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EN
Ru En RoSBERTa
R
Ru En RoSBERTa
Developed by ai-forever
RoBERTaアーキテクチャに基づくバイリンガル（ロシア語-英語）テキスト処理モデル、様々な分類と検索タスクに適応
テキスト埋め込み
Transformers Supports Multiple LanguagesOpen Source License:MIT#ロシア語・英語バイリンガル理解#マルチタスクテキスト分類#高精度意味マッチング
Transformers Downloads 15.95k
Release Time : 7/29/2024
Model Overview
このモデルはRoBERTaアーキテクチャに基づくバイリンガル（ロシア語-英語）テキスト処理モデルで、様々なテキスト分類と検索タスクで良好な性能を発揮し、マルチラベル分類、クラスタリング、並べ替えなどのタスクをサポート
Model Features
バイリンガルサポート
ロシア語と英語のテキスト処理を同時にサポート
マルチタスク適応
分類、クラスタリング、並べ替えなど様々なタスクで良好な性能
効率的な検索
MIRACLロシア語検索タスクで優れた性能を発揮
Model Capabilities
テキスト分類
マルチラベル分類
テキストクラスタリング
情報検索
テキスト並べ替え
Use Cases
コンテンツ分類
ニュース見出し分類
ニュース見出しを分類
HeadlineClassificationデータセットで78%の精度を達成
映画レビュー感情分析
映画レビューの感情傾向を分析
KinopoiskClassificationデータセットで63.27%の精度を達成
情報検索
ロシア語ドキュメント検索
ロシア語ドキュメントの検索と並べ替え
MIRACLロシア語検索タスクでNDCG@10が56.912を達成
model-index:
- name: ru-en-RoSBERTa
results:
- dataset:
config: default
name: MTEB CEDRClassification (default)
revision: c0ba03d058e3e1b2f3fd20518875a4563dd12db4
split: test
type: ai-forever/cedr-classification
metrics:
- type: accuracy value: 44.68650371944739
- type: f1 value: 40.7601061886426
- type: lrap value: 70.69633368756747
- type: main_score value: 44.68650371944739 task: type: MultilabelClassification
- dataset:
config: default
name: MTEB GeoreviewClassification (default)
revision: 3765c0d1de6b7d264bc459433c45e5a75513839c
split: test
type: ai-forever/georeview-classification
metrics:
- type: accuracy value: 49.697265625
- type: f1 value: 47.793186725286866
- type: f1_weighted value: 47.79131720298068
- type: main_score value: 49.697265625 task: type: Classification
- dataset:
config: default
name: MTEB GeoreviewClusteringP2P (default)
revision: 97a313c8fc85b47f13f33e7e9a95c1ad888c7fec
split: test
type: ai-forever/georeview-clustering-p2p
metrics:
- type: main_score value: 65.42249614873316
- type: v_measure value: 65.42249614873316
- type: v_measure_std value: 0.8524815312312278 task: type: Clustering
- dataset:
config: default
name: MTEB HeadlineClassification (default)
revision: 2fe05ee6b5832cda29f2ef7aaad7b7fe6a3609eb
split: test
type: ai-forever/headline-classification
metrics:
- type: accuracy value: 78.0029296875
- type: f1 value: 77.95151940601424
- type: f1_weighted value: 77.95054643947716
- type: main_score value: 78.0029296875 task: type: Classification
- dataset:
config: default
name: MTEB InappropriatenessClassification (default)
revision: 601651fdc45ef243751676e62dd7a19f491c0285
split: test
type: ai-forever/inappropriateness-classification
metrics:
- type: accuracy value: 61.32324218750001
- type: ap value: 57.11029460364367
- type: ap_weighted value: 57.11029460364367
- type: f1 value: 60.971337406307214
- type: f1_weighted value: 60.971337406307214
- type: main_score value: 61.32324218750001 task: type: Classification
- dataset:
config: default
name: MTEB KinopoiskClassification (default)
revision: 5911f26666ac11af46cb9c6849d0dc80a378af24
split: test
type: ai-forever/kinopoisk-sentiment-classification
metrics:
- type: accuracy value: 63.27333333333334
- type: f1 value: 61.007042785228116
- type: f1_weighted value: 61.007042785228116
- type: main_score value: 63.27333333333334 task: type: Classification
- dataset:
config: ru
name: MTEB MIRACLReranking (ru)
revision: 6d1962c527217f8927fca80f890f14f36b2802af
split: dev
type: miracl/mmteb-miracl-reranking
metrics:
- type: MAP@1(MIRACL) value: 30.691000000000003
- type: MAP@10(MIRACL) value: 49.178
- type: MAP@100(MIRACL) value: 51.225
- type: MAP@1000(MIRACL) value: 51.225
- type: MAP@20(MIRACL) value: 50.613
