metadata

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 model finetuned from 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
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 tokens
Similarity Function: Cosine Similarity
Language: vi-focused, multilingual
License: apache-2.0

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

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:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# 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

Evaluation

Metrics

Information Retrieval

Dataset: updating
note: We sampled 20% of the Zalo Legal train dataset for fast testing; our model did not train on this dataset.
Evaluated with 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

Citation

BibTeX

Sentence Transformers

@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

@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

@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}
}