SentenceTransformer based on klue/roberta-large
This is a sentence-transformers model finetuned from klue/roberta-large. It maps sentences & paragraphs to a 1024-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: klue/roberta-large
- Maximum Sequence Length: 256 tokens
- Output Dimensionality: 1024 tokens
- Similarity Function: Cosine Similarity
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: RobertaModel
(1): Pooling({'word_embedding_dimension': 1024, '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})
)
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 = [
'네, 네.',
'네!',
'남자들은 같은 팀이다.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Evaluation
Metrics
Semantic Similarity
- Dataset:
sts-dev
- Evaluated with
EmbeddingSimilarityEvaluator
Metric | Value |
---|---|
pearson_cosine | 0.8554 |
spearman_cosine | 0.8624 |
pearson_manhattan | 0.8462 |
spearman_manhattan | 0.8512 |
pearson_euclidean | 0.8464 |
spearman_euclidean | 0.8516 |
pearson_dot | 0.8353 |
spearman_dot | 0.8344 |
pearson_max | 0.8554 |
spearman_max | 0.8624 |
Training Details
Training Datasets
Unnamed Dataset
- Size: 568,640 training samples
- Columns:
sentence_0
,sentence_1
, andsentence_2
- Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1 sentence_2 type string string string details - min: 4 tokens
- mean: 18.97 tokens
- max: 156 tokens
- min: 4 tokens
- mean: 18.36 tokens
- max: 95 tokens
- min: 4 tokens
- mean: 14.33 tokens
- max: 46 tokens
- Samples:
sentence_0 sentence_1 sentence_2 악기를 연주하는 사람.
여자 옆에서 백파이프를 연주하는 잘 차려입은 남자
노숙자가 잔돈을 구걸한다.
셔츠에 이벤트 번호를 새긴 남자들은 길을 걸어간다.
멘스 셔츠에 숫자가 적혀 있다.
남자들이 길에서 자고 있다.
군인들은 기지에서 함께 어울린다.
한 무리의 군인들이 그늘을 입고 방에 함께 앉아 있었고, 벽에 있는 작은 틈으로 빛이 최고조에 달했다.
한 무리의 민간인들이 적의 공격으로부터 움츠러든다.
- Loss:
MatryoshkaLoss
with these parameters:{ "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 768, 256 ], "matryoshka_weights": [ 1, 1 ], "n_dims_per_step": -1 }
Unnamed Dataset
- Size: 5,749 training samples
- Columns:
sentence_0
,sentence_1
, andlabel
- Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1 label type string string float details - min: 4 tokens
- mean: 17.52 tokens
- max: 50 tokens
- min: 4 tokens
- mean: 17.49 tokens
- max: 64 tokens
- min: 0.0
- mean: 0.54
- max: 1.0
- Samples:
sentence_0 sentence_1 label 49명이 사망하고 148명이 이라크에서 폭력적인 공격으로 부상당했다.
이라크에서 2명이 사망하고 11명이 부상당했다.
0.27999999999999997
그것은 도덕적으로 강요하지 않는다.
그것은 법을 제정하고 있다.
0.22000000000000003
여자가 기관총을 쏘고 있다.
남자가 총을 쏘고 있다.
0.4
- Loss:
MatryoshkaLoss
with these parameters:{ "loss": "CosineSimilarityLoss", "matryoshka_dims": [ 768, 256 ], "matryoshka_weights": [ 1, 1 ], "n_dims_per_step": -1 }
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: stepsnum_train_epochs
: 5batch_sampler
: no_duplicatesmulti_dataset_batch_sampler
: round_robin
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: stepsprediction_loss_only
: Trueper_device_train_batch_size
: 8per_device_eval_batch_size
: 8per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonelearning_rate
: 5e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1num_train_epochs
: 5max_steps
: -1lr_scheduler_type
: linearlr_scheduler_kwargs
: {}warmup_ratio
: 0.0warmup_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
: Falsefp16
: Falsefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Nonelocal_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
: Falseignore_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_torchoptim_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
: round_robin
Training Logs
Epoch | Step | Training Loss | sts-dev_spearman_max |
---|---|---|---|
0.3477 | 500 | 0.7919 | - |
0.6954 | 1000 | 0.6243 | 0.8517 |
1.0007 | 1439 | - | 0.8469 |
1.0424 | 1500 | 0.5267 | - |
1.3901 | 2000 | 0.2332 | 0.8485 |
1.7378 | 2500 | 0.1397 | - |
2.0007 | 2878 | - | 0.8614 |
2.0848 | 3000 | 0.1072 | 0.8624 |
Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.0
- Transformers: 4.41.2
- PyTorch: 2.3.0+cu121
- Accelerate: 0.30.1
- Datasets: 2.19.2
- 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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Evaluation results
- Pearson Cosine on sts devself-reported0.855
- Spearman Cosine on sts devself-reported0.862
- Pearson Manhattan on sts devself-reported0.846
- Spearman Manhattan on sts devself-reported0.851
- Pearson Euclidean on sts devself-reported0.846
- Spearman Euclidean on sts devself-reported0.852
- Pearson Dot on sts devself-reported0.835
- Spearman Dot on sts devself-reported0.834
- Pearson Max on sts devself-reported0.855
- Spearman Max on sts devself-reported0.862