privacy_embedding_rag_10k_base_checkpoint_2-klej-dyk-v0.1
This is a sentence-transformers model finetuned from liddlefish/privacy_embedding_rag_10k_base_checkpoint_2. 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: liddlefish/privacy_embedding_rag_10k_base_checkpoint_2
- Maximum Sequence Length: 512 tokens
- Output Dimensionality: 768 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': 512, 'do_lower_case': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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?',
'dlaczego Ōkunoshima została wymazana z map Japonii?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Evaluation
Metrics
Information Retrieval
- Dataset:
dim_768
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.1875 |
cosine_accuracy@3 | 0.4543 |
cosine_accuracy@5 | 0.6226 |
cosine_accuracy@10 | 0.7308 |
cosine_precision@1 | 0.1875 |
cosine_precision@3 | 0.1514 |
cosine_precision@5 | 0.1245 |
cosine_precision@10 | 0.0731 |
cosine_recall@1 | 0.1875 |
cosine_recall@3 | 0.4543 |
cosine_recall@5 | 0.6226 |
cosine_recall@10 | 0.7308 |
cosine_ndcg@10 | 0.4453 |
cosine_mrr@10 | 0.355 |
cosine_map@100 | 0.3624 |
Information Retrieval
- Dataset:
dim_512
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.1827 |
cosine_accuracy@3 | 0.4447 |
cosine_accuracy@5 | 0.6034 |
cosine_accuracy@10 | 0.7115 |
cosine_precision@1 | 0.1827 |
cosine_precision@3 | 0.1482 |
cosine_precision@5 | 0.1207 |
cosine_precision@10 | 0.0712 |
cosine_recall@1 | 0.1827 |
cosine_recall@3 | 0.4447 |
cosine_recall@5 | 0.6034 |
cosine_recall@10 | 0.7115 |
cosine_ndcg@10 | 0.4349 |
cosine_mrr@10 | 0.3472 |
cosine_map@100 | 0.3548 |
Information Retrieval
- Dataset:
dim_256
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.1875 |
cosine_accuracy@3 | 0.4231 |
cosine_accuracy@5 | 0.5577 |
cosine_accuracy@10 | 0.6683 |
cosine_precision@1 | 0.1875 |
cosine_precision@3 | 0.141 |
cosine_precision@5 | 0.1115 |
cosine_precision@10 | 0.0668 |
cosine_recall@1 | 0.1875 |
cosine_recall@3 | 0.4231 |
cosine_recall@5 | 0.5577 |
cosine_recall@10 | 0.6683 |
cosine_ndcg@10 | 0.414 |
cosine_mrr@10 | 0.3337 |
cosine_map@100 | 0.3427 |
Information Retrieval
- Dataset:
dim_128
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.1707 |
cosine_accuracy@3 | 0.3678 |
cosine_accuracy@5 | 0.512 |
cosine_accuracy@10 | 0.601 |
cosine_precision@1 | 0.1707 |
cosine_precision@3 | 0.1226 |
cosine_precision@5 | 0.1024 |
cosine_precision@10 | 0.0601 |
cosine_recall@1 | 0.1707 |
cosine_recall@3 | 0.3678 |
cosine_recall@5 | 0.512 |
cosine_recall@10 | 0.601 |
cosine_ndcg@10 | 0.3712 |
cosine_mrr@10 | 0.2988 |
cosine_map@100 | 0.3067 |
Information Retrieval
- Dataset:
dim_64
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.1587 |
cosine_accuracy@3 | 0.3101 |
cosine_accuracy@5 | 0.387 |
cosine_accuracy@10 | 0.4928 |
cosine_precision@1 | 0.1587 |
cosine_precision@3 | 0.1034 |
cosine_precision@5 | 0.0774 |
cosine_precision@10 | 0.0493 |
cosine_recall@1 | 0.1587 |
cosine_recall@3 | 0.3101 |
cosine_recall@5 | 0.387 |
cosine_recall@10 | 0.4928 |
cosine_ndcg@10 | 0.3131 |
cosine_mrr@10 | 0.2569 |
cosine_map@100 | 0.2651 |
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: 89.43 tokens
- max: 507 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": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 }
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: epochper_device_train_batch_size
: 16per_device_eval_batch_size
: 16gradient_accumulation_steps
: 16learning_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
: 16per_device_eval_batch_size
: 16per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 16eval_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_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 |
---|---|---|---|---|---|---|---|
0.0684 | 1 | 9.112 | - | - | - | - | - |
