README.md

---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:112
- loss:MultipleNegativesRankingLoss
base_model: google/embeddinggemma-300m
widget:
- source_sentence: 슈파인
  sentences:
  - park | 장비를 파킹(대기) 위치로 이동 또는 튜브를 맨위로 | 파킹
  - tubeToStandCenter | 튜브를 스탠드 센터를 향하도록 이동, 어브도민, 이렉트, 체스트, 홀스파인, 슈파인, abdomen, erect,
    chest, chest PA, Whole spine, supine | 튜브 스탠드 센터로
  - tubeToTableCenter | 튜브를 테이블 센터를 향하도록 이동 | 튜브 테이블 센터로
- source_sentence: 그만 정지 멈추지 그만
  sentences:
  - stopAction | 어느 위치에서든 장비 즉각 멈춤 | 멈춰
  - park | 장비를 파킹(대기) 위치로 이동 또는 튜브를 맨위로 | 파킹
  - tubeToTableCenter | 튜브를 테이블 센터를 향하도록 이동 | 튜브 테이블 센터로
- source_sentence: 이렉트
  sentences:
  - tubeToStandCenter | 튜브를 스탠드 센터를 향하도록 이동, 어브도민, 이렉트, 체스트, 홀스파인, 슈파인, abdomen, erect,
    chest, chest PA, Whole spine, supine | 튜브 스탠드 센터로
  - tubeToStandCenter | 튜브를 스탠드 센터를 향하도록 이동, 어브도민, 이렉트, 체스트, 홀스파인, 슈파인, abdomen, erect,
    chest, chest PA, Whole spine, supine | 튜브 스탠드 센터로
  - tubeToTableCenter | 튜브를 테이블 센터를 향하도록 이동 | 튜브 테이블 센터로
- source_sentence: 아니
  sentences:
  - tubeToTableCenter | 튜브를 테이블 센터를 향하도록 이동 | 튜브 테이블 센터로
  - responseNo | 부정의 응답 | 아니요, 노
  - park | 장비를 파킹(대기) 위치로 이동 또는 튜브를 맨위로 | 파킹
- source_sentence: 멈춰
  sentences:
  - tubeToTableCenter | 튜브를 테이블 센터를 향하도록 이동 | 튜브 테이블 센터로
  - tubeToStandCenter | 튜브를 스탠드 센터를 향하도록 이동, 어브도민, 이렉트, 체스트, 홀스파인, 슈파인, abdomen, erect,
    chest, chest PA, Whole spine, supine | 튜브 스탠드 센터로
  - stopAction | 어느 위치에서든 장비 즉각 멈춤 | 멈춰
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---

# SentenceTransformer based on google/embeddinggemma-300m

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m). 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:** [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m) <!-- at revision 57c266a740f537b4dc058e1b0cda161fd15afa75 -->
- **Maximum Sequence Length:** 2048 tokens
- **Output Dimensionality:** 768 dimensions
- **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/huggingface/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': 2048, 'do_lower_case': False, 'architecture': 'Gemma3TextModel'})
  (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})
  (2): Dense({'in_features': 768, 'out_features': 3072, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
  (3): Dense({'in_features': 3072, 'out_features': 768, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
  (4): Normalize()
)
```

## 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
queries = [
    "\uba48\ucdb0",
]
documents = [
    'stopAction | 어느 위치에서든 장비 즉각 멈춤 | 멈춰',
    'tubeToTableCenter | 튜브를 테이블 센터를 향하도록 이동 | 튜브 테이블 센터로',
    'tubeToStandCenter | 튜브를 스탠드 센터를 향하도록 이동, 어브도민, 이렉트, 체스트, 홀스파인, 슈파인, abdomen, erect, chest, chest PA, Whole spine, supine | 튜브 스탠드 센터로',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 768] [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[0.6204, 0.0847, 0.1969]])
```

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You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

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## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 112 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 112 samples:
  |         | sentence_0                                                                       | sentence_1                                                                         |
  |:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                             |
  | details | <ul><li>min: 3 tokens</li><li>mean: 7.67 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 37.14 tokens</li><li>max: 62 tokens</li></ul> |
* Samples:
  | sentence_0                        | sentence_1                                                                                                                                           |
  |:----------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>체스트 PA</code>               | <code>tubeToStandCenter \| 튜브를 스탠드 센터를 향하도록 이동, 어브도민, 이렉트, 체스트, 홀스파인, 슈파인, abdomen, erect, chest, chest PA, Whole spine, supine \| 튜브 스탠드 센터로</code> |
  | <code>튜브 스탠드 백색 센치로 센터 맞춰줘</code> | <code>tubeToStandCenter \| 튜브를 스탠드 센터를 향하도록 이동, 어브도민, 이렉트, 체스트, 홀스파인, 슈파인, abdomen, erect, chest, chest PA, Whole spine, supine \| 튜브 스탠드 센터로</code> |
  | <code>튜브</code>                   | <code>tubeToTableCenter \| 튜브를 테이블 센터를 향하도록 이동 \| 튜브 테이블 센터로</code>                                                                                  |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false
  }
  ```

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

- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 1
- `multi_dataset_batch_sampler`: round_robin

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

- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_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`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: None
- `warmup_ratio`: None
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `enable_jit_checkpoint`: False
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `use_cpu`: False
- `seed`: 42
- `data_seed`: None
- `bf16`: False
- `fp16`: False
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: -1
- `ddp_backend`: None
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `parallelism_config`: None
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `group_by_length`: False
- `length_column_name`: length
- `project`: huggingface
- `trackio_space_id`: trackio
- `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
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `hub_revision`: None
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `auto_find_batch_size`: False
- `full_determinism`: False
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_num_input_tokens_seen`: no
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: True
- `use_cache`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
- `router_mapping`: {}
- `learning_rate_mapping`: {}

</details>

### Framework Versions
- Python: 3.11.6
- Sentence Transformers: 5.2.2
- Transformers: 5.0.0
- PyTorch: 2.7.1+cpu
- Accelerate: 1.12.0
- Datasets: 4.5.0
- Tokenizers: 0.22.2

## 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",
}
```

#### 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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