README.md

---
base_model: intfloat/multilingual-e5-small
language:
- multilingual
library_name: sentence-transformers
license: apache-2.0
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
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:2320
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: 'MVGO; medium vacuum

    gas oil'
  sentences:
  - 과분해
  - Medium Vacuum Gas Oil(MVGO) ;
  - '선적 전 또는 양하 후에 화물창에 잔존하는 소량의 액체화물 양을 결정하는 수학

    적인 계산 수식'
- source_sentence: PLE; plain large end
  sentences:
  - Plain Large End ;
  - '부하중 변압기 Tap 변환기 ;

    변압기 권선의 Tap을 무정전으로 변경하는 장치'
  - Cone Roof Tank에서 Tank내의 Vapor가 외부로 나갈 수 있도록 만들어 놓은 구멍
- source_sentence: Fluidization
  sentences:
  - '핵심성과지표;

    어떤 계획이나 목표가 성공하였는지 또는 성공하고 있는지를 확인하려면 그 성공

    을 구성하는 요소들을 측정하는 지표를 찾아 측정하여야 하는데, 이들 지표 중 성

    공을 확인할 수 있는 가장 결정적인 지표를 KPI라고 부릅니다.'
  - '전압변동에 영향을 주는 무효전력을 줄이기 위한 조상설비의 일종으로 정지형 무

    효전력 보상장치'
  - 고체층을 액체나 기체로 확대시키거나 현탁시켜 유통하도록 하는 것
- source_sentence: 'SH; surface hardened

    steel body'
  sentences:
  - Surface Hardened Steel Body ;
  - 분산제 ; 슬러지 생성을 방지하기 위하여 Oil에 넣어주는 약품
  - '작업위험성평가;

    현장에서 수행되는 작업을 포함한 전반적인 직무 활동에 대하여 위험요인을 분석

    하여 현재 안전조치를 검토하고 안전대책을 마련하는 기법'
- source_sentence: U-205200
  sentences:
  - 물속의 (-)ion을 OH-로 치환해 주는 이온교환수지탑
  - 차단기, 스위치류 , 스위치
  - 올레핀 송유/동력 Nitrogen Section
model-index:
- name: Multilingual base soil embedding model (quantized)
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.2441860465116279
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.31007751937984496
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.3643410852713178
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.4108527131782946
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.2441860465116279
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.10335917312661498
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.07286821705426358
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.041085271317829464
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.2441860465116279
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.31007751937984496
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.3643410852713178
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.4108527131782946
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.3172493867293268
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.28840746893072483
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.3003133446683658
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.2054263565891473
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.28294573643410853
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.3178294573643411
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.38372093023255816
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.2054263565891473
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.09431524547803617
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06356589147286822
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.03837209302325582
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.2054263565891473
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.28294573643410853
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.3178294573643411
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.38372093023255816
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.2850988708112555
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.25465270087363123
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.26532412971784447
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.1937984496124031
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.2713178294573643
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.29844961240310075
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.3488372093023256
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.1937984496124031
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.0904392764857881
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.059689922480620154
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.03488372093023256
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.1937984496124031
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.2713178294573643
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.29844961240310075
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.3488372093023256
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.26467320016495083
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.2385474344776671
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.2482312240959752
      name: Cosine Map@100
---

# Multilingual base soil embedding model (quantized)

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small). It maps sentences & paragraphs to a 384-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-small](https://huggingface.co/intfloat/multilingual-e5-small) <!-- at revision fd1525a9fd15316a2d503bf26ab031a61d056e98 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **Language:** multilingual
- **License:** apache-2.0

### 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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, '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("ValentinaKim/Multilingual-base-soil-embedding")
# Run inference
sentences = [
    'U-205200',
    '올레핀 송유/동력 Nitrogen Section',
    '차단기, 스위치류 , 스위치',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

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### Direct Usage (Transformers)

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</details>
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### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

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

</details>
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### Out-of-Scope Use

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## Evaluation

### Metrics

#### Information Retrieval
* Dataset: `dim_256`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.2442     |
| cosine_accuracy@3   | 0.3101     |
| cosine_accuracy@5   | 0.3643     |
| cosine_accuracy@10  | 0.4109     |
| cosine_precision@1  | 0.2442     |
| cosine_precision@3  | 0.1034     |
| cosine_precision@5  | 0.0729     |
| cosine_precision@10 | 0.0411     |
| cosine_recall@1     | 0.2442     |
| cosine_recall@3     | 0.3101     |
| cosine_recall@5     | 0.3643     |
| cosine_recall@10    | 0.4109     |
| cosine_ndcg@10      | 0.3172     |
| cosine_mrr@10       | 0.2884     |
| **cosine_map@100**  | **0.3003** |