- type: MAP@3(MIRACL) value: 42.457
- type: MAP@5(MIRACL) value: 46.172000000000004
- type: NDCG@1(MIRACL) value: 51.002
- type: NDCG@10(MIRACL) value: 56.912
- type: NDCG@100(MIRACL) value: 61.197
- type: NDCG@1000(MIRACL) value: 61.197
- type: NDCG@20(MIRACL) value: 59.453
- type: NDCG@3(MIRACL) value: 51.083
- type: NDCG@5(MIRACL) value: 53.358000000000004
- type: P@1(MIRACL) value: 51.002
- type: P@10(MIRACL) value: 14.852000000000002
- type: P@100(MIRACL) value: 1.9529999999999998
- type: P@1000(MIRACL) value: 0.19499999999999998
- type: P@20(MIRACL) value: 8.657
- type: P@3(MIRACL) value: 31.435000000000002
- type: P@5(MIRACL) value: 23.608999999999998
- type: Recall@1(MIRACL) value: 30.691000000000003
- type: Recall@10(MIRACL) value: 67.006
- type: Recall@100(MIRACL) value: 79.952
- type: Recall@1000(MIRACL) value: 79.952
- type: Recall@20(MIRACL) value: 73.811
- type: Recall@3(MIRACL) value: 49.142
- type: Recall@5(MIRACL) value: 57.553
- type: main_score value: 56.912
- type: nAUC_MAP@1000_diff1(MIRACL) value: 10.786403475779332
- type: nAUC_MAP@1000_max(MIRACL) value: 29.477246196287275
- type: nAUC_MAP@1000_std(MIRACL) value: 15.938834129839046
- type: nAUC_MAP@100_diff1(MIRACL) value: 10.786403475779332
- type: nAUC_MAP@100_max(MIRACL) value: 29.477246196287275
- type: nAUC_MAP@100_std(MIRACL) value: 15.938834129839046
- type: nAUC_MAP@10_diff1(MIRACL) value: 12.255091348037595
- type: nAUC_MAP@10_max(MIRACL) value: 26.72625370045134
- type: nAUC_MAP@10_std(MIRACL) value: 14.180071586837812
- type: nAUC_MAP@1_diff1(MIRACL) value: 28.616487922173768
- type: nAUC_MAP@1_max(MIRACL) value: 12.986192530664518
- type: nAUC_MAP@1_std(MIRACL) value: 4.086145762604503
- type: nAUC_MAP@20_diff1(MIRACL) value: 11.360341572700476
- type: nAUC_MAP@20_max(MIRACL) value: 28.612330384153832
- type: nAUC_MAP@20_std(MIRACL) value: 15.787480742877937
- type: nAUC_MAP@3_diff1(MIRACL) value: 18.033783954867623
- type: nAUC_MAP@3_max(MIRACL) value: 20.97092332905034
- type: nAUC_MAP@3_std(MIRACL) value: 9.106058710108279
- type: nAUC_MAP@5_diff1(MIRACL) value: 14.784231238848433
- type: nAUC_MAP@5_max(MIRACL) value: 23.841145797143
- type: nAUC_MAP@5_std(MIRACL) value: 11.25686258970321
- type: nAUC_NDCG@1000_diff1(MIRACL) value: 1.4728095471561125
- type: nAUC_NDCG@1000_max(MIRACL) value: 39.84262968697792
- type: nAUC_NDCG@1000_std(MIRACL) value: 22.4186410243652
- type: nAUC_NDCG@100_diff1(MIRACL) value: 1.4728095471561125
- type: nAUC_NDCG@100_max(MIRACL) value: 39.84262968697792
- type: nAUC_NDCG@100_std(MIRACL) value: 22.4186410243652
- type: nAUC_NDCG@10_diff1(MIRACL) value: 5.242996478950954
- type: nAUC_NDCG@10_max(MIRACL) value: 33.86925934510759
- type: nAUC_NDCG@10_std(MIRACL) value: 19.457386638149625
- type: nAUC_NDCG@1_diff1(MIRACL) value: 16.925455715967676
- type: nAUC_NDCG@1_max(MIRACL) value: 36.72266755084653
- type: nAUC_NDCG@1_std(MIRACL) value: 18.357456476212622
- type: nAUC_NDCG@20_diff1(MIRACL) value: 3.361697278095995
- type: nAUC_NDCG@20_max(MIRACL) value: 37.38923489423496
- type: nAUC_NDCG@20_std(MIRACL) value: 22.29168372402657
- type: nAUC_NDCG@3_diff1(MIRACL) value: 10.936904314592084
- type: nAUC_NDCG@3_max(MIRACL) value: 30.547718047674284
- type: nAUC_NDCG@3_std(MIRACL) value: 15.142352896765665
- type: nAUC_NDCG@5_diff1(MIRACL) value: 8.618074920961075
- type: nAUC_NDCG@5_max(MIRACL) value: 30.808600807482367
- type: nAUC_NDCG@5_std(MIRACL) value: 15.793512242130051
- type: nAUC_P@1000_diff1(MIRACL) value: -24.81839490148569
- type: nAUC_P@1000_max(MIRACL) value: 34.16200383739091
