README.md

---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:43318
- loss:MultipleNegativesRankingLoss
base_model: intfloat/multilingual-e5-large
widget:
- source_sentence: 'query: 3PL 사용 시의 비용 절감 메커니즘은 어떤 것이 있나요?'
  sentences:
  - 'passage: 3 Dimension-Through Silicon Via (Technical)'
  - 'passage: Third Party Logistics (상업)'
  - 'passage: Authorization Account Answer (Technical)'
- source_sentence: 'query: How can ACE be utilized?'
  sentences:
  - 'passage: Audio Connecting Equipment (Applicational)'
  - 'passage: Access Class-Barring (활용)'
  - 'passage: Abort Accept (기술)'
- source_sentence: 'query: What makes the 1x RTT technology significant?'
  sentences:
  - 'passage: Ab Wire Test (Conceptual)'
  - 'passage: CDMA2000 1x Radio Transmission Technology (Conceptual)'
  - 'passage: Authentication, Authorization, Accounting (기술)'
- source_sentence: 'query: 2WPD의 전력 분배 방식은 어떻게 이루어지나요?'
  sentences:
  - 'passage: Triple Digital Encryption Standard (기술)'
  - 'passage: Air Baffle (Conceptual)'
  - 'passage: 2 Way Power Divider (기술)'
- source_sentence: 'query: 3D-TSV가 반도체 설계에 미치는 영향은 무엇인가요?'
  sentences:
  - 'passage: Available Bit Rate (Applicational)'
  - 'passage: Average Bouncing Busy Hour (개념)'
  - 'passage: 3 Dimension-Through Silicon Via (기술)'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
model-index:
- name: SentenceTransformer based on intfloat/multilingual-e5-large
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: e5 eval real
      type: e5-eval-real
    metrics:
    - type: cosine_accuracy@1
      value: 0.9683
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9981
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9997
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9999
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.9683
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3326999999999999
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.19994000000000006
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09999000000000001
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.9683
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9981
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9997
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9999
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9873905751741222
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9830366666666664
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9830414285714285
      name: Cosine Map@100
---

# SentenceTransformer based on intfloat/multilingual-e5-large

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) on the train dataset. 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:** [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) <!-- at revision 0dc5580a448e4284468b8909bae50fa925907bc5 -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - train
<!-- - **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, 'architecture': '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})
  (2): 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
sentences = [
    'query: 3D-TSV가 반도체 설계에 미치는 영향은 무엇인가요?',
    'passage: 3 Dimension-Through Silicon Via (기술)',
    'passage: Available Bit Rate (Applicational)',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000,  0.8098, -0.1741],
#         [ 0.8098,  1.0000, -0.2449],
#         [-0.1741, -0.2449,  1.0000]])
```

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## Evaluation

### Metrics

#### Information Retrieval

* Dataset: `e5-eval-real`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.9683     |
| cosine_accuracy@3   | 0.9981     |
| cosine_accuracy@5   | 0.9997     |
| cosine_accuracy@10  | 0.9999     |
| cosine_precision@1  | 0.9683     |
| cosine_precision@3  | 0.3327     |
| cosine_precision@5  | 0.1999     |
| cosine_precision@10 | 0.1        |
| cosine_recall@1     | 0.9683     |
| cosine_recall@3     | 0.9981     |
| cosine_recall@5     | 0.9997     |
| cosine_recall@10    | 0.9999     |
| **cosine_ndcg@10**  | **0.9874** |
| cosine_mrr@10       | 0.983      |
| cosine_map@100      | 0.983      |

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## Training Details

### Training Dataset

#### train

* Dataset: train
* Size: 43,318 training samples
* Columns: <code>0</code> and <code>1</code>
* Approximate statistics based on the first 1000 samples:
  |         | 0                                                                                  | 1                                                                                 |
  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                            |
  | details | <ul><li>min: 11 tokens</li><li>mean: 17.56 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 14.13 tokens</li><li>max: 27 tokens</li></ul> |
* Samples:
  | 0                                                                                                     | 1                                                                   |
  |:------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------|
  | <code>query: ABPL은 ATM의 기초 속도를 지원하는 물리 계층 장치로 어떻게 구성되나요?</code>                                       | <code>passage: ATM Base Rate Physical Layer Unit (기술)</code>        |
  | <code>query: How is the ABPL configured as a physical layer unit supporting the ATM base rate?</code> | <code>passage: ATM Base Rate Physical Layer Unit (Technical)</code> |
  | <code>query: ABPL의 역할은 ATM 네트워크에서 무엇을 의미하나요?</code>                                                   | <code>passage: ATM Base Rate Physical Layer Unit (개념)</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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `learning_rate`: 1e-05
- `weight_decay`: 0.01
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `batch_sampler`: no_duplicates

#### 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`: 64
- `per_device_eval_batch_size`: 64
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 1e-05
- `weight_decay`: 0.01
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 3
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `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`: True
- `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}
- `parallelism_config`: None
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `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`: None
- `hub_always_push`: False
- `hub_revision`: None
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `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
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `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`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}

</details>

### Training Logs
| Epoch  | Step | Training Loss | e5-eval-real_cosine_ndcg@10 |
|:------:|:----:|:-------------:|:---------------------------:|
| 0.0015 | 1    | 2.8346        | -                           |
| 0.1477 | 100  | 1.1145        | -                           |
| 0.2954 | 200  | 0.0332        | 0.9633                      |
| 0.4431 | 300  | 0.0185        | -                           |
| 0.5908 | 400  | 0.0154        | 0.9782                      |
| 0.7386 | 500  | 0.0116        | -                           |
| 0.8863 | 600  | 0.0107        | 0.9810                      |
| 1.0340 | 700  | 0.0078        | -                           |
| 1.1817 | 800  | 0.0076        | 0.9830                      |
| 1.3294 | 900  | 0.0045        | -                           |
| 1.4771 | 1000 | 0.0043        | 0.9851                      |
| 1.6248 | 1100 | 0.0034        | -                           |
| 1.7725 | 1200 | 0.0037        | 0.9862                      |
| 1.9202 | 1300 | 0.0031        | -                           |
| 2.0679 | 1400 | 0.0034        | 0.9870                      |
| 2.2157 | 1500 | 0.0029        | -                           |
| 2.3634 | 1600 | 0.0025        | 0.9872                      |
| 2.5111 | 1700 | 0.0027        | -                           |
| 2.6588 | 1800 | 0.0022        | 0.9875                      |
| 2.8065 | 1900 | 0.0027        | -                           |
| 2.9542 | 2000 | 0.0025        | 0.9875                      |
| -1     | -1   | -             | 0.9874                      |


### Framework Versions
- Python: 3.12.11
- Sentence Transformers: 5.1.0
- Transformers: 4.56.1
- PyTorch: 2.8.0+cu126
- Accelerate: 1.10.1
- Datasets: 3.6.0
- Tokenizers: 0.22.0

## 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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