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tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dense
  - generated_from_trainer
  - dataset_size:22
  - loss:MatryoshkaLoss
  - loss:MultipleNegativesRankingLoss
base_model: intfloat/multilingual-e5-base
widget:
  - source_sentence: >-
      К ним относятся граждане Российской Федерации, у которых один или оба
      родителя являются (или являлись) военнослужащими или сотрудниками.
    sentences:
      - >-
        Кто относится к числу детей военнослужащих и сотрудников в Российской
        Федерации?
      - >-
        Что происходит с местами специальной квоты, которые остались
        незаполненными после зачисления?
      - >-
        Для каких категорий детей военнослужащих и сотрудников предусмотрены
        особые условия приема в МГУ по результатам общеобразовательных и
        дополнительных вступительных испытаний в 2022 году?
  - source_sentence: The provided text is empty, so there is no main topic to discuss.
    sentences:
      - >-
        Из скольких частей состоит конкурсный список на места в пределах
        специальной квоты и что включает в себя первая часть?
      - Что требуется для зачисления в рамках специальной квоты?
      - What is the main topic of the provided text?
  - source_sentence: >-
      Специальная квота выделяется по программам бакалавриата и программам
      специалитета.
    sentences:
      - >-
        На какие образовательные программы выделяется специальная квота в
        соответствии с приведенным текстом?
      - >-
        Какое минимальное требование к баллам за ДВИ установлено для поступающих
        по специальной квоте в МГУ в 2022 году?
      - >-
        Могут ли поступающие, указанные в пункте 8, использовать результаты ЕГЭ
        при поступлении в МГУ?
  - source_sentence: >-
      В заявлении они указывают, что являются детьми военнослужащих или
      сотрудников, и прикладывают копии документов, подтверждающих это право.
    sentences:
      - >-
        Что указывают в заявлении о приеме поступающие на места в пределах
        специальной квоты?
      - >-
        На сколько этапов осуществляется зачисление на места в пределах
        специальной квоты в МГУ имени М.В.Ломоносова?
      - >-
        На какие образовательные программы распространяется Указ № 268 с учетом
        приказа Минобрнауки от 21 августа 2020 г.?
  - source_sentence: >-
      Прием осуществляется только по результатам дополнительных вступительных
      испытаний (ДВИ профильной, творческой и/или профессиональной
      направленности), проводимых МГУ в 2022 году.
    sentences:
      - >-
        На каком основании осуществляется отнесение поступающих к числу детей
        военнослужащих и сотрудников в пределах специальной квоты?
      - >-
        Какие категории лиц имеют право на прием в пределах специальной квоты в
        МГУ в 2022 году согласно представленным Особенностям?
      - >-
        Как осуществляется прием по специальной квоте для детей военнослужащих и
        сотрудников, погибших или получивших увечье, в МГУ в 2022 году?
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - cosine_accuracy@1
  - cosine_accuracy@3
  - cosine_accuracy@5
  - cosine_accuracy@10
  - cosine_precision@1
  - cosine_precision@3
  - cosine_precision@5
  - cosine_precision@10
  - cosine_recall@1
  - cosine_recall@3
  - cosine_recall@5
  - cosine_recall@10
  - cosine_ndcg@10
  - cosine_mrr@10
  - cosine_map@100
model-index:
  - name: SentenceTransformer based on intfloat/multilingual-e5-base
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 768
          type: dim_768
        metrics:
          - type: cosine_accuracy@1
            value: 0.4090909090909091
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.5454545454545454
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.6818181818181818
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9090909090909091
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.4090909090909091
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.18181818181818182
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1363636363636364
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09090909090909093
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.4090909090909091
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.5454545454545454
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.6818181818181818
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9090909090909091
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.6218521355989215
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.5343434343434343
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.5400475341651813
            name: Cosine Map@100
          - type: cosine_accuracy@1
            value: 0.5454545454545454
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.9090909090909091
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.9090909090909091
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 1
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.5454545454545454
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.303030303030303
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.18181818181818185
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.10000000000000003
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.5454545454545454
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.9090909090909091
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.9090909090909091
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 1
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7865203171807432
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7167207792207791
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7167207792207791
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.4090909090909091
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.5454545454545454
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.6818181818181818
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8636363636363636
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.4090909090909091
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.18181818181818182
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1363636363636364
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08636363636363639
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.4090909090909091
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.5454545454545454
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.6818181818181818
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8636363636363636
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.6067456436856664
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.5277597402597403
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.5366137603366705
            name: Cosine Map@100
          - type: cosine_accuracy@1
            value: 0.6363636363636364
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8636363636363636
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.9090909090909091
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 1
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6363636363636364
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2878787878787878
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.18181818181818185
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.10000000000000003
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6363636363636364
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8636363636363636
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.9090909090909091
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 1
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8169210922230377
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7583874458874459
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7583874458874459
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.4090909090909091
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.5454545454545454
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.7727272727272727
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9090909090909091
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.4090909090909091
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.18181818181818182
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1545454545454546
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09090909090909093
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.4090909090909091
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.5454545454545454
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.7727272727272727
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9090909090909091
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.619726662716026
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.5318722943722943
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.5386783225686969
            name: Cosine Map@100
          - type: cosine_accuracy@1
            value: 0.6363636363636364
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.9545454545454546
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.9545454545454546
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 1
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6363636363636364
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.3181818181818181
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.19090909090909094
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.10000000000000003
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6363636363636364
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.9545454545454546
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.9545454545454546
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 1
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8414025434075142
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7878787878787878
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7878787878787878
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.4090909090909091
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.5454545454545454
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.6363636363636364
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8636363636363636
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.4090909090909091
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.18181818181818182
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.12727272727272732
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08636363636363639
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.4090909090909091
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.5454545454545454
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.6363636363636364
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8636363636363636
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.5940890631161904
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.5130591630591631
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.5219218281718282
            name: Cosine Map@100
          - type: cosine_accuracy@1
            value: 0.5909090909090909
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 1
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 1
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 1
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.5909090909090909
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.33333333333333326
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.20000000000000007
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.10000000000000003
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.5909090909090909
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 1
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 1
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 1
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8133086027597443
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7499999999999999
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7499999999999999
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.5
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.6363636363636364
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.7727272727272727
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9090909090909091
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.5
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2121212121212121
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1545454545454546
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09090909090909093
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.5
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.6363636363636364
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.7727272727272727
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9090909090909091
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.667548589082649
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.5939935064935066
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.5989400305576777
            name: Cosine Map@100
          - type: cosine_accuracy@1
            value: 0.8181818181818182
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.9545454545454546
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 1
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 1
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.8181818181818182
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.3181818181818181
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.20000000000000007
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.10000000000000003
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.8181818181818182
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.9545454545454546
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 1
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 1
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.9158505597444297
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.8878787878787878
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.8878787878787878
            name: Cosine Map@100

