README.md

---
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:10501
- loss:CosineSimilarityLoss
base_model: BAAI/bge-m3
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
widget:
- source_sentence: 숙소가 기대했던 것 이상으로 좋았습니다.
  sentences:
  - 숙소가 생각보다 좋았어요.
  - 어떻게 해야 환풍기를 작동시킬 수 있어?
  - 우리 집 바로 옆에 슈퍼마켓이 있는데, 무엇보다도 조용해요.
- source_sentence: 위치, 청결 상태, 주변 편의시설 모든게 좋았어요.
  sentences:
  - 집주인이 있기에 이 나라에서 잊을 수 없는 추억을 남겼습니다.
  - 두 개 방 모두에서 누우면 에펠탑이 보입니다!
  - 위치와 청결도 편의시설 크기 등등 모든게 좋습니다.
- source_sentence: 인내심을 가지고 결실을 맺는다는 자세가 필요합니다.
  sentences:
  - 같은 날, 바이오 산업은 정부에게 바이오 전문가 공급 시설, 새로운 시장 창출을 위한 규제 완화, 세금과 같은 인센티브 확대 등을 제안했습니다.
  - 그런 점에서 매우 힘든 기간을 보내고 계십니다.
  - 접속 가능한 계정 네이트나 네이버 메일 중 하나만 알려줘
- source_sentence: 비가 올지 맑을지 오늘 날씨를 찾아봐줄래?
  sentences:
  - 이번 태풍 진행 방향은?
  - 제가 지메일을 몇 번 가입했는지 알려주실 수 있나요?
  - 할부지 덕분에 산타모니카에 있는 내내 행복했어요.
- source_sentence: 티비 켜고 싶은데 말로 어떻게 명령해야하는 지 알려줘
  sentences:
  - 가습기 어떻게 써?
  - 친절한 설명으로 많은 도움이 되었습니다.
  - 에어컨과 뜨거운 물 모두 좋았습니다.
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on BAAI/bge-m3
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: pearson_cosine
      value: 0.9599773741282561
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.9215829115320294
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.9448530221078223
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.9182945172058137
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.9451692315193281
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.9184981231098932
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.9576506770371606
      name: Pearson Dot
    - type: spearman_dot
      value: 0.9159848293826075
      name: Spearman Dot
    - type: pearson_max
      value: 0.9599773741282561
      name: Pearson Max
    - type: spearman_max
      value: 0.9215829115320294
      name: Spearman Max
---

# SentenceTransformer based on BAAI/bge-m3

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3). 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:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) <!-- at revision 5617a9f61b028005a4858fdac845db406aefb181 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 1024 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': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (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:

```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("jeonseonjin/embedding_BAAI-bge-m3")
# Run inference
sentences = [
    '티비 켜고 싶은데 말로 어떻게 명령해야하는 지 알려줘',
    '가습기 어떻게 써?',
    '친절한 설명으로 많은 도움이 되었습니다.',
]
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]
```

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### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

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

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## Evaluation

### Metrics

#### Semantic Similarity

* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric             | Value      |
|:-------------------|:-----------|
| pearson_cosine     | 0.96       |
| spearman_cosine    | 0.9216     |
| pearson_manhattan  | 0.9449     |
| spearman_manhattan | 0.9183     |
| pearson_euclidean  | 0.9452     |
| spearman_euclidean | 0.9185     |
| pearson_dot        | 0.9577     |
| spearman_dot       | 0.916      |
| pearson_max        | 0.96       |
| **spearman_max**   | **0.9216** |

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 10,501 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: 7 tokens</li><li>mean: 21.15 tokens</li><li>max: 97 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 20.2 tokens</li><li>max: 61 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.44</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence_0                             | sentence_1                                          | label             |
  |:---------------------------------------|:----------------------------------------------------|:------------------|
  | <code>공원에서 열리는 시장도 구경할 수 있었어요.</code>  | <code>공원에서 시장을 볼 수 있었어요.</code>                     | <code>0.74</code> |
  | <code>베네치아에서 2박 3일 일정으로 머물렀습니다.</code> | <code>저는 2박 3일 동안 베니스에 머물렀습니다.</code>               | <code>0.74</code> |
  | <code>메일로 홍보하는 학회 리스트 불러줘</code>       | <code>보낸메일함의 메일은 주기적으로 백업하세요. 간헐적으로 하면 안됩니다.</code> | <code>0.12</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
  ```json
  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  ```

### 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>

- `overwrite_output_dir`: False
- `do_predict`: False
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `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`: 1
- `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
- `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, '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_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin

</details>

### Training Logs
| Epoch  | Step | Training Loss | spearman_max |
|:------:|:----:|:-------------:|:------------:|
| 0      | 0    | -             | 0.9196       |
| 0.7610 | 500  | 0.024         | -            |
| 1.0    | 657  | -             | 0.9216       |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.1.1
- Transformers: 4.40.1
- PyTorch: 2.4.1+cu121
- Accelerate: 0.34.2
- Datasets: 2.19.0
- 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",
}
```

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