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--- |
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datasets: |
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- GreenNode/GreenNode-Table-Markdown-Retrieval |
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language: |
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- vi |
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- en |
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library_name: sentence-transformers |
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pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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widget: [] |
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metrics: |
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- InfoNCE |
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license: mit |
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--- |
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# SentenceTransformer |
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This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Maximum Sequence Length:** 8192 tokens |
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- **Output Dimensionality:** 1024 tokens |
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- **Similarity Function:** Cosine Similarity |
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- **Training Dataset:** - [GreenNode/GreenNode-Table-Markdown-Retrieval](https://huggingface.co/datasets/GreenNode/GreenNode-Table-Markdown-Retrieval-VN) |
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- **Language:** Vietnamese |
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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### Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel |
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(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
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(2): Normalize() |
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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("GreenNode/GreenNode-Embedding-Large-VN-Mixed-V1") |
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# Run inference |
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sentences = [ |
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'The weather is lovely today.', |
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"It's so sunny outside!", |
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'He drove to the stadium.', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 1024] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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## Evaluation |
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### Table: Performance comparison of various models on GreenNodeTableRetrieval |
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Dataset: [GreenNode/GreenNode-Table-Markdown-Retrieval](https://huggingface.co/datasets/GreenNode/GreenNode-Table-Markdown-Retrieval-VN) |
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| Model Name | MAP@5 ↑ | MRR@5 ↑ | NDCG@5 ↑ | Recall@5 ↑ | Mean ↑ | |
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|--------------------------------------------|--------:|--------:|---------:|-----------:|-------:| |
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| **Multilingual Embedding models** | | | | | | |
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| me5_small | 33.75 | 33.75 | 35.68 | 41.49 | 36.17 | |
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| me5_large | 38.16 | 38.16 | 40.27 | 46.62 | 40.80 | |
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| M3-Embedding | 36.52 | 36.52 | 38.60 | 44.84 | 39.12 | |
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| OpenAI-embedding-v3 | 30.61 | 30.61 | 32.57 | 38.46 | 33.06 | |
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| **Vietnamese Embedding models (Prior Work)**| | | | | | |
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| halong-embedding | 32.15 | 32.15 | 34.13 | 40.09 | 34.63 | |
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| sup-SimCSE-VietNamese-phobert_base | 10.90 | 10.90 | 12.03 | 15.41 | 12.31 | |
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| vietnamese-bi-encoder | 13.61 | 13.61 | 14.63 | 17.68 | 14.89 | |
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| **GreenNode-Embedding (Our Work)** | | | | | | |
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| *M3-GN-VN* | _41.85_ | _41.85_ | _44.15_ | _57.05_ | _46.23_ | |
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| **M3-GN-VN-Mixed** | **42.08** | **42.08** | **44.33** | **51.06** | **44.89** | |
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### Table: Performance comparison of various models on ZacLegalTextRetrieval |
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Dataset: [GreenNode/zalo-ai-legal-text-retrieval-vn](https://huggingface.co/datasets/GreenNode/zalo-ai-legal-text-retrieval-vn) |
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| Model Name | MAP@5 ↑ | MRR@5 ↑ | NDCG@5 ↑ | Recall@5 ↑ | Mean ↑ | |
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|--------------------------------------------|--------:|--------:|---------:|-----------:|-------:| |
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| **Multilingual Embedding models** | | | | | | |
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| me5_small | 54.68 | 54.37 | 58.32 | 69.16 | 59.13 | |
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| me5_large | 60.14 | 59.62 | 64.17 | 76.02 | 64.99 | |
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| *M3-Embedding* | _69.34_ | _68.96_ | _73.70_ | _86.68_ | _74.67_ | |
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| OpenAI-embedding-v3 | 38.68 | 38.80 | 41.53 | 49.94 | 41.74 | |
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| **Vietnamese Embedding models (Prior Work)**| | | | | | |
