metadata
datasets:
- GreenNode/GreenNode-Table-Markdown-Retrieval
language:
- vi
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
widget: []
metrics:
- InfoNCE
license: cc-by-4.0
SentenceTransformer
This is a sentence-transformers 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.
Model Details
Model Description
- Model Type: Sentence Transformer
- Maximum Sequence Length: 8192 tokens
- Output Dimensionality: 1024 tokens
- Similarity Function: Cosine Similarity
- Training Dataset: - GreenNode/GreenNode-Table-Markdown-Retrieval
- Language: Vietnamese
- License: cc-by-4.0
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': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(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})
(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("sentence_transformers_model_id")
# Run inference
sentences = [
'The weather is lovely today.',
"It's so sunny outside!",
'He drove to the stadium.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Training Details
Evaluation
Table: Performance comparison of various models on GreenNodeTableRetrieval
Dataset: GreenNode/GreenNode-Table-Markdown-Retrieval
| Model Name | MAP@5 ↑ | MRR@5 ↑ | NDCG@5 ↑ | Recall@5 ↑ | Mean ↑ |
|---|---|---|---|---|---|
| Multilingual Embedding models | |||||
| me5_small | 33.75 | 33.75 | 35.68 | 41.49 | 36.17 |
| me5_large | 38.16 | 38.16 | 40.27 | 46.62 | 40.80 |
| M3-Embedding | 36.52 | 36.52 | 38.60 | 44.84 | 39.12 |
| OpenAI-embedding-v3 | 30.61 | 30.61 | 32.57 | 38.46 | 33.06 |
| Vietnamese Embedding models (Prior Work) | |||||
| halong-embedding | 32.15 | 32.15 | 34.13 | 40.09 | 34.63 |
| sup-SimCSE-VietNamese-phobert_base | 10.90 | 10.90 | 12.03 | 15.41 | 12.31 |
| vietnamese-bi-encoder | 13.61 | 13.61 | 14.63 | 17.68 | 14.89 |
| GreenNode-Embedding (Our Work) | |||||
| M3-GN-VN | 41.85 | 41.85 | 44.15 | 57.05 | 46.23 |
| M3-GN-VN-Mixed | 42.08 | 42.08 | 44.33 | 51.06 | 44.89 |
Table: Performance comparison of various models on ZacLegalTextRetrieval
Dataset: GreenNode/zalo-ai-legal-text-retrieval-vn
| Model Name | MAP@5 ↑ | MRR@5 ↑ | NDCG@5 ↑ | Recall@5 ↑ | Mean ↑ |
|---|---|---|---|---|---|
| Multilingual Embedding models | |||||
| me5_small | 54.68 | 54.37 | 58.32 | 69.16 | 59.13 |
| me5_large | 60.14 | 59.62 | 64.17 | 76.02 | 64.99 |
| M3-Embedding | 69.34 | 68.96 | 73.70 | 86.68 | 74.67 |
| OpenAI-embedding-v3 | 38.68 | 38.80 | 41.53 | 49.94 | 41.74 |
| Vietnamese Embedding models (Prior Work) | |||||
| halong-embedding | 52.57 | 52.28 | 56.64 | 68.72 | 57.55 |
| sup-SimCSE-VietNamese-phobert_base | 25.15 | 25.07 | 27.81 | 35.79 | 28.46 |
| vietnamese-bi-encoder | 54.88 | 54.47 | 59.10 | 79.51 | 61.99 |
| GreenNode-Embedding (Our Work) | |||||
| M3-GN-VN | 65.03 | 64.80 | 69.19 | 81.66 | 70.17 |
| M3-GN-VN-Mixed | 69.75 | 69.28 | 74.01 | 86.74 | 74.95 |
Table: Performance comparison of various models on VieQuADRetrieval
Dataset: taidng/UIT-ViQuAD2.0
| Model Name | MAP@5 ↑ | MRR@5 ↑ | NDCG@5 ↑ | Recall@5 ↑ | Mean ↑ |
|---|---|---|---|---|---|
| Multilingual Embedding models | |||||
| me5_small | 40.42 | 69.21 | 50.05 | 50.71 | 52.60 |
| me5_large | 44.18 | 67.81 | 53.04 | 55.86 | 55.22 |
| M3-Embedding | 44.08 | 72.28 | 54.07 | 56.01 | 56.61 |
| OpenAI-embedding-v3 | 32.39 | 53.97 | 40.48 | 43.02 | 42.47 |
| Vietnamese Embedding models (Prior Work) | |||||
| halong-embedding | 39.42 | 62.31 | 48.63 | 52.73 | 50.77 |
| sup-SimCSE-VietNamese-phobert_base | 20.45 | 35.99 | 26.73 | 29.59 | 28.19 |
| vietnamese-bi-encoder | 31.89 | 54.62 | 40.26 | 42.53 | 42.33 |
| GreenNode-Embedding (Our Work) | |||||
| M3-GN-VN | 42.85 | 71.98 | 52.90 | 54.25 | 55.50 |
| M3-GN-VN-Mixed | 44.20 | 72.64 | 54.30 | 56.30 | 56.86 |
Table: Performance comparison of various models on GreenNodeTableRetrieval (Hit Rate)
| Model Name | Hit Rate@1 ↑ | Hit Rate@5 ↑ | Hit Rate@10 ↑ | Hit Rate@20 ↑ |
|---|---|---|---|---|
| Multilingual Embedding models | ||||
| me5_small | 38.99 | 53.37 | 59.28 | 65.09 |
| me5_large | 43.99 | 59.74 | 65.74 | 71.59 |
| bge-m3 | 42.15 | 57.00 | 63.05 | 68.96 |
| OpenAI-embedding-v3 | - | - | - | - |
| Vietnamese Embedding models (Prior Work) | ||||
| halong-embedding | 37.22 | 52.49 | 58.57 | 64.64 |
| sup-SimCSE-VietNamese-phobert_base | 14.00 | 24.74 | 30.32 | 36.44 |
| vietnamese-bi-encoder | 16.89 | 25.