README.md

metadata

base_model: sentence-transformers/all-MiniLM-L6-v2
language:
  - en
library_name: sentence-transformers
license: apache-2.0
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
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dataset_size:1K<n<10K
  - loss:MatryoshkaLoss
  - loss:MultipleNegativesRankingLoss
widget:
  - source_sentence: Żywot św. Stanisława
    sentences:
      - czym różni się Żywot św. Stanisława od Legendy św. Stanisława?
      - kto uczył malarstwa olimpijczyka Bronisława Czecha?
      - St. Louis Eagles
  - source_sentence: Jaakow Jicchak Szapira
    sentences:
      - czym jest Kompas Sztuki?
      - z czego wykonana jest rzeźba Robotnik i kołchoźnica?
      - podczas którego soboru zostało ogłoszone chalcedońskie wyznanie wiary?
  - source_sentence: Chłopiec z Nariokotome
    sentences:
      - ile wynosiła objętość mózgu chłopca z Nariokotome?
      - jaki pomnik odsłonięto we Lwowie 3 lipca 2011 roku?
      - Voyager 2 Voyager Golden Record Pale Blue Dot
  - source_sentence: skąd pochodzi wino cirò?
    sentences:
      - skąd pochodzi nazwa Kotylniczy Wierch?
      - do czego współcześnie wykorzystuje się papier amate?
      - erystyka sofizmat błędy logiczno-językowe onus probandi
  - source_sentence: Sen o zastrzyku Irmy
    sentences:
      - gdzie Freud spotkał Irmę we śnie o zastrzyku Irmy?
      - ile razy Srebrna Biblia była przywożona do Szwecji?
      - Voyager 2 Voyager Golden Record Pale Blue Dot
model-index:
  - name: all-MiniLM-L6-v2-klej-dyk-v0.1
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 384
          type: dim_384
        metrics:
          - type: cosine_accuracy@1
            value: 0.19951923076923078
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.43028846153846156
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.5384615384615384
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.6225961538461539
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.19951923076923078
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.14342948717948717
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.10769230769230768
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.06225961538461538
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.19951923076923078
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.43028846153846156
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.5384615384615384
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.6225961538461539
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.4067615454626299
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.3376678876678877
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.3451711286911671
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.18509615384615385
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.41346153846153844
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.5096153846153846
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.6033653846153846
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.18509615384615385
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.1378205128205128
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.10192307692307692
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.06033653846153846
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.18509615384615385
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.41346153846153844
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.5096153846153846
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.6033653846153846
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.39112028533472887
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.32341746794871795
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.3303671597529028
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.18028846153846154
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.35336538461538464
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.4423076923076923
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.5192307692307693
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.18028846153846154
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.11778846153846154
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.08846153846153845
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.05192307692307692
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.18028846153846154
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.35336538461538464
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.4423076923076923
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.5192307692307693
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.3443315125767603
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.2888621794871794
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.2960334956693037
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.13701923076923078
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.2644230769230769
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.32211538461538464
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.3798076923076923
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.13701923076923078
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.08814102564102563
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.06442307692307693
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.03798076923076923
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.13701923076923078
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.2644230769230769
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.32211538461538464
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.3798076923076923
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.2529381675019326
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.21289396367521363
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.2208612925846397
            name: Cosine Map@100

all-MiniLM-L6-v2-klej-dyk-v0.1

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2. It maps sentences & paragraphs to a 384-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: sentence-transformers/all-MiniLM-L6-v2
• Maximum Sequence Length: 256 tokens
• Output Dimensionality: 384 tokens
• Similarity Function: Cosine Similarity
• Language: en
• License: apache-2.0

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': 256, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, '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 = [
    'Sen o zastrzyku Irmy',
    'gdzie Freud spotkał Irmę we śnie o zastrzyku Irmy?',
    'ile razy Srebrna Biblia była przywożona do Szwecji?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Information Retrieval

• Dataset: dim_384
• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.1995
cosine_accuracy@3	0.4303
cosine_accuracy@5	0.5385
cosine_accuracy@10	0.6226
cosine_precision@1	0.1995
cosine_precision@3	0.1434
cosine_precision@5	0.1077
cosine_precision@10	0.0623
cosine_recall@1	0.1995
cosine_recall@3	0.4303
cosine_recall@5	0.5385
cosine_recall@10	0.6226
cosine_ndcg@10	0.4068
cosine_mrr@10	0.3377
cosine_map@100	0.3452

Information Retrieval

• Dataset: dim_256
• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.1851
cosine_accuracy@3	0.4135
cosine_accuracy@5	0.5096
cosine_accuracy@10	0.6034
cosine_precision@1	0.1851
cosine_precision@3	0.1378
cosine_precision@5	0.1019
cosine_precision@10	0.0603
cosine_recall@1	0.1851
cosine_recall@3	0.4135
cosine_recall@5	0.5096
cosine_recall@10	0.6034
cosine_ndcg@10	0.3911
cosine_mrr@10	0.3234
cosine_map@100	0.3304

Information Retrieval

• Dataset: dim_128
• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.1803
cosine_accuracy@3	0.3534
cosine_accuracy@5	0.4423
cosine_accuracy@10	0.5192
cosine_precision@1	0.1803
cosine_precision@3	0.1178
cosine_precision@5	0.0885
cosine_precision@10	0.0519
cosine_recall@1	0.1803
cosine_recall@3	0.3534
cosine_recall@5	0.4423
cosine_recall@10	0.5192
cosine_ndcg@10	0.3443
cosine_mrr@10	0.2889
cosine_map@100	0.296