SentenceTransformer based on intfloat/multilingual-e5-base

This is a sentence-transformers model finetuned from intfloat/multilingual-e5-base on the train dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: intfloat/multilingual-e5-base
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- train

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'XLMRobertaModel'})
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Прием осуществляется только по результатам дополнительных вступительных испытаний (ДВИ профильной, творческой и/или профессиональной направленности), проводимых МГУ в 2022 году.',
    'Как осуществляется прием по специальной квоте для детей военнослужащих и сотрудников, погибших или получивших увечье, в МГУ в 2022 году?',
    'На каком основании осуществляется отнесение поступающих к числу детей военнослужащих и сотрудников в пределах специальной квоты?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.8577, 0.7622],
#         [0.8577, 1.0000, 0.8551],
#         [0.7622, 0.8551, 1.0000]])

Evaluation

Metrics

Information Retrieval

Dataset: dim_768
Evaluated with InformationRetrievalEvaluator with these parameters:
```
{
    "truncate_dim": 768
}
```

Metric	Value
cosine_accuracy@1	0.4091
cosine_accuracy@3	0.5455
cosine_accuracy@5	0.6818
cosine_accuracy@10	0.9091
cosine_precision@1	0.4091
cosine_precision@3	0.1818
cosine_precision@5	0.1364
cosine_precision@10	0.0909
cosine_recall@1	0.4091
cosine_recall@3	0.5455
cosine_recall@5	0.6818
cosine_recall@10	0.9091
cosine_ndcg@10	0.6219
cosine_mrr@10	0.5343
cosine_map@100	0.54

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator with these parameters:
```
{
    "truncate_dim": 512
}
```

Metric	Value
cosine_accuracy@1	0.4091
cosine_accuracy@3	0.5455
cosine_accuracy@5	0.6818
cosine_accuracy@10	0.8636
cosine_precision@1	0.4091
cosine_precision@3	0.1818
cosine_precision@5	0.1364
cosine_precision@10	0.0864
cosine_recall@1	0.4091
cosine_recall@3	0.5455
cosine_recall@5	0.6818
cosine_recall@10	0.8636
cosine_ndcg@10	0.6067
cosine_mrr@10	0.5278
cosine_map@100	0.5366

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator with these parameters:
```
{
    "truncate_dim": 256
}
```