- type: nAUC_P@1000_std(MIRACL) value: 20.95890369662007
- type: nAUC_P@100_diff1(MIRACL) value: -24.818394901485657
- type: nAUC_P@100_max(MIRACL) value: 34.16200383739092
- type: nAUC_P@100_std(MIRACL) value: 20.958903696620112
- type: nAUC_P@10_diff1(MIRACL) value: -22.646461560750986
- type: nAUC_P@10_max(MIRACL) value: 34.57373514819872
- type: nAUC_P@10_std(MIRACL) value: 24.27599718176041
- type: nAUC_P@1_diff1(MIRACL) value: 16.925455715967676
- type: nAUC_P@1_max(MIRACL) value: 36.72266755084653
- type: nAUC_P@1_std(MIRACL) value: 18.357456476212622
- type: nAUC_P@20_diff1(MIRACL) value: -23.33449798384014
- type: nAUC_P@20_max(MIRACL) value: 34.92822081787735
- type: nAUC_P@20_std(MIRACL) value: 25.048280657629267
- type: nAUC_P@3_diff1(MIRACL) value: -11.60659490286
- type: nAUC_P@3_max(MIRACL) value: 38.187883056013035
- type: nAUC_P@3_std(MIRACL) value: 21.234776997940628
- type: nAUC_P@5_diff1(MIRACL) value: -18.86697977242918
- type: nAUC_P@5_max(MIRACL) value: 35.6110661197626
- type: nAUC_P@5_std(MIRACL) value: 22.11165620702996
- type: nAUC_Recall@1000_diff1(MIRACL) value: -31.456413113303867
- type: nAUC_Recall@1000_max(MIRACL) value: 63.785265733309636
- type: nAUC_Recall@1000_std(MIRACL) value: 36.587933217871914
- type: nAUC_Recall@100_diff1(MIRACL) value: -31.456413113303867
- type: nAUC_Recall@100_max(MIRACL) value: 63.785265733309636
- type: nAUC_Recall@100_std(MIRACL) value: 36.587933217871914
- type: nAUC_Recall@10_diff1(MIRACL) value: -9.518740341549913
- type: nAUC_Recall@10_max(MIRACL) value: 35.00853357699468
- type: nAUC_Recall@10_std(MIRACL) value: 22.79313936486099
- type: nAUC_Recall@1_diff1(MIRACL) value: 28.616487922173768
- type: nAUC_Recall@1_max(MIRACL) value: 12.986192530664518
- type: nAUC_Recall@1_std(MIRACL) value: 4.086145762604503
- type: nAUC_Recall@20_diff1(MIRACL) value: -17.771143411342166
- type: nAUC_Recall@20_max(MIRACL) value: 47.59780316487735
- type: nAUC_Recall@20_std(MIRACL) value: 33.25494707686132
- type: nAUC_Recall@3_diff1(MIRACL) value: 10.171226133119783
- type: nAUC_Recall@3_max(MIRACL) value: 21.097634288680847
- type: nAUC_Recall@3_std(MIRACL) value: 10.087211861733298
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- type: nAUC_Recall@5_max(MIRACL) value: 25.874440474993165
- type: nAUC_Recall@5_std(MIRACL) value: 13.46380924822079 task: type: Reranking
- dataset:
config: ru
name: MTEB MIRACLRetrieval (ru)
revision: main
split: dev
type: miracl/mmteb-miracl
metrics:
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- type: map_at_1 value: 24.308
- type: map_at_10 value: 43.258
- type: map_at_100 value: 46.053
- type: map_at_1000 value: 46.176
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- type: nauc_recall_at_3_diff1 value: 26.13225834790859
- type: nauc_recall_at_3_max value: 27.915627935543725
- type: nauc_recall_at_3_std value: -8.069525359773976
- type: nauc_recall_at_5_diff1 value: 24.184086614024686
- type: nauc_recall_at_5_max value: 32.607378848166675
- type: nauc_recall_at_5_std value: -7.730984752196379
- type: ndcg_at_1 value: 55.969
- type: ndcg_at_10 value: 66.77499999999999
- type: ndcg_at_100 value: 70.324
- type: ndcg_at_1000 value: 70.95700000000001
- type: ndcg_at_20 value: 68.613
- type: ndcg_at_3 value: 59.256
- type: ndcg_at_5 value: 63.223
- type: precision_at_1 value: 55.969
- type: precision_at_10 value: 13.297999999999998
- type: precision_at_100 value: 1.585
- type: precision_at_1000 value: 0.167
- type: precision_at_20 value: 7.222
- type: precision_at_3 value: 32.467
- type: precision_at_5 value: 23.073
