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
andanchor
- 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
: epochper_device_train_batch_size
: 32per_device_eval_batch_size
: 32gradient_accumulation_steps
: 32learning_rate
: 2e-05num_train_epochs
: 5lr_scheduler_type
: cosinewarmup_ratio
: 0.1bf16
: Truetf32
: Trueload_best_model_at_end
: Trueoptim
: adamw_torch_fusedbatch_sampler
: no_duplicates
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: epochprediction_loss_only
: Trueper_device_train_batch_size
: 32per_device_eval_batch_size
: 32per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 32eval_accumulation_steps
: Nonelearning_rate
: 2e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 5max_steps
: -1lr_scheduler_type
: cosinelr_scheduler_kwargs
: {}warmup_ratio
: 0.1warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Truefp16
: Falsefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Truelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Trueignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torch_fusedoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Falsehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseeval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falsebatch_sampler
: no_duplicatesmulti_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}
}
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Base model
sentence-transformers/all-MiniLM-L6-v2Evaluation results
- Cosine Accuracy@1 on dim 384self-reported0.200
- Cosine Accuracy@3 on dim 384self-reported0.430
- Cosine Accuracy@5 on dim 384self-reported0.538
- Cosine Accuracy@10 on dim 384self-reported0.623
- Cosine Precision@1 on dim 384self-reported0.200
- Cosine Precision@3 on dim 384self-reported0.143
- Cosine Precision@5 on dim 384self-reported0.108
- Cosine Precision@10 on dim 384self-reported0.062
- Cosine Recall@1 on dim 384self-reported0.200
- Cosine Recall@3 on dim 384self-reported0.430