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---

base_model: intfloat/multilingual-e5-base
datasets: []
language:
- vi
- en
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
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Bóng đá có lợi ích gì cho sức khỏe?
  sentences:
  - Bóng đá giúp cải thiện sức khỏe tim mạch và tăng cường sức bền.
  - Bóng đá là môn thể thao phổ biến nhất thế giới.
  - Bóng đá có thể giúp bạn kết nối với nhiều người hơn.

model-index:
- name: Halong Embedding
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.8294209702660407
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9233176838810642
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9436619718309859
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9687010954616588
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.8294209702660407
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3145539906103286
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1931142410015649
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09906103286384975
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.8145539906103286
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9178403755868545
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9389671361502347
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9640062597809077
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8976041381292648
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.879893558884169
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8763179130484675
      name: Cosine Map@100

---


# Halong Embedding

Halong Embedding is a Vietnamese text embedding focused on RAG and production efficiency:
- 📚 Trained on a in house dataset consist of approximately 100,000 examples of question and related documents
- 🪆 Trained with a Matryoshka loss, allowing you to truncate embeddings with minimal performance loss: smaller embeddings are faster to compare.

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base). 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:** [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) <!-- at revision d13f1b27baf31030b7fd040960d60d909913633f -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **Language:** vi-focused, 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: 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})

  (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

import torch



# Download from the 🤗 Hub

model = SentenceTransformer("hiieu/halong_embedding")



# Define query and documents

query = "Bóng đá có lợi ích gì cho sức khỏe?"

docs = [

    "Bóng đá giúp cải thiện sức khỏe tim mạch và tăng cường sức bền.",

    "Bóng đá là môn thể thao phổ biến nhất thế giới.",

    "Chơi bóng đá giúp giảm căng thẳng và cải thiện tâm lý.",

    "Bóng đá có thể giúp bạn kết nối với nhiều người hơn.",

    "Bóng đá không chỉ là môn thể thao mà còn là cách để giải trí."

]



# Encode query and documents

query_embedding = model.encode([query])

doc_embeddings = model.encode(docs)

similarities = model.similarity(query_embedding, doc_embeddings).flatten()



# Sort documents by cosine similarity

sorted_indices = torch.argsort(similarities, descending=True)

sorted_docs = [docs[idx] for idx in sorted_indices]

sorted_scores = [similarities[idx].item() for idx in sorted_indices]



# Print sorted documents with their cosine scores

for doc, score in zip(sorted_docs, sorted_scores):

    print(f"Document: {doc} - Cosine Similarity: {score:.4f}")



# Document: Bóng đá giúp cải thiện sức khỏe tim mạch và tăng cường sức bền. - Cosine Similarity: 0.7318

# Document: Chơi bóng đá giúp giảm căng thẳng và cải thiện tâm lý. - Cosine Similarity: 0.6623

# Document: Bóng đá không chỉ là môn thể thao mà còn là cách để giải trí. - Cosine Similarity: 0.6102

# Document: Bóng đá có thể giúp bạn kết nối với nhiều người hơn. - Cosine Similarity: 0.4988

# Document: Bóng đá là môn thể thao phổ biến nhất thế giới. - Cosine Similarity: 0.4828

```

### Matryoshka Embeddings Inference
```python

from sentence_transformers import SentenceTransformer

import torch.nn.functional as F

import torch



matryoshka_dim = 64

model = SentenceTransformer(

    "hiieu/halong_embedding",

    truncate_dim=matryoshka_dim,

)



# Define query and documents

query = "Bóng đá có lợi ích gì cho sức khỏe?"

docs = [

    "Bóng đá giúp cải thiện sức khỏe tim mạch và tăng cường sức bền.",

    "Bóng đá là môn thể thao phổ biến nhất thế giới.",

    "Chơi bóng đá giúp giảm căng thẳng và cải thiện tâm lý.",

    "Bóng đá có thể giúp bạn kết nối với nhiều người hơn.",

    "Bóng đá không chỉ là môn thể thao mà còn là cách để giải trí."