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| halong-embedding | 52.57 | 52.28 | 56.64 | 68.72 | 57.55 | |
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| sup-SimCSE-VietNamese-phobert_base | 25.15 | 25.07 | 27.81 | 35.79 | 28.46 | |
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| vietnamese-bi-encoder | 54.88 | 54.47 | 59.10 | 79.51 | 61.99 | |
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| **GreenNode-Embedding (Our Work)** | | | | | | |
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| M3-GN-VN | 65.03 | 64.80 | 69.19 | 81.66 | 70.17 | |
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| **M3-GN-VN-Mixed** | **69.75** | **69.28** | **74.01** | **86.74** | **74.95** | |
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### Table: Performance comparison of various models on VieQuADRetrieval |
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Dataset: [taidng/UIT-ViQuAD2.0](https://huggingface.co/datasets/taidng/UIT-ViQuAD2.0) |
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| Model Name | MAP@5 ↑ | MRR@5 ↑ | NDCG@5 ↑ | Recall@5 ↑ | Mean ↑ | |
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|--------------------------------------------|--------:|--------:|---------:|-----------:|-------:| |
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| **Multilingual Embedding models** | | | | | | |
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| me5_small | 40.42 | 69.21 | 50.05 | 50.71 | 52.60 | |
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| me5_large | 44.18 | 67.81 | 53.04 | 55.86 | 55.22 | |
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| *M3-Embedding* | _44.08_ | _72.28_ | _54.07_ | _56.01_ | _56.61_ | |
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| OpenAI-embedding-v3 | 32.39 | 53.97 | 40.48 | 43.02 | 42.47 | |
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| **Vietnamese Embedding models (Prior Work)**| | | | | | |
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| halong-embedding | 39.42 | 62.31 | 48.63 | 52.73 | 50.77 | |
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| sup-SimCSE-VietNamese-phobert_base | 20.45 | 35.99 | 26.73 | 29.59 | 28.19 | |
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| vietnamese-bi-encoder | 31.89 | 54.62 | 40.26 | 42.53 | 42.33 | |
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| **GreenNode-Embedding (Our Work)** | | | | | | |
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| M3-GN-VN | 42.85 | 71.98 | 52.90 | 54.25 | 55.50 | |
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| **M3-GN-VN-Mixed** | **44.20** | **72.64** | **54.30** | **56.30** | **56.86** | |
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### Table: Performance comparison of various models on GreenNodeTableRetrieval (Hit Rate) |
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| Model Name | Hit Rate@1 ↑ | Hit Rate@5 ↑ | Hit Rate@10 ↑ | Hit Rate@20 ↑ | |
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|------------------------------------------------|--------------|--------------|---------------|---------------| |
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| **Multilingual Embedding models** | | | | | |
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| me5_small | 38.99 | 53.37 | 59.28 | 65.09 | |
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| me5_large | 43.99 | 59.74 | 65.74 | 71.59 | |
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| bge-m3 | 42.15 | 57.00 | 63.05 | 68.96 | |
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| OpenAI-embedding-v3 | - | - | - | - | |
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| **Vietnamese Embedding models (Prior Work)** | | | | | |
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| halong-embedding | 37.22 | 52.49 | 58.57 | 64.64 | |
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| sup-SimCSE-VietNamese-phobert_base | 14.00 | 24.74 | 30.32 | 36.44 | |
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| vietnamese-bi-encoder | 16.89 | 25.94 | 30.50 | 35.70 | |
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| **GreenNode-Embedding (Our Work)** | | | | | |
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| **M3-GN-VN** | **48.31** | **64.60** | **70.83** | **76.46** | |
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| *M3-GN-VN-Mixed* | _47.94_ | _64.24_ | _70.43_ | _76.14_ | |
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### Framework Versions |
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- Python: 3.10.14 |
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- Sentence Transformers: 3.0.1 |
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- Transformers: 4.42.4 |
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- PyTorch: 2.3.1 |
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- Accelerate: 0.33.0 |
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- Datasets: 2.20.0 |
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- Tokenizers: 0.19.1 |
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## Follow us |
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https://x.com/greennode23 |
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## Support |
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https://discord.gg/B6MJFM3J3a |
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## License |
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This repository and the model weights are licensed under the [MIT License](LICENSE). |
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## Citation |
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## Contact Us |
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- General & Collaboration: tung.vu@greennode.ai, thuvt@greennode.ai |
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- Technical: viethq5@greennode.ai |
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