---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:33
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: keepitreal/vietnamese-sbert
widget:
- source_sentence: Áo Polo Lacoste với chất liệu Petit Piqué và thiết kế cổ gập kinh
    điển
  sentences:
  - Giày cao gót đẳng cấp
  - Xe điều khiển từ xa
  - Áo polo sang trọng
- source_sentence: Sony Alpha A7 IV với cảm biến CMOS Exmor R 33MP và khả năng quay
    4K 60fps
  sentences:
  - Giày cao gót sang trọng
  - Sách văn học tuổi thơ
  - Máy ảnh chuyên nghiệp
- source_sentence: Laneige Water Bank Cream với công nghệ Hydro Ionized Mineral Water
    và kết cấu gel mỏng nhẹ
  sentences:
  - Điện thoại flagship cao cấp
  - Kem dưỡng ẩm nổi bật
  - Giày tây nam lịch lãm
- source_sentence: Adidas Ultraboost với công nghệ Boost™ và đế ngoài Continental™
    Rubber
  sentences:
  - Tai nghe chống ồn hàng đầu
  - Quần short kaki trẻ trung
  - Giày chạy bộ hiện đại
- source_sentence: Áo thun từ cotton mềm mại, kiểu dáng đa dạng phù hợp cho nhiều
    phong cách
  sentences:
  - Máy ảnh vlog chuyên nghiệp
  - Áo thun thoải mái
  - Laptop hiệu năng mạnh mẽ
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 keepitreal/vietnamese-sbert
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.25
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.75
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.75
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 1.0
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.25
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.25
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.15000000000000002
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.1
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.25
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.75
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.75
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 1.0
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.6377310833652008
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.525
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.525
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.25
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.75
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.75
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 1.0
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.25
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.25
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.15000000000000002
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.1
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.25
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.75
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.75
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 1.0
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.6079899373088598
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.4861111111111111
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.4861111111111111
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.25
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.75
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.75
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.75
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.25
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.25
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.15000000000000002
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.07500000000000001
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.25
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.75
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.75
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.75
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.5
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.41666666666666663
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.43749999999999994
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.5
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.75
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.75
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 1.0
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.5
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.25
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.15000000000000002
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.1
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.5
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.75
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.75
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 1.0
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7410657717261977
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.6607142857142857
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.6607142857142857
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.0
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.5
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.5
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.75
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.0
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.16666666666666666
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.07500000000000001
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.0
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.5
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.5
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.75
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.4045166735627343
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.29166666666666663
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.30952380952380953
      name: Cosine Map@100
---

# SentenceTransformer based on keepitreal/vietnamese-sbert

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [keepitreal/vietnamese-sbert](https://huggingface.co/keepitreal/vietnamese-sbert) on the json dataset. 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:** [keepitreal/vietnamese-sbert](https://huggingface.co/keepitreal/vietnamese-sbert) <!-- at revision a9467ef2ef47caa6448edeabfd8e5e5ce0fa2a23 -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - json
<!-- - **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}) with Transformer model: RobertaModel 
  (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("zxcvo/product-search-model")
# Run inference
sentences = [
    'Áo thun từ cotton mềm mại, kiểu dáng đa dạng phù hợp cho nhiều phong cách',
    'Áo thun thoải mái',
    'Laptop hiệu năng mạnh mẽ',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

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

<!--
### Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details>
-->

<!--
### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

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

</details>
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

## Evaluation

### Metrics

#### Information Retrieval

* Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | dim_768    | dim_512   | dim_256 | dim_128    | dim_64     |
|:--------------------|:-----------|:----------|:--------|:-----------|:-----------|
| cosine_accuracy@1   | 0.25       | 0.25      | 0.25    | 0.5        | 0.0        |
| cosine_accuracy@3   | 0.75       | 0.75      | 0.75    | 0.75       | 0.5        |
| cosine_accuracy@5   | 0.75       | 0.75      | 0.75    | 0.75       | 0.5        |
| cosine_accuracy@10  | 1.0        | 1.0       | 0.75    | 1.0        | 0.75       |
| cosine_precision@1  | 0.25       | 0.25      | 0.25    | 0.5        | 0.0        |
| cosine_precision@3  | 0.25       | 0.25      | 0.25    | 0.25       | 0.1667     |
| cosine_precision@5  | 0.15       | 0.15      | 0.15    | 0.15       | 0.1        |
| cosine_precision@10 | 0.1        | 0.1       | 0.075   | 0.1        | 0.075      |
| cosine_recall@1     | 0.25       | 0.25      | 0.25    | 0.5        | 0.0        |
| cosine_recall@3     | 0.75       | 0.75      | 0.75    | 0.75       | 0.5        |
| cosine_recall@5     | 0.75       | 0.75      | 0.75    | 0.75       | 0.5        |
| cosine_recall@10    | 1.0        | 1.0       | 0.75    | 1.0        | 0.75       |
| **cosine_ndcg@10**  | **0.6377** | **0.608** | **0.5** | **0.7411** | **0.4045** |
| cosine_mrr@10       | 0.525      | 0.4861    | 0.4167  | 0.6607     | 0.2917     |
| cosine_map@100      | 0.525      | 0.4861    | 0.4375  | 0.6607     | 0.3095     |

<!--
## 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.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Dataset

#### json

* Dataset: json
* Size: 33 training samples
* Columns: <code>positive</code> and <code>anchor</code>
* Approximate statistics based on the first 33 samples:
  |         | positive                                                                           | anchor                                                                          |
  |:--------|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                          |
  | details | <ul><li>min: 18 tokens</li><li>mean: 22.82 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 6.97 tokens</li><li>max: 8 tokens</li></ul> |
* Samples:
  | positive                                                                                       | anchor                                   |
  |:-----------------------------------------------------------------------------------------------|:-----------------------------------------|
  | <code>Áo Sơ Mi Nam Trắng Classic với chất liệu cotton cao cấp, kiểu dáng lịch lãm</code>       | <code>Áo sơ mi tinh tế</code>            |
  | <code>Đắc Nhân Tâm của Dale Carnegie với những nguyên tắc xây dựng mối quan hệ hiệu quả</code> | <code>Sách kinh điển về giao tiếp</code> |
  | <code>Nike Air Force 1 với thiết kế logo Swoosh và công nghệ Air-Sole</code>                   | <code>Giày sneaker cổ điển</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,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

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

- `eval_strategy`: epoch
- `per_device_train_batch_size`: 32
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 4
- `bf16`: True
- `load_best_model_at_end`: True

#### 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`: 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`: 4
- `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
- `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`: 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}
- `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_eval_metrics`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch   | Step  | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 |
|:-------:|:-----:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
| 1.0     | 1     | 0.6050                 | 0.6050                 | 0.5                    | 0.7083                 | 0.5767                |
| **2.0** | **2** | **0.605**              | **0.608**              | **0.5**                | **0.7411**             | **0.4045**            |
| 3.0     | 3     | 0.6377                 | 0.6080                 | 0.5                    | 0.6488                 | 0.4045                |
| 4.0     | 4     | 0.6377                 | 0.6080                 | 0.5                    | 0.7411                 | 0.4045                |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.11.0
- Sentence Transformers: 3.3.1
- Transformers: 4.41.2
- PyTorch: 2.5.1+cu124
- Accelerate: 1.2.1
- 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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