Metric	Value
cosine_accuracy@1	0.4091
cosine_accuracy@3	0.5455
cosine_accuracy@5	0.7727
cosine_accuracy@10	0.9091
cosine_precision@1	0.4091
cosine_precision@3	0.1818
cosine_precision@5	0.1545
cosine_precision@10	0.0909
cosine_recall@1	0.4091
cosine_recall@3	0.5455
cosine_recall@5	0.7727
cosine_recall@10	0.9091
cosine_ndcg@10	0.6197
cosine_mrr@10	0.5319
cosine_map@100	0.5387

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator with these parameters:
```
{
    "truncate_dim": 128
}
```

Metric	Value
cosine_accuracy@1	0.4091
cosine_accuracy@3	0.5455
cosine_accuracy@5	0.6364
cosine_accuracy@10	0.8636
cosine_precision@1	0.4091
cosine_precision@3	0.1818
cosine_precision@5	0.1273
cosine_precision@10	0.0864
cosine_recall@1	0.4091
cosine_recall@3	0.5455
cosine_recall@5	0.6364
cosine_recall@10	0.8636
cosine_ndcg@10	0.5941
cosine_mrr@10	0.5131
cosine_map@100	0.5219

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator with these parameters:
```
{
    "truncate_dim": 64
}
```

Metric	Value
cosine_accuracy@1	0.5
cosine_accuracy@3	0.6364
cosine_accuracy@5	0.7727
cosine_accuracy@10	0.9091
cosine_precision@1	0.5
cosine_precision@3	0.2121
cosine_precision@5	0.1545
cosine_precision@10	0.0909
cosine_recall@1	0.5
cosine_recall@3	0.6364
cosine_recall@5	0.7727
cosine_recall@10	0.9091
cosine_ndcg@10	0.6675
cosine_mrr@10	0.594
cosine_map@100	0.5989

Information Retrieval

Dataset: dim_768
Evaluated with InformationRetrievalEvaluator with these parameters:
```
{
    "truncate_dim": 768
}
```

Metric	Value
cosine_accuracy@1	0.5455
cosine_accuracy@3	0.9091
cosine_accuracy@5	0.9091
cosine_accuracy@10	1.0
cosine_precision@1	0.5455
cosine_precision@3	0.303
cosine_precision@5	0.1818
cosine_precision@10	0.1
cosine_recall@1	0.5455
cosine_recall@3	0.9091
cosine_recall@5	0.9091
cosine_recall@10	1.0
cosine_ndcg@10	0.7865
cosine_mrr@10	0.7167
cosine_map@100	0.7167

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator with these parameters:
```
{
    "truncate_dim": 512
}
```

Metric	Value
cosine_accuracy@1	0.6364
cosine_accuracy@3	0.8636
cosine_accuracy@5	0.9091
cosine_accuracy@10	1.0
cosine_precision@1	0.6364
cosine_precision@3	0.2879
cosine_precision@5	0.1818
cosine_precision@10	0.1
cosine_recall@1	0.6364
cosine_recall@3	0.8636
cosine_recall@5	0.9091
cosine_recall@10	1.0
cosine_ndcg@10	0.8169
cosine_mrr@10	0.7584
cosine_map@100	0.7584

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator with these parameters:
```
{
    "truncate_dim": 256
}
```

Metric	Value
cosine_accuracy@1	0.6364
cosine_accuracy@3	0.9545
cosine_accuracy@5	0.9545
cosine_accuracy@10	1.0
cosine_precision@1	0.6364
cosine_precision@3	0.3182
cosine_precision@5	0.1909
cosine_precision@10	0.1
cosine_recall@1	0.6364
cosine_recall@3	0.9545
cosine_recall@5	0.9545
cosine_recall@10	1.0
cosine_ndcg@10	0.8414
cosine_mrr@10	0.7879
cosine_map@100	0.7879

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator with these parameters:
```
{
    "truncate_dim": 128
}
```

Metric	Value
cosine_accuracy@1	0.5909
cosine_accuracy@3	1.0
cosine_accuracy@5	1.0
cosine_accuracy@10	1.0
cosine_precision@1	0.5909
cosine_precision@3	0.3333
cosine_precision@5	0.2
cosine_precision@10	0.1
cosine_recall@1	0.5909
cosine_recall@3	1.0
cosine_recall@5	1.0
cosine_recall@10	1.0
cosine_ndcg@10	0.8133
cosine_mrr@10	0.75
cosine_map@100	0.75

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator with these parameters:
```
{
    "truncate_dim": 64
}
```