- type: recall_at_1 value: 38.964
- type: recall_at_10 value: 81.248
- type: recall_at_100 value: 95.124
- type: recall_at_1000 value: 99.30600000000001
- type: recall_at_20 value: 87.35199999999999
- type: recall_at_3 value: 62.785000000000004
- type: recall_at_5 value: 71.986 task: type: Retrieval
- dataset:
config: default
name: MTEB RuReviewsClassification (default)
revision: f6d2c31f4dc6b88f468552750bfec05b4b41b05a
split: test
type: ai-forever/ru-reviews-classification
metrics:
- type: accuracy value: 67.958984375
- type: f1 value: 67.250877785427
- type: f1_weighted value: 67.25215701797296
- type: main_score value: 67.958984375 task: type: Classification
- dataset:
config: default
name: MTEB RuSTSBenchmarkSTS (default)
revision: 7cf24f325c6da6195df55bef3d86b5e0616f3018
split: test
type: ai-forever/ru-stsbenchmark-sts
metrics:
- type: cosine_pearson value: 79.11336124619963
- type: cosine_spearman value: 78.69157477180703
- type: euclidean_pearson value: 77.84066073571212
- type: euclidean_spearman value: 78.69157477180703
- type: main_score value: 78.69157477180703
- type: manhattan_pearson value: 77.79213012957939
- type: manhattan_spearman value: 78.61384378877501
- type: pearson value: 79.11336124619963
- type: spearman value: 78.69157477180703 task: type: STS
- dataset:
config: default
name: MTEB RuSciBenchGRNTIClassification (default)
revision: 673a610d6d3dd91a547a0d57ae1b56f37ebbf6a1
split: test
type: ai-forever/ru-scibench-grnti-classification
metrics:
- type: accuracy value: 59.326171875
- type: f1 value: 58.01171745357119
- type: f1_weighted value: 58.02106511480968
- type: main_score value: 59.326171875 task: type: Classification
- dataset:
config: default
name: MTEB RuSciBenchGRNTIClusteringP2P (default)
revision: 673a610d6d3dd91a547a0d57ae1b56f37ebbf6a1
split: test
type: ai-forever/ru-scibench-grnti-classification
metrics:
- type: main_score value: 55.46570753380975
- type: v_measure value: 55.46570753380975
- type: v_measure_std value: 0.9813885872798612 task: type: Clustering
- dataset:
config: default
name: MTEB RuSciBenchOECDClassification (default)
revision: 26c88e99dcaba32bb45d0e1bfc21902337f6d471
split: test
type: ai-forever/ru-scibench-oecd-classification
metrics:
- type: accuracy value: 46.328125
- type: f1 value: 44.19158709013339
- type: f1_weighted value: 44.190957945676026
- type: main_score value: 46.328125 task: type: Classification
- dataset:
config: default
name: MTEB RuSciBenchOECDClusteringP2P (default)
revision: 26c88e99dcaba32bb45d0e1bfc21902337f6d471
split: test
type: ai-forever/ru-scibench-oecd-classification
metrics:
- type: main_score value: 47.28635342613908
- type: v_measure value: 47.28635342613908
- type: v_measure_std value: 0.7431017612993989 task: type: Clustering
- dataset:
config: ru
name: MTEB STS22 (ru)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cosine_pearson value: 63.10139371129796
- type: cosine_spearman value: 67.06445400504978
- type: euclidean_pearson value: 62.74563386470613
- type: euclidean_spearman value: 67.06445400504978
- type: main_score value: 67.06445400504978
- type: manhattan_pearson value: 62.540465664732395
- type: manhattan_spearman value: 66.65899492022648
- type: pearson value: 63.10139371129796
- type: spearman value: 67.06445400504978 task: type: STS
- dataset:
config: default
name: MTEB SensitiveTopicsClassification (default)
revision: 416b34a802308eac30e4192afc0ff99bb8dcc7f2
split: test
type: ai-forever/sensitive-topics-classification
metrics:
- type: accuracy value: 33.0712890625
- type: f1 value: 38.063573562290024