Information Retrieval

• Dataset: dim_64
• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.137
cosine_accuracy@3	0.2644
cosine_accuracy@5	0.3221
cosine_accuracy@10	0.3798
cosine_precision@1	0.137
cosine_precision@3	0.0881
cosine_precision@5	0.0644
cosine_precision@10	0.038
cosine_recall@1	0.137
cosine_recall@3	0.2644
cosine_recall@5	0.3221
cosine_recall@10	0.3798
cosine_ndcg@10	0.2529
cosine_mrr@10	0.2129
cosine_map@100	0.2209

Training Details

Training Dataset

Unnamed Dataset

• Size: 3,738 training samples
• Columns: positive and anchor
• Approximate statistics based on the first 1000 samples:

  	positive	anchor
  type	string	string
  details	min: 7 tokens mean: 87.54 tokens max: 256 tokens	min: 9 tokens mean: 30.98 tokens max: 76 tokens

• Samples:

  positive	anchor
  Zespół Blaua (zespół Jabsa, ang. Blau syndrome, BS) – rzadka choroba genetyczna o dziedziczeniu autosomalnym dominującym, charakteryzująca się ziarniniakowym zapaleniem stawów o wczesnym początku, zapaleniem jagodówki (uveitis) i wysypką skórną, a także kamptodaktylią.	jakie choroby genetyczne dziedziczą się autosomalnie dominująco?
  Gorgippia Gorgippia – starożytne miasto bosporańskie nad Morzem Czarnym, którego pozostałości znajdują się obecnie pod współczesną zabudową centralnej części miasta Anapa w Kraju Krasnodarskim w Rosji.	gdzie obecnie znajduje się starożytne miasto Gorgippia?
  Ulubionym dystansem Rücker było 400 metrów i to na nim notowała największe indywidualne sukcesy : srebrny medal Mistrzostw Europy juniorów w lekkoatletyce (Saloniki 1991) 6. miejsce w Pucharze Świata w Lekkoatletyce (Hawana 1992) 5. miejsce na Mistrzostwach Europy w Lekkoatletyce (Helsinki 1994) srebro podczas Mistrzostw Świata w Lekkoatletyce (Sewilla 1999) złota medalistka mistrzostw Niemiec Duże sukcesy odnosiła także w sztafecie 4 x 400 metrów : złoto Mistrzostw Europy juniorów w lekkoatletyce (Varaždin 1989) złoty medal Mistrzostw Europy juniorów w lekkoatletyce (Saloniki 1991) brąz na Mistrzostwach Europy w Lekkoatletyce (Helsinki 1994) brązowy medal podczas Igrzysk Olimpijskich (Atlanta 1996) brąz na Halowych Mistrzostwach Świata w Lekkoatletyce (Paryż 1997) złoto Mistrzostw Świata w Lekkoatletyce (Ateny 1997) brązowy medal Mistrzostw Świata w Lekkoatletyce (Sewilla 1999)	kto zaprojektował medale, które będą wręczane podczas tegorocznych mistrzostw Europy juniorów w lekkoatletyce?

• Loss: MatryoshkaLoss with these parameters:

  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          384,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: epoch
• per_device_train_batch_size: 32
• per_device_eval_batch_size: 32
• gradient_accumulation_steps: 32
• learning_rate: 2e-05
• num_train_epochs: 5
• lr_scheduler_type: cosine
• warmup_ratio: 0.1
• bf16: True
• tf32: True
• load_best_model_at_end: True
• optim: adamw_torch_fused
• 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: 32
• per_device_eval_batch_size: 32
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 32
• eval_accumulation_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
• use_ipex: False
• bf16: True
• fp16: False
• fp16_opt_level: O1
• half_precision_backend: auto
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: True
• 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}
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch_fused
• optim_args: None
• adafactor: False
• group_by_length: False
• length_column_name: length
• 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: False
• hub_always_push: False
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_inputs_for_metrics: False
• 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
• dispatch_batches: None
• split_batches: None
• include_tokens_per_second: False
• include_num_input_tokens_seen: False
• neftune_noise_alpha: None
• optim_target_modules: None
• batch_eval_metrics: False
• batch_sampler: no_duplicates
• multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	dim_128_cosine_map@100	dim_256_cosine_map@100	dim_384_cosine_map@100	dim_64_cosine_map@100
0	0	-	0.1945	0.2243	0.2302	0.1499
0.2735	1	8.2585	-	-	-	-
0.5470	2	8.4215	-	-	-	-
0.8205	3	7.899	0.2205	0.2510	0.2597	0.1677
1.0855	4	6.5734	-	-	-	-
1.3590	5	6.2406	-	-	-	-
1.6325	6	6.0949	-	-	-	-
1.9060	7	5.7149	0.2736	0.3061	0.3224	0.2124
2.1709	8	5.153	-	-	-	-
2.4444	9	5.3615	-	-	-	-
2.7179	10	5.3069	-	-	-	-
2.9915	11	5.1567	0.2914	0.3238	0.3402	0.2191
3.2564	12	4.6824	-	-	-	-
3.5299	13	5.1072	-	-	-	-
3.8034	14	5.1575	0.2967	0.3302	0.3443	0.2196
4.0684	15	4.5651	0.2960	0.3304	0.3452	0.2209

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.12.2
• Sentence Transformers: 3.0.0
• Transformers: 4.41.2
• PyTorch: 2.3.1
• Accelerate: 0.27.2
• Datasets: 2.19.1
• Tokenizers: 0.19.1

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}
}