Metric	Value
cosine_accuracy@1	0.8182
cosine_accuracy@3	0.9545
cosine_accuracy@5	1.0
cosine_accuracy@10	1.0
cosine_precision@1	0.8182
cosine_precision@3	0.3182
cosine_precision@5	0.2
cosine_precision@10	0.1
cosine_recall@1	0.8182
cosine_recall@3	0.9545
cosine_recall@5	1.0
cosine_recall@10	1.0
cosine_ndcg@10	0.9159
cosine_mrr@10	0.8879
cosine_map@100	0.8879

Training Details

Training Dataset

train

Dataset: train
Size: 22 training samples
Columns: positive and anchor
Approximate statistics based on the first 22 samples:
positive anchor
type string string
details
min: 8 tokens
mean: 38.55 tokens
max: 90 tokens

min: 12 tokens
mean: 25.95 tokens
max: 39 tokens

	positive	anchor
type	string	string
details	min: 8 tokens mean: 38.55 tokens max: 90 tokens	min: 12 tokens mean: 25.95 tokens max: 39 tokens

Samples:

positive	anchor
`The provided text is empty, so there is no main topic to discuss.`	`What is the main topic of the provided text?`
На прием в пределах специальной квоты имеют право дети военнослужащих и сотрудников федеральных органов исполнительной власти и государственных органов, где предусмотрена военная служба, а также сотрудников органов внутренних дел РФ, которые участвовали или участвуют в специальной военной операции на территориях Донецкой Народной Республики, Луганской Народной Республики и Украины, в том числе погибших (умерших) при исполнении обязанностей.	`Какие категории лиц имеют право на прием в пределах специальной квоты в МГУ в 2022 году согласно представленным Особенностям?`
`Указ № 268 с учетом указанного приказа распространяется на прием на обучение по программам высшего образования – бакалавриата, магистратуры и специалитета.`	`На какие образовательные программы распространяется Указ № 268 с учетом приказа Минобрнауки от 21 августа 2020 г.?`

Loss: MatryoshkaLoss with these parameters:

{
    "loss": "MultipleNegativesRankingLoss",
    "matryoshka_dims": [
        768,
        512,
        256,
        128,
        64
    ],
    "matryoshka_weights": [
        1,
        1,
        1,
        1,
        1
    ],
    "n_dims_per_step": -1
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 4
per_device_eval_batch_size: 16
gradient_accumulation_steps: 16
learning_rate: 2e-05
num_train_epochs: 5
lr_scheduler_type: cosine
warmup_ratio: 0.1
bf16: True
load_best_model_at_end: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
prediction_loss_only: True
per_device_train_batch_size: 4
per_device_eval_batch_size: 16
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 16
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 2e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 5
max_steps: -1
lr_scheduler_type: cosine
lr_scheduler_kwargs: {}
warmup_ratio: 0.1
warmup_steps: 0
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
save_safetensors: True
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
no_cuda: False
use_cpu: False
use_mps_device: False
seed: 42
data_seed: None
jit_mode_eval: False
bf16: True
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: True
ignore_data_skip: False
fsdp: []
fsdp_min_num_params: 0
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
fsdp_transformer_layer_cls_to_wrap: None
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
parallelism_config: None
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
project: huggingface
trackio_space_id: trackio
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
dataloader_pin_memory: True
dataloader_persistent_workers: False
skip_memory_metrics: True
use_legacy_prediction_loop: False
push_to_hub: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_private_repo: None
hub_always_push: False
hub_revision: None
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
eval_do_concat_batches: True
fp16_backend: auto
push_to_hub_model_id: None
push_to_hub_organization: None
mp_parameters:
auto_find_batch_size: False
full_determinism: False
torchdynamo: None
ray_scope: last
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
include_tokens_per_second: False
include_num_input_tokens_seen: no
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: True
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	dim_768_cosine_ndcg@10	dim_512_cosine_ndcg@10	dim_256_cosine_ndcg@10	dim_128_cosine_ndcg@10	dim_64_cosine_ndcg@10
-1	-1	0.6232	0.6067	0.6197	0.5875	0.6656
1.0	1	0.6219	0.6067	0.6197	0.5941	0.6675
-1	-1	0.6219	0.6067	0.6197	0.5941	0.6675
1.0	1	0.6219	0.6067	0.6197	0.5941	0.6675
2.0	2	0.6763	0.6801	0.7014	0.6618	0.7527
3.0	3	0.7702	0.7747	0.7452	0.7744	0.8252
4.0	4	0.7834	0.7995	0.8285	0.7979	0.8900
5.0	5	0.7865	0.8169	0.8414	0.8133	0.9159

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.12.12
Sentence Transformers: 5.1.2
Transformers: 4.57.1
PyTorch: 2.8.0+cu126
Accelerate: 1.11.0
Datasets: 4.0.0
Tokenizers: 0.22.1

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

MatryoshkaLoss

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}