sentence-transformers safetensors roberta sentence-similarity arxiv:1908.10084 arxiv:2205.13147 arxiv:1705.00652 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
pipeline_tag: sentence-similarity
widget:
- source_sentence: 백마를 이끄는 여자
  sentences:
  - 갈색 말을 타고 있는 여자
  - 남자와 여자가 키스하고 있다.
  - 남자가 칼로 물병을 썰고 있다
- source_sentence: 꽤 괜찮은데
  sentences:
  - 아주 좋아요.
  - 개가 옷을 입고 있다.
  - 아무도 무대에 서지 않는다.
- source_sentence: 지루하군요.
  sentences:
  - 힘드네요! 정말 힘드네요!
  - 여자는 아이를 돕는다.
  - 사람들이 손을 내밀고 있다
- source_sentence: 인간의 지위
  sentences:
  - 인간의 지위.
  - 그것은 비열하지 않다.
  - 아무도 해고당하지 않는다.
- source_sentence: 인간의 지적
  sentences:
  - 인간 관찰
  - 사람들이 안에 서 있다
  - 아무도 앉아 있지 않다
model-index:
- name: SentenceTransformer based on klue/roberta-small
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev
      type: sts-dev
    metrics:
    - type: pearson_cosine
      value: 0.848109514939322
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8469617889194193
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8290541524988974
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.832916353112548
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8296914939989355
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8335696459808043
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.7961861998493428
      name: Pearson Dot
    - type: spearman_dot
      value: 0.7996870460025013
      name: Spearman Dot
    - type: pearson_max
      value: 0.848109514939322
      name: Pearson Max
    - type: spearman_max
      value: 0.8469617889194193
      name: Spearman Max
---


## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### 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:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
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]
```

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## Evaluation

### Metrics

#### Semantic Similarity
* Dataset: `sts-dev`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.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** |

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## Training Details

### Training Datasets

#### Unnamed Dataset


* Size: 568,640 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence_0                                                                         | sentence_1                                                                        | sentence_2                                                                        |
  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                            | string                                                                            |
  | details | <ul><li>min: 4 tokens</li><li>mean: 19.02 tokens</li><li>max: 156 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 18.36 tokens</li><li>max: 95 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.31 tokens</li><li>max: 35 tokens</li></ul> |
* Samples:
  | sentence_0                                | sentence_1                                                             | sentence_2                                |
  |:------------------------------------------|:-----------------------------------------------------------------------|:------------------------------------------|
  | <code>악기를 연주하는 사람.</code>                 | <code>여자 옆에서 백파이프를 연주하는 잘 차려입은 남자</code>                               | <code>노숙자가 잔돈을 구걸한다.</code>               |
  | <code>셔츠에 이벤트 번호를 새긴 남자들은 길을 걸어간다.</code> | <code>멘스 셔츠에 숫자가 적혀 있다.</code>                                         | <code>남자들이 길에서 자고 있다.</code>              |
  | <code>군인들은 기지에서 함께 어울린다.</code>           | <code>한 무리의 군인들이 그늘을 입고 방에 함께 앉아 있었고, 벽에 있는 작은 틈으로 빛이 최고조에 달했다.</code> | <code>한 무리의 민간인들이 적의 공격으로부터 움츠러든다.</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768,
          256
      ],
      "matryoshka_weights": [
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

#### Unnamed Dataset


* Size: 5,749 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence_0                                                                        | sentence_1                                                                        | label                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | float                                                          |
  | details | <ul><li>min: 5 tokens</li><li>mean: 17.15 tokens</li><li>max: 71 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 16.86 tokens</li><li>max: 76 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.54</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence_0                               | sentence_1                         | label             |
  |:-----------------------------------------|:-----------------------------------|:------------------|
  | <code>남자가 기타를 치고 있다.</code>              | <code>소뇌는 기타를 치고 있다.</code>        | <code>0.72</code> |
  | <code>고양이가 빨판을 핥고 있다.</code>             | <code>한 여성이 오이를 자르고 있다.</code>     | <code>0.0</code>  |
  | <code>누군가가 파워 드릴로 나무 조각에 구멍을 뚫는다.</code> | <code>한 남자가 나무 조각에 구멍을 뚫는다.</code> | <code>0.64</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "CosineSimilarityLoss",
      "matryoshka_dims": [
          768,
          256
      ],
      "matryoshka_weights": [
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: steps
- `num_train_epochs`: 5
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: round_robin

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 8
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 5
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `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`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `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`: False
- `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
- `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`: round_robin

</details>

### 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
```bibtex
@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
```bibtex
@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
```bibtex
@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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