]



# Encode query and documents

query_embedding = model.encode([query])

doc_embeddings = model.encode(docs)

similarities = model.similarity(query_embedding, doc_embeddings).flatten()



# Sort documents by cosine similarity

sorted_indices = torch.argsort(similarities, descending=True)

sorted_docs = [docs[idx] for idx in sorted_indices]

sorted_scores = [similarities[idx].item() for idx in sorted_indices]



# Print sorted documents with their cosine scores

for doc, score in zip(sorted_docs, sorted_scores):

    print(f"Document: {doc} - Cosine Similarity: {score:.4f}")



# Document: Bóng đá giúp cải thiện sức khỏe tim mạch và tăng cường sức bền. - Cosine Similarity: 0.8045

# Document: Chơi bóng đá giúp giảm căng thẳng và cải thiện tâm lý. - Cosine Similarity: 0.7676

# Document: Bóng đá không chỉ là môn thể thao mà còn là cách để giải trí. - Cosine Similarity: 0.6758

# Document: Bóng đá có thể giúp bạn kết nối với nhiều người hơn. - Cosine Similarity: 0.5931

# Document: Bóng đá là môn thể thao phổ biến nhất thế giới. - Cosine Similarity: 0.5105

```
<!--
### 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>
-->

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### Out-of-Scope Use

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## Evaluation

### Metrics

#### Information Retrieval
* Dataset: [Zalo legal retrieval dataet](https://huggingface.co/datasets/hiieu/legal_eval_label)
* *note*: We sampled 20% of the Zalo Legal train dataset for fast testing; our model did not train on this dataset.
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Model                | Accuracy@1 | Accuracy@3 | Accuracy@5 | Accuracy@10 | Precision@1 | Precision@3 | Precision@5 | Precision@10 | Recall@1 | Recall@3 | Recall@5 | Recall@10 | NDCG@10 | MRR@10 | MAP@100 |
|----------------------|------------|------------|------------|-------------|-------------|--------------|--------------|---------------|-----------|-----------|-----------|------------|---------|--------|---------|
| 
vietnamese-bi-encoder            | 0.8169     | 0.9108     | 0.9437     | 0.9640      | 0.8169      | 0.3099       | 0.1931       | 0.0987        | 0.8020    | 0.9045    | 0.9390    | 0.9601     | 0.8882  | 0.8685 | 0.8652  |
| sup-SimCSE-VietNamese-phobert-base            | 0.5540     | 0.7308     | 0.7981     | 0.8748      | 0.5540      | 0.2473       | 0.1621       | 0.0892        | 0.5446    | 0.7246    | 0.7903    | 0.8693     | 0.7068  | 0.6587 | 0.6592  |
| halong_embedding (768)         | 0.8294     | 0.9233     | 0.9437     | 0.9687      | 0.8294      | 0.3146       | 0.1931       | 0.0991        | 0.8146    | 0.9178    | 0.9390    | 0.9640     | 0.8976  | 0.8799 | 0.8763  |

| halong_embedding (512)         | 0.8138     | 0.9233     | 0.9390     | 0.9703      | 0.8138      | 0.3146       | 0.1922       | 0.0992        | 0.7989    | 0.9178    | 0.9343    | 0.9656     | 0.8917  | 0.8715 | 0.8678  |
| halong_embedding (256)         | 0.7934     | 0.8967     | 0.9280     | 0.9593      | 0.7934      | 0.3062       | 0.1900       | 0.0981        | 0.7786    | 0.8920    | 0.9233    | 0.9546     | 0.8743  | 0.8520 | 0.8489  |

| halong_embedding (128)         | 0.7840     | 0.8951     | 0.9264     | 0.9515      | 0.7840      | 0.3046       | 0.1894       | 0.0975        | 0.7707    | 0.8889    | 0.9210    | 0.9476     | 0.8669  | 0.8439 | 0.8412  |
| halong_embedding (64)          | 0.6980     | 0.8435     | 0.8920     | 0.9358      | 0.6980      | 0.2864       | 0.1815       | 0.0958        | 0.6854    | 0.8365    | 0.8842    | 0.9311     | 0.8145  | 0.7805 | 0.7775  |





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## Citation



You can cite our work as below:



```Plaintext

@misc{HalongEmbedding,

  title={HalongEmbedding: A Vietnamese Text Embedding},

  author={Ngo Hieu},

  year={2024},

  publisher={Huggingface},

}

```





### 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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