metadata
license: mit
language:
- en
- ru
metrics:
- accuracy
- f1
- recall
library_name: transformers
pipeline_tag: sentence-similarity
tags:
- mteb
- retrieval
- retriever
- pruned
- e5
- sentence-transformers
- feature-extraction
- sentence-similarity
model-index:
- name: e5-large-en-ru
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 79.5671641791045
- type: ap
value: 44.011060753169424
- type: f1
value: 73.76504135120175
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 57.69669466706412
- type: mrr
value: 70.61370531592138
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 86.36465960226795
- type: cos_sim_spearman
value: 84.57602350761223
- type: euclidean_pearson
value: 84.31391364490506
- type: euclidean_spearman
value: 84.57602350761223
- type: manhattan_pearson
value: 84.15796224236456
- type: manhattan_spearman
value: 84.3645729064343
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.105698873583098
- type: mrr
value: 32.163780846856206
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 83.75973907678062
- type: cos_sim_spearman
value: 80.54994608351296
- type: euclidean_pearson
value: 80.58496551316748
- type: euclidean_spearman
value: 80.54993996457814
- type: manhattan_pearson
value: 80.49280884070782
- type: manhattan_spearman
value: 80.41230093993471
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 87.345503928209
- type: cos_sim_spearman
value: 80.4634619001261
- type: euclidean_pearson
value: 84.2666575030677
- type: euclidean_spearman
value: 80.46347579495351
- type: manhattan_pearson
value: 84.14370038922885
- type: manhattan_spearman
value: 80.36565043629274
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 75.14644787456163
- type: cos_sim_spearman
value: 75.88443166051762
- type: euclidean_pearson
value: 76.19117255044588
- type: euclidean_spearman
value: 75.88443166051762
- type: manhattan_pearson
value: 76.00450128624708
- type: manhattan_spearman
value: 75.69943934692938
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 77.60763524019471
- type: cos_sim_spearman
value: 77.2591077818027
- type: euclidean_pearson
value: 77.14021401348042
- type: euclidean_spearman
value: 77.25911027186999
- type: manhattan_pearson
value: 76.87139081109731
- type: manhattan_spearman
value: 76.98379627773018
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 88.18321035966198
- type: cos_sim_spearman
value: 89.0469892725742
- type: euclidean_pearson
value: 88.05085809092137
- type: euclidean_spearman
value: 89.04698194601134
- type: manhattan_pearson
value: 88.03620967628684
- type: manhattan_spearman
value: 89.02859425307943
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 82.39166503459249
- type: cos_sim_spearman
value: 83.71826060604693
- type: euclidean_pearson
value: 82.70145770530107
- type: euclidean_spearman
value: 83.71826045549452
- type: manhattan_pearson
value: 82.56870669205291
- type: manhattan_spearman
value: 83.55353737670136
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 89.58290721169323
- type: cos_sim_spearman
value: 89.25956993522081
- type: euclidean_pearson
value: 89.4716703635447
- type: euclidean_spearman
value: 89.25956993522081
- type: manhattan_pearson
value: 89.4475864648432
- type: manhattan_spearman
value: 89.14694174575615
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 81.4879065181404
- type: mrr
value: 94.81295937178291
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.73960396039604
- type: cos_sim_ap
value: 92.70840767967965
- type: cos_sim_f1
value: 86.90890990542557
- type: cos_sim_precision
value: 86.5213082259663
- type: cos_sim_recall
value: 87.3
- type: dot_accuracy
value: 99.73960396039604
- type: dot_ap
value: 92.70828452993575
- type: dot_f1
value: 86.90890990542557
- type: dot_precision
value: 86.5213082259663
- type: dot_recall
value: 87.3
- type: euclidean_accuracy
value: 99.73960396039604
- type: euclidean_ap
value: 92.7084093403562
- type: euclidean_f1
value: 86.90890990542557
- type: euclidean_precision
value: 86.5213082259663
- type: euclidean_recall
value: 87.3
- type: manhattan_accuracy
value: 99.74059405940594
- type: manhattan_ap
value: 92.7406819850299
- type: manhattan_f1
value: 87.01234567901234
- type: manhattan_precision
value: 85.95121951219512
- type: manhattan_recall
value: 88.1
- type: max_accuracy
value: 99.74059405940594
- type: max_ap
value: 92.7406819850299
- type: max_f1
value: 87.01234567901234
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 48.566931484512196
- type: mrr
value: 49.23111100500807
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 86.27287357692079
- type: cos_sim_ap
value: 74.20855854505362
- type: cos_sim_f1
value: 69.09903201787044
- type: cos_sim_precision
value: 65.22961574507966
- type: cos_sim_recall
value: 73.45646437994723
- type: dot_accuracy
value: 86.27287357692079
- type: dot_ap
value: 74.20853189774614
- type: dot_f1
value: 69.09903201787044
- type: dot_precision
value: 65.22961574507966
- type: dot_recall
value: 73.45646437994723
- type: euclidean_accuracy
value: 86.27287357692079
- type: euclidean_ap
value: 74.20857455896677
- type: euclidean_f1
value: 69.09903201787044
- type: euclidean_precision
value: 65.22961574507966
- type: euclidean_recall
value: 73.45646437994723
- type: manhattan_accuracy
value: 86.2192287059665
- type: manhattan_ap
value: 74.0513280969461
- type: manhattan_f1
value: 69.13344473621389
- type: manhattan_precision
value: 63.12118570183086
- type: manhattan_recall
value: 76.41160949868075
- type: max_accuracy
value: 86.27287357692079
- type: max_ap
value: 74.20857455896677
- type: max_f1
value: 69.13344473621389
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.16055419722902
- type: cos_sim_ap
value: 86.03614264194854
- type: cos_sim_f1
value: 78.89855695205357
- type: cos_sim_precision
value: 73.74656938215409
- type: cos_sim_recall
value: 84.82445334154605
- type: dot_accuracy
value: 89.16055419722902
- type: dot_ap
value: 86.03614225282097
- type: dot_f1
value: 78.89855695205357
- type: dot_precision
value: 73.74656938215409
- type: dot_recall
value: 84.82445334154605
- type: euclidean_accuracy
value: 89.16055419722902
- type: euclidean_ap
value: 86.0361548355667
- type: euclidean_f1
value: 78.89855695205357
- type: euclidean_precision
value: 73.74656938215409
- type: euclidean_recall
value: 84.82445334154605
- type: manhattan_accuracy
value: 89.11786393448985
- type: manhattan_ap
value: 86.00799361972808
- type: manhattan_f1
value: 78.84721152788472
- type: manhattan_precision
value: 75.26776338816941
- type: manhattan_recall
value: 82.78410840776101
- type: max_accuracy
value: 89.16055419722902
- type: max_ap
value: 86.0361548355667
- type: max_f1
value: 78.89855695205357
E5-large-en-ru
Model info
This is vocabulary pruned version of intfloat/multilingual-e5-large.