#### Information Retrieval
* Dataset: `dim_128`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.2054     |
| cosine_accuracy@3   | 0.2829     |
| cosine_accuracy@5   | 0.3178     |
| cosine_accuracy@10  | 0.3837     |
| cosine_precision@1  | 0.2054     |
| cosine_precision@3  | 0.0943     |
| cosine_precision@5  | 0.0636     |
| cosine_precision@10 | 0.0384     |
| cosine_recall@1     | 0.2054     |
| cosine_recall@3     | 0.2829     |
| cosine_recall@5     | 0.3178     |
| cosine_recall@10    | 0.3837     |
| cosine_ndcg@10      | 0.2851     |
| cosine_mrr@10       | 0.2547     |
| **cosine_map@100**  | **0.2653** |

#### Information Retrieval
* Dataset: `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.1938     |
| cosine_accuracy@3   | 0.2713     |
| cosine_accuracy@5   | 0.2984     |
| cosine_accuracy@10  | 0.3488     |
| cosine_precision@1  | 0.1938     |
| cosine_precision@3  | 0.0904     |
| cosine_precision@5  | 0.0597     |
| cosine_precision@10 | 0.0349     |
| cosine_recall@1     | 0.1938     |
| cosine_recall@3     | 0.2713     |
| cosine_recall@5     | 0.2984     |
| cosine_recall@10    | 0.3488     |
| cosine_ndcg@10      | 0.2647     |
| cosine_mrr@10       | 0.2385     |
| **cosine_map@100**  | **0.2482** |

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 2,320 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                           | positive                                                                           |
  |:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                             |
  | details | <ul><li>min: 3 tokens</li><li>mean: 6.72 tokens</li><li>max: 16 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 35.77 tokens</li><li>max: 408 tokens</li></ul> |
* Samples:
  | anchor                                            | positive                                                                                                            |
  |:--------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------|
  | <code>Deionizer</code>                            | <code>탈이온장치 ; Demineralizer와 동일</code>                                                                              |
  | <code>Sub-CC; sub-contracting<br>committee</code> | <code>외주 계약의 투명성과 공정성을 확보하기 위한 Sub-계약위원회로서 위원 및 위원<br>장은 CEO가 임명한다. CC이원원 부문장 이상 임원으로 하고 간사는 구매관리팀<br>장이 한다.</code> |
  | <code>In-line Sampler</code>                      | <code>원유 속의 물과 침전물의 함량을 측정하기 위하여 원유하역 Line에 설치해 놓은<br>시료채취기</code>                                                  |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

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

- `eval_strategy`: epoch
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 10
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `tf32`: False
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates

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

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 16
- `eval_accumulation_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 10
- `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`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: False
- `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_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`: 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`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_64_cosine_map@100 |
|:------:|:----:|:-------------:|:----------------------:|:----------------------:|:---------------------:|
| 0.8767 | 4    | -             | 0.2156                 | 0.2448                 | 0.1831                |
| 1.9726 | 9    | -             | 0.2511                 | 0.2765                 | 0.2154                |
| 2.1918 | 10   | 7.6309        | -                      | -                      | -                     |
| 2.8493 | 13   | -             | 0.2531                 | 0.2852                 | 0.2345                |
| 3.9452 | 18   | -             | 0.2617                 | 0.2914                 | 0.2353                |
| 4.3836 | 20   | 5.3042        | -                      | -                      | -                     |
| 4.8219 | 22   | -             | 0.2626                 | 0.2946                 | 0.2422                |
| 5.9178 | 27   | -             | 0.2629                 | 0.2987                 | 0.2481                |
| 6.5753 | 30   | 4.2433        | -                      | -                      | -                     |
| 6.7945 | 31   | -             | 0.2684                 | 0.2988                 | 0.2495                |
| 7.8904 | 36   | -             | 0.2652                 | 0.3003                 | 0.2488                |
| 8.7671 | 40   | 3.9117        | 0.2653                 | 0.3003                 | 0.2482                |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.1.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 1.0.0
- Datasets: 2.19.1
- 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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