0.1368 | 2 | 9.5133 | - | - | - | - | - |
0.2051 | 3 | 9.0946 | - | - | - | - | - |
0.2735 | 4 | 8.9744 | - | - | - | - | - |
0.3419 | 5 | 7.9039 | - | - | - | - | - |
0.4103 | 6 | 8.1973 | - | - | - | - | - |
0.4786 | 7 | 6.8979 | - | - | - | - | - |
0.5470 | 8 | 7.0324 | - | - | - | - | - |
0.6154 | 9 | 6.6472 | - | - | - | - | - |
0.6838 | 10 | 6.3009 | - | - | - | - | - |
0.7521 | 11 | 6.8778 | - | - | - | - | - |
0.8205 | 12 | 5.9809 | - | - | - | - | - |
0.8889 | 13 | 5.3054 | - | - | - | - | - |
0.9573 | 14 | 5.706 | 0.2868 | 0.3280 | 0.3522 | 0.2415 | 0.3477 |
1.0256 | 15 | 5.0592 | - | - | - | - | - |
1.0940 | 16 | 4.7655 | - | - | - | - | - |
1.1624 | 17 | 4.9682 | - | - | - | - | - |
1.2308 | 18 | 5.1226 | - | - | - | - | - |
1.2991 | 19 | 4.8655 | - | - | - | - | - |
1.3675 | 20 | 4.2008 | - | - | - | - | - |
1.4359 | 21 | 5.0281 | - | - | - | - | - |
1.5043 | 22 | 4.3074 | - | - | - | - | - |
1.5726 | 23 | 4.3163 | - | - | - | - | - |
1.6410 | 24 | 3.9344 | - | - | - | - | - |
1.7094 | 25 | 4.6567 | - | - | - | - | - |
1.7778 | 26 | 4.5145 | - | - | - | - | - |
1.8462 | 27 | 4.1319 | - | - | - | - | - |
1.9145 | 28 | 3.8768 | - | - | - | - | - |
1.9829 | 29 | 3.5525 | 0.2986 | 0.3330 | 0.3483 | 0.2590 | 0.3534 |
2.0513 | 30 | 3.8693 | - | - | - | - | - |
2.1197 | 31 | 3.4675 | - | - | - | - | - |
2.1880 | 32 | 4.0598 | - | - | - | - | - |
2.2564 | 33 | 4.2429 | - | - | - | - | - |
2.3248 | 34 | 3.3686 | - | - | - | - | - |
2.3932 | 35 | 3.2663 | - | - | - | - | - |
2.4615 | 36 | 3.8585 | - | - | - | - | - |
2.5299 | 37 | 3.1157 | - | - | - | - | - |
2.5983 | 38 | 3.5254 | - | - | - | - | - |
2.6667 | 39 | 3.2782 | - | - | - | - | - |
2.7350 | 40 | 4.3151 | - | - | - | - | - |
2.8034 | 41 | 3.4567 | - | - | - | - | - |
2.8718 | 42 | 3.3976 | - | - | - | - | - |
2.9402 | 43 | 3.3945 | 0.3014 | 0.3343 | 0.3522 | 0.2626 | 0.3593 |
3.0085 | 44 | 3.4487 | - | - | - | - | - |
3.0769 | 45 | 3.0021 | - | - | - | - | - |
3.1453 | 46 | 3.2332 | - | - | - | - | - |
3.2137 | 47 | 3.3012 | - | - | - | - | - |
3.2821 | 48 | 3.2735 | - | - | - | - | - |
3.3504 | 49 | 2.5335 | - | - | - | - | - |
3.4188 | 50 | 3.7025 | - | - | - | - | - |
3.4872 | 51 | 2.8596 | - | - | - | - | - |
3.5556 | 52 | 3.1108 | - | - | - | - | - |
3.6239 | 53 | 3.2807 | - | - | - | - | - |
3.6923 | 54 | 3.1604 | - | - | - | - | - |
3.7607 | 55 | 3.7179 | - | - | - | - | - |
3.8291 | 56 | 3.3418 | - | - | - | - | - |
3.8974 | 57 | 2.9735 | - | - | - | - | - |
3.9658 | 58 | 3.2755 | 0.3066 | 0.3409 | 0.3546 | 0.2653 | 0.3626 |
4.0342 | 59 | 3.1444 | - | - | - | - | - |
4.1026 | 60 | 3.0212 | - | - | - | - | - |
4.1709 | 61 | 3.1298 | - | - | - | - | - |
4.2393 | 62 | 3.3195 | - | - | - | - | - |
4.3077 | 63 | 2.996 | - | - | - | - | - |
4.3761 | 64 | 2.4636 | - | - | - | - | - |
4.4444 | 65 | 3.2388 | - | - | - | - | - |
4.5128 | 66 | 2.747 | - | - | - | - | - |
4.5812 | 67 | 2.8715 | - | - | - | - | - |
4.6496 | 68 | 3.1402 | - | - | - | - | - |
4.7179 | 69 | 3.547 | - | - | - | - | - |
4.7863 | 70 | 3.6094 | 0.3067 | 0.3427 | 0.3548 | 0.2651 | 0.3624 |
- 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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Model tree for ve88ifz2/privacy_embedding_rag_10k_base_checkpoint_2-klej-dyk-v0.1
Finetuned
this model
Evaluation results
- Cosine Accuracy@1 on dim 768self-reported0.188
- Cosine Accuracy@3 on dim 768self-reported0.454
- Cosine Accuracy@5 on dim 768self-reported0.623
- Cosine Accuracy@10 on dim 768self-reported0.731
- Cosine Precision@1 on dim 768self-reported0.188
- Cosine Precision@3 on dim 768self-reported0.151
- Cosine Precision@5 on dim 768self-reported0.125
- Cosine Precision@10 on dim 768self-reported0.073
- Cosine Recall@1 on dim 768self-reported0.188
- Cosine Recall@3 on dim 768self-reported0.454