Uses only russian and english tokens.
Size
| intfloat/multilingual-e5-large | d0rj/e5-large-en-ru | |
|---|---|---|
| Model size (MB) | 2135.82 | 1394.8 |
| Params (count) | 559,890,946 | 365,638,14 |
| Word embeddings dim | 256,002,048 | 61,749,248 |
Performance
Equal performance on SberQuAD dev benchmark.
| Metric on SberQuAD (4122 questions) | intfloat/multilingual-e5-large | d0rj/e5-large-en-ru |
|---|---|---|
| recall@3 | 0.787239204269772 | 0.7882096069868996 |
| map@3 | 0.7230713245997101 | 0.723192624939351 |
| mrr@3 | 0.7241630276564784 | 0.7243651948892132 |
| recall@5 | 0.8277535177098496 | 0.8284813197476953 |
| map@5 | 0.7301603186155587 | 0.7302573588872716 |
| mrr@5 | 0.7334667637069385 | 0.7335718906679607 |
| recall@10 | 0.8716642406598738 | 0.871421639980592 |
| map@10 | 0.7314774917730316 | 0.7313000338687417 |
| mrr@10 | 0.7392223685527911 | 0.7391814537556898 |
Usage
Use dot product distance for retrieval.
Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval.
Use "query: " prefix for symmetric tasks such as semantic similarity, bitext mining, paraphrase retrieval.
Use "query: " prefix if you want to use embeddings as features, such as linear probing classification, clustering.
transformers
Direct usage
import torch.nn.functional as F
from torch import Tensor
from transformers import XLMRobertaTokenizer, XLMRobertaModel
def average_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor:
last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
input_texts = [
'query: How does a corporate website differ from a business card website?',
'query: Где был создан первый троллейбус?',
'passage: The first trolleybus was created in Germany by engineer Werner von Siemens, probably influenced by the idea of his brother, Dr. Wilhelm Siemens, who lived in England, expressed on May 18, 1881 at the twenty-second meeting of the Royal Scientific Society. The electrical circuit was carried out by an eight-wheeled cart (Kontaktwagen) rolling along two parallel contact wires. The wires were located quite close to each other, and in strong winds they often overlapped, which led to short circuits. An experimental trolleybus line with a length of 540 m (591 yards), opened by Siemens & Halske in the Berlin suburb of Halensee, operated from April 29 to June 13, 1882.',
'passage: Корпоративный сайт — содержит полную информацию о компании-владельце, услугах/продукции, событиях в жизни компании. Отличается от сайта-визитки и представительского сайта полнотой представленной информации, зачастую содержит различные функциональные инструменты для работы с контентом (поиск и фильтры, календари событий, фотогалереи, корпоративные блоги, форумы). Может быть интегрирован с внутренними информационными системами компании-владельца (КИС, CRM, бухгалтерскими системами). Может содержать закрытые разделы для тех или иных групп пользователей — сотрудников, дилеров, контрагентов и пр.',
]
tokenizer = XLMRobertaTokenizer.from_pretrained('d0rj/e5-large-en-ru', use_cache=False)
model = XLMRobertaModel.from_pretrained('d0rj/e5-large-en-ru', use_cache=False)
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())
# [[68.59542846679688, 81.75910949707031], [80.36100769042969, 64.77748107910156]]
Pipeline
from transformers import pipeline
pipe = pipeline('feature-extraction', model='d0rj/e5-large-en-ru')
embeddings = pipe(input_texts, return_tensors=True)
embeddings[0].size()
# torch.Size([1, 17, 1024])
sentence-transformers
from sentence_transformers import SentenceTransformer
sentences = [
'query: Что такое круглые тензоры?',
'passage: Abstract: we introduce a novel method for compressing round tensors based on their inherent radial symmetry. We start by generalising PCA and eigen decomposition on round tensors...',
]
model = SentenceTransformer('d0rj/e5-large-en-ru')
embeddings = model.encode(sentences, convert_to_tensor=True)
embeddings.size()
# torch.Size([2, 1024])