README.md

---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:76932
- loss:MultipleNegativesRankingLoss
base_model: intfloat/multilingual-e5-large
widget:
- source_sentence: 'query: ATM Adaptation Layer 2의 약어는 무엇인가요?'
  sentences:
  - 'passage: 2 Transmit 2 Receive (기술)'
  - 'passage: Alternating Current (개념)'
  - 'passage: AAL2 (기술)'
- source_sentence: 'query: AC의 접근 클래스 C0부터 C15까지의 기능은 무엇인가요?'
  sentences:
  - 'passage: Access Class (C0 to C15) (개념)'
  - 'passage: 3 Dimension-Through Silicon Via (기술)'
  - 'passage: ACAP (Conceptual)'
- source_sentence: 'query: What is the abbreviation for Alarm Agent Handling Block?'
  sentences:
  - 'passage: ATM Connection establishment/release Control Block (기술)'
  - 'passage: AAGHB (Technical)'
  - 'passage: Account Card Calling (활용)'
- source_sentence: 'query: ABPL의 ATM 기본 속도 물리 계층 장치는 어떻게 구성되어 있나요?'
  sentences:
  - 'passage: ATM Base Rate Physical Layer Unit (기술)'
  - 'passage: 3A (개념)'
  - 'passage: 5GTF (Conceptual)'
- source_sentence: 'query: How does the triple encryption process of 3-DES enhance
    security?'
  sentences:
  - 'passage: 5th Generation Technical Forum (Conceptual)'
  - 'passage: Triple Data Encryption Standard (Technical)'
  - 'passage: ABCDEF (활용)'
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: 0.8686666666666667
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.969
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9832
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9922
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.8686666666666667
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.323
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.19664000000000004
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09922000000000002
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.8686666666666667
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.969
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9832
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9922
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9376619313817377
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9193550000000039
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9197550584627825
      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: How does the triple encryption process of 3-DES enhance security?',
    'passage: Triple Data Encryption Standard (Technical)',
    'passage: ABCDEF (활용)',
]
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, 0.8389, 0.1546],
#         [0.8389, 1.0000, 0.0850],
#         [0.1546, 0.0850, 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   | 0.8687     |
| cosine_accuracy@3   | 0.969      |
| cosine_accuracy@5   | 0.9832     |
| cosine_accuracy@10  | 0.9922     |
| cosine_precision@1  | 0.8687     |
| cosine_precision@3  | 0.323      |
| cosine_precision@5  | 0.1966     |
| cosine_precision@10 | 0.0992     |
| cosine_recall@1     | 0.8687     |
| cosine_recall@3     | 0.969      |
| cosine_recall@5     | 0.9832     |
| cosine_recall@10    | 0.9922     |
| **cosine_ndcg@10**  | **0.9377** |
| cosine_mrr@10       | 0.9194     |
| cosine_map@100      | 0.9198     |

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### Recommendations

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## Training Details

### Training Dataset

#### train

* Dataset: train
* Size: 76,932 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: 19.44 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 12.28 tokens</li><li>max: 27 tokens</li></ul> |
* Samples:
  | 0                                                                   | 1                                                                 |
  |:--------------------------------------------------------------------|:------------------------------------------------------------------|
  | <code>query: 3D-TSV 기술의 구조는 어떻게 되어 있나요?</code>                      | <code>passage: 3 Dimension-Through Silicon Via (기술)</code>        |
  | <code>query: What is the structure of the 3D-TSV technology?</code> | <code>passage: 3 Dimension-Through Silicon Via (Technical)</code> |
  | <code>query: 3 Dimension-Through Silicon Via의 줄임말이 뭐죠?</code>       | <code>passage: 3D-TSV (기술)</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 |
|:------:|:----:|:-------------:|:---------------------------:|
| 0.0008 | 1    | 3.1575        | -                           |
| 0.0831 | 100  | 1.6593        | -                           |
| 0.1663 | 200  | 0.1298        | 0.8389                      |
| 0.2494 | 300  | 0.0848        | -                           |
| 0.3325 | 400  | 0.0716        | 0.8808                      |
| 0.4156 | 500  | 0.0504        | -                           |
| 0.4988 | 600  | 0.0421        | 0.9033                      |
| 0.5819 | 700  | 0.042         | -                           |
| 0.6650 | 800  | 0.0398        | 0.9095                      |
| 0.7481 | 900  | 0.0384        | -                           |
| 0.8313 | 1000 | 0.0383        | 0.9111                      |
| 0.9144 | 1100 | 0.0321        | -                           |
| 0.9975 | 1200 | 0.0317        | 0.9186                      |
| 1.0806 | 1300 | 0.0299        | -                           |
| 1.1638 | 1400 | 0.0302        | 0.9161                      |
| 1.2469 | 1500 | 0.025         | -                           |
| 1.3300 | 1600 | 0.0199        | 0.9261                      |
| 1.4131 | 1700 | 0.0179        | -                           |
| 1.4963 | 1800 | 0.0117        | 0.9305                      |
| 1.5794 | 1900 | 0.013         | -                           |
| 1.6625 | 2000 | 0.012         | 0.9308                      |
| 1.7456 | 2100 | 0.0137        | -                           |
| 1.8288 | 2200 | 0.0141        | 0.9309                      |
| 1.9119 | 2300 | 0.0127        | -                           |
| 1.9950 | 2400 | 0.0115        | 0.9332                      |
| 2.0781 | 2500 | 0.0114        | -                           |
| 2.1613 | 2600 | 0.011         | 0.9351                      |
| 2.2444 | 2700 | 0.0107        | -                           |
| 2.3275 | 2800 | 0.0087        | 0.9357                      |
| 2.4106 | 2900 | 0.0084        | -                           |
| 2.4938 | 3000 | 0.0059        | 0.9366                      |
| 2.5769 | 3100 | 0.0062        | -                           |
| 2.6600 | 3200 | 0.0071        | 0.9377                      |
| 2.7431 | 3300 | 0.0072        | -                           |
| 2.8263 | 3400 | 0.0079        | 0.9376                      |
| 2.9094 | 3500 | 0.0071        | -                           |
| 2.9925 | 3600 | 0.0068        | 0.9376                      |
| -1     | -1   | -             | 0.9377                      |


### 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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