value: 63.5201
- type: spearman
value: 62.578100000000006
- type: cosine_pearson
value: 63.5201
- type: cosine_spearman
value: 62.5783
- type: manhattan_pearson
value: 62.4579
- type: manhattan_spearman
value: 62.52910000000001
- type: euclidean_pearson
value: 62.49209999999999
- type: euclidean_spearman
value: 62.5783
- type: main_score
value: 62.5783
task:
type: STS
- dataset:
config: eng
name: MTEB SemRel24STS (eng)
revision: ef5c383d1b87eb8feccde3dfb7f95e42b1b050dd
split: test
type: SemRel/SemRel2024
metrics:
- type: pearson
value: 71.6465
- type: spearman
value: 70.3151
- type: cosine_pearson
value: 71.6465
- type: cosine_spearman
value: 70.3151
- type: manhattan_pearson
value: 70.49260000000001
- type: manhattan_spearman
value: 70.1974
- type: euclidean_pearson
value: 70.763
- type: euclidean_spearman
value: 70.3151
- type: main_score
value: 70.3151
task:
type: STS
This Model2Vec model was created by using Tokenlearn, with nomic-embed-text-v2-moe as a base.
The output dimension is 768.
Usage
Load this model using the from_pretrained method:
from model2vec import StaticModel
# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("cnmoro/static-nomic-large")
# Compute text embeddings
embeddings = model.encode(["Example sentence"])