README.md

---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:1109
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
widget:
- source_sentence: Lãi suất vay tiêu dùng từ thẻ kỳ hạn 2-5 tháng là 12%/năm.
  sentences:
  - Mức lãi suất áp dụng cho khoản vay tiêu dùng thẻ kỳ hạn ngắn (2-5 tháng) là 12%.
  - Hạn dùng ưu đãi của khách hàng VIP được tính theo năm dương lịch hưởng quyền lợi.
  - dịch vụ áp dụng cho nhân viên sacombank được ủy quyền sử dụng thẻ
- source_sentence: Mở tài khoản ngân hàng cần giấy tờ gì?
  sentences:
  - Địa chỉ Hanoi Le Jardin Hotel & Spa là số 46A đường Nguyễn Trường Tộ, Ba Đình.
  - Hồ sơ mở tài khoản thanh toán cá nhân
  - CCTG cần được giữ gìn nguyên vẹn, tránh tẩy xóa hay làm hỏng.
- source_sentence: Chứng chỉ tiền gửi có lãi suất 7,1%/năm.
  sentences:
  - Địa chỉ nhà hàng A Bản là số 76 đường Trần Phú, Quận Ba Đình, Hà Nội.
  - Điều kiện miễn phí cho sinh viên trên 20 tuổi là duy trì số dư bình quân từ 500.000
    VND.
  - Mức lãi suất cố định áp dụng cho Chứng chỉ tiền gửi là 7,1% một năm.
- source_sentence: Thời gian xử lý hoàn tiền vào thẻ là 5-10 phút.
  sentences:
  - 'bảo hiểm mục 13: các loại trừ chung'
  - Khách hàng sử dụng Combo Đa Lợi không bị thu phí khi giao dịch qua Ngân hàng số.
  - Chủ thẻ sẽ nhận lại tiền vào hạn mức tín dụng sau khoảng 5 đến 10 phút.
- source_sentence: Quy đổi 1 lượt golf thành 1 đêm nghỉ dưỡng tiêu chuẩn cho 2 người.
  sentences:
  - Giao dịch ở siêu thị bằng thẻ được hoàn lại giá trị
  - Ngân hàng sẽ báo trước 1 tuần nếu có thay đổi về quy định dịch vụ.
  - Mỗi lượt golf trong tài khoản tương đương với 01 đêm phòng tiêu chuẩn dành cho
    02 khách.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
model-index:
- name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: banking val
      type: banking-val
    metrics:
    - type: pearson_cosine
      value: 0.48784389453148286
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.4829396210794567
      name: Spearman Cosine
---

# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2). 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:** [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) <!-- at revision 4328cf26390c98c5e3c738b4460a05b95f4911f5 -->
- **Maximum Sequence Length:** 512 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/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, 'architecture': 'XLMRobertaModel'})
  (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})
)
```

## 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 = [
    'Quy đổi 1 lượt golf thành 1 đêm nghỉ dưỡng tiêu chuẩn cho 2 người.',
    'Mỗi lượt golf trong tài khoản tương đương với 01 đêm phòng tiêu chuẩn dành cho 02 khách.',
    'Giao dịch ở siêu thị bằng thẻ được hoàn lại giá trị',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000,  0.7473, -0.0708],
#         [ 0.7473,  1.0000, -0.0487],
#         [-0.0708, -0.0487,  1.0000]])
```

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## Evaluation

### Metrics

#### Semantic Similarity

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

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.4878     |
| **spearman_cosine** | **0.4829** |

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## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 1,109 training samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                         | sentence2                                                                         |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            |
  | details | <ul><li>min: 7 tokens</li><li>mean: 17.54 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 19.86 tokens</li><li>max: 34 tokens</li></ul> |
* Samples:
  | sentence1                                                              | sentence2                                                                              |
  |:-----------------------------------------------------------------------|:---------------------------------------------------------------------------------------|
  | <code>Hạn mức chuyển tiền qua internet banking</code>                  | <code>Giới hạn giao dịch trên mobile banking mỗi ngày</code>                           |
  | <code>Lãi suất tiền gửi Tương lai kỳ hạn 1 năm là 3,70%/năm.</code>    | <code>Sản phẩm Tiền gửi Tương lai 12 tháng có lãi suất 3,70%.</code>                   |
  | <code>Chi tiêu khác ngoài siêu thị và di chuyển được hoàn 0,5%.</code> | <code>Các giao dịch chi tiêu thông thường khác áp dụng tỷ lệ hoàn tiền là 0,5%.</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768
      ],
      "matryoshka_weights": [
          1
      ],
      "n_dims_per_step": -1
  }
  ```

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

- `eval_strategy`: epoch
- `per_device_train_batch_size`: 32
- `learning_rate`: 2e-05
- `num_train_epochs`: 8
- `warmup_ratio`: 0.1
- `fp16`: True
- `load_best_model_at_end`: True
- `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`: 8
- `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`: 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`: 8
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `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
- `bf16`: False
- `fp16`: True
- `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`: True
- `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
- `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
- `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`: 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
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}

</details>

### Training Logs
| Epoch   | Step    | Training Loss | banking-val_spearman_cosine |
|:-------:|:-------:|:-------------:|:---------------------------:|
| 0.2857  | 10      | 0.4973        | -                           |
| 0.5714  | 20      | 0.3515        | -                           |
| 0.8571  | 30      | 0.2183        | -                           |
| 1.0     | 35      | -             | 0.4564                      |
| 1.1429  | 40      | 0.1684        | -                           |
| 1.4286  | 50      | 0.0942        | -                           |
| 1.7143  | 60      | 0.117         | -                           |
| 2.0     | 70      | 0.0823        | 0.4266                      |
| 2.2857  | 80      | 0.0539        | -                           |
| 2.5714  | 90      | 0.0506        | -                           |
| 2.8571  | 100     | 0.1039        | -                           |
| 3.0     | 105     | -             | 0.4439                      |
| 3.1429  | 110     | 0.0516        | -                           |
| 3.4286  | 120     | 0.0325        | -                           |
| 3.7143  | 130     | 0.0457        | -                           |
| 4.0     | 140     | 0.0933        | 0.4489                      |
| 4.2857  | 150     | 0.0759        | -                           |
| 4.5714  | 160     | 0.0441        | -                           |
| 4.8571  | 170     | 0.0379        | -                           |
| 5.0     | 175     | -             | 0.4735                      |
| 5.1429  | 180     | 0.0337        | -                           |
| 5.4286  | 190     | 0.0368        | -                           |
| 5.7143  | 200     | 0.0536        | -                           |
| **6.0** | **210** | **0.0487**    | **0.4899**                  |
| 6.2857  | 220     | 0.0355        | -                           |
| 6.5714  | 230     | 0.0469        | -                           |
| 6.8571  | 240     | 0.0319        | -                           |
| 7.0     | 245     | -             | 0.4845                      |
| 7.1429  | 250     | 0.0306        | -                           |
| 7.4286  | 260     | 0.0272        | -                           |
| 7.7143  | 270     | 0.0398        | -                           |
| 8.0     | 280     | 0.0313        | 0.4829                      |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.12.12
- Sentence Transformers: 5.1.1
- Transformers: 4.57.1
- PyTorch: 2.8.0+cu126
- Accelerate: 1.11.0
- Datasets: 4.4.2
- Tokenizers: 0.22.1

## 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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