- type: lrap value: 49.586995442707696
- type: main_score value: 33.0712890625 task: type: MultilabelClassification
- dataset:
config: default
name: MTEB TERRa (default)
revision: 7b58f24536063837d644aab9a023c62199b2a612
split: dev
type: ai-forever/terra-pairclassification
metrics:
- type: cosine_accuracy value: 61.563517915309454
- type: cosine_accuracy_threshold value: 75.3734290599823
- type: cosine_ap value: 60.78861909325018
- type: cosine_f1 value: 67.25663716814158
- type: cosine_f1_threshold value: 54.05237674713135
- type: cosine_precision value: 50.836120401337794
- type: cosine_recall value: 99.34640522875817
- type: dot_accuracy value: 61.563517915309454
- type: dot_accuracy_threshold value: 75.37343502044678
- type: dot_ap value: 60.78861909325018
- type: dot_f1 value: 67.25663716814158
- type: dot_f1_threshold value: 54.05237674713135
- type: dot_precision value: 50.836120401337794
- type: dot_recall value: 99.34640522875817
- type: euclidean_accuracy value: 61.563517915309454
- type: euclidean_accuracy_threshold value: 70.18057107925415
- type: euclidean_ap value: 60.78861909325018
- type: euclidean_f1 value: 67.25663716814158
- type: euclidean_f1_threshold value: 95.86195945739746
- type: euclidean_precision value: 50.836120401337794
- type: euclidean_recall value: 99.34640522875817
- type: main_score value: 60.78861909325018
- type: manhattan_accuracy value: 60.91205211726385
- type: manhattan_accuracy_threshold value: 1813.1645202636719
- type: manhattan_ap value: 60.478709337038936
- type: manhattan_f1 value: 67.10816777041943
- type: manhattan_f1_threshold value: 2475.027275085449
- type: manhattan_precision value: 50.66666666666667
- type: manhattan_recall value: 99.34640522875817
- type: max_ap value: 60.78861909325018
- type: max_f1 value: 67.25663716814158
- type: max_precision value: 50.836120401337794
- type: max_recall value: 99.34640522875817
- type: similarity_accuracy value: 61.563517915309454
- type: similarity_accuracy_threshold value: 75.3734290599823
- type: similarity_ap value: 60.78861909325018
- type: similarity_f1 value: 67.25663716814158
- type: similarity_f1_threshold value: 54.05237674713135
- type: similarity_precision value: 50.836120401337794
- type: similarity_recall value: 99.34640522875817 task: type: PairClassification license: mit language:
- dataset:
config: default
name: MTEB CEDRClassification (default)
revision: c0ba03d058e3e1b2f3fd20518875a4563dd12db4
split: test
type: ai-forever/cedr-classification
metrics:
- ru
- en tags:
- mteb
- transformers
- sentence-transformers base_model: ai-forever/ruRoberta-large
Model Card for ru-en-RoSBERTa
The ru-en-RoSBERTa is a general text embedding model for Russian. The model is based on ruRoBERTa and fine-tuned with ~4M pairs of supervised, synthetic and unsupervised data in Russian and English. Tokenizer supports some English tokens from RoBERTa tokenizer.
For more model details please refer to our article.
Usage
The model can be used as is with prefixes. It is recommended to use CLS pooling. The choice of prefix and pooling depends on the task.
We use the following basic rules to choose a prefix:
"search_query: "and"search_document: "prefixes are for answer or relevant paragraph retrieval"classification: "prefix is for symmetric paraphrasing related tasks (STS, NLI, Bitext Mining)"clustering: "prefix is for any tasks that rely on thematic features (topic classification, title-body retrieval)
To better tailor the model to your needs, you can fine-tune it with relevant high-quality Russian and English datasets.
Below are examples of texts encoding using the Transformers and SentenceTransformers libraries.
Transformers
import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel
def pool(hidden_state, mask, pooling_method="cls"):
if pooling_method == "mean":
s = torch.sum(hidden_state * mask.unsqueeze(-1).float(), dim=1)
d = mask.sum(axis=1, keepdim=True).float()
return s / d
elif pooling_method == "cls":
return hidden_state[:, 0]
inputs = [
#
"classification: Он нам и <unk> не нужон ваш Интернет!",
"clustering: В Ярославской области разрешили работу бань, но без посетителей",
"search_query: Сколько программистов нужно, чтобы вкрутить лампочку?",
#
"classification: What a time to be alive!",
"clustering: Ярославским баням разрешили работать без посетителей",
"search_document: Чтобы вкрутить лампочку, требуется три программиста: один напишет программу извлечения лампочки, другой — вкручивания лампочки, а третий проведет тестирование.",
]
tokenizer = AutoTokenizer.from_pretrained("ai-forever/ru-en-RoSBERTa")
model = AutoModel.from_pretrained("ai-forever/ru-en-RoSBERTa")
tokenized_inputs = tokenizer(inputs, max_length=512, padding=True, truncation=True, return_tensors="pt")
with torch.no_grad():
outputs = model(**tokenized_inputs)
embeddings = pool(
outputs.last_hidden_state,
tokenized_inputs["attention_mask"],
pooling_method="cls" # or try "mean"
)
embeddings = F.normalize(embeddings, p=2, dim=1)
sim_scores = embeddings[:3] @ embeddings[3:].T
print(sim_scores.diag().tolist())
# [0.4796873927116394, 0.9409002065658569, 0.7761015892028809]
SentenceTransformers
from sentence_transformers import SentenceTransformer
inputs = [
#
"classification: Он нам и <unk> не нужон ваш Интернет!",
"clustering: В Ярославской области разрешили работу бань, но без посетителей",
"search_query: Сколько программистов нужно, чтобы вкрутить лампочку?",
#
"classification: What a time to be alive!",
"clustering: Ярославским баням разрешили работать без посетителей",
"search_document: Чтобы вкрутить лампочку, требуется три программиста: один напишет программу извлечения лампочки, другой — вкручивания лампочки, а третий проведет тестирование.",
]
# loads model with CLS pooling
model = SentenceTransformer("ai-forever/ru-en-RoSBERTa")
# embeddings are normalized by default
embeddings = model.encode(inputs, convert_to_tensor=True)
sim_scores = embeddings[:3] @ embeddings[3:].T
print(sim_scores.diag().tolist())
# [0.47968706488609314, 0.940900444984436, 0.7761018872261047]
or using prompts (sentence-transformers>=2.4.0):
from sentence_transformers import SentenceTransformer
# loads model with CLS pooling
model = SentenceTransformer("ai-forever/ru-en-RoSBERTa")
classification = model.encode(["Он нам и <unk> не нужон ваш Интернет!", "What a time to be alive!"], prompt_name="classification")
print(classification[0] @ classification[1].T) # 0.47968706488609314
clustering = model.encode(["В Ярославской области разрешили работу бань, но без посетителей", "Ярославским баням разрешили работать без посетителей"], prompt_name="clustering")
print(clustering[0] @ clustering[1].T) # 0.940900444984436
query_embedding = model.encode("Сколько программистов нужно, чтобы вкрутить лампочку?", prompt_name="search_query")
document_embedding = model.encode("Чтобы вкрутить лампочку, требуется три программиста: один напишет программу извлечения лампочки, другой — вкручивания лампочки, а третий проведет тестирование.", prompt_name="search_document")
print(query_embedding @ document_embedding.T) # 0.7761018872261047
Citation
@misc{snegirev2024russianfocusedembeddersexplorationrumteb,
title={The Russian-focused embedders' exploration: ruMTEB benchmark and Russian embedding model design},
author={Artem Snegirev and Maria Tikhonova and Anna Maksimova and Alena Fenogenova and Alexander Abramov},
year={2024},
eprint={2408.12503},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2408.12503},
}
Limitations
The model is designed to process texts in Russian, the quality in English is unknown. Maximum input text length is limited to 512 tokens.
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