SentenceTransformer based on klue/roberta-small
This is a sentence-transformers model finetuned from klue/roberta-small. 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: klue/roberta-small
- Maximum Sequence Length: 256 tokens
- Output Dimensionality: 768 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': 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})
)
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 = [
'인간의 지적',
'인간 관찰',
'사람들이 안에 서 있다',
]
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
Semantic Similarity
- Dataset:
sts-dev
- Evaluated with
EmbeddingSimilarityEvaluator
Metric | Value |
---|---|
pearson_cosine | 0.8481 |
spearman_cosine | 0.847 |
pearson_manhattan | 0.8291 |
spearman_manhattan | 0.8329 |
pearson_euclidean | 0.8297 |
spearman_euclidean | 0.8336 |
pearson_dot | 0.7962 |
spearman_dot | 0.7997 |
pearson_max | 0.8481 |
spearman_max | 0.847 |
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: 19.02 tokens
- max: 156 tokens
- min: 4 tokens
- mean: 18.36 tokens
- max: 95 tokens
- min: 4 tokens
- mean: 14.31 tokens
- max: 35 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: 5 tokens
- mean: 17.15 tokens
- max: 71 tokens
- min: 4 tokens
- mean: 16.86 tokens
- max: 76 tokens
- min: 0.0
- mean: 0.54
- max: 1.0
- Samples:
sentence_0 sentence_1 label 남자가 기타를 치고 있다.
소뇌는 기타를 치고 있다.
0.72
고양이가 빨판을 핥고 있다.
한 여성이 오이를 자르고 있다.
0.0
누군가가 파워 드릴로 나무 조각에 구멍을 뚫는다.
한 남자가 나무 조각에 구멍을 뚫는다.
0.64
- 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 | 0.931 | - |
0.6954 | 1000 | 0.7062 | 0.8313 |
1.0007 | 1439 | - | 0.8379 |
1.0424 | 1500 | 0.5893 | - |
1.3901 | 2000 | 0.3406 | 0.8343 |
1.7378 | 2500 | 0.2514 | - |
2.0007 | 2878 | - | 0.8450 |
2.0848 | 3000 | 0.2252 | 0.8470 |
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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Model tree for djsull/sentence-roberta-small
Base model
klue/roberta-smallEvaluation results
- Pearson Cosine on sts devself-reported0.848
- Spearman Cosine on sts devself-reported0.847
- Pearson Manhattan on sts devself-reported0.829
- Spearman Manhattan on sts devself-reported0.833
- Pearson Euclidean on sts devself-reported0.830
- Spearman Euclidean on sts devself-reported0.834
- Pearson Dot on sts devself-reported0.796
- Spearman Dot on sts devself-reported0.800
- Pearson Max on sts devself-reported0.848
- Spearman Max on sts devself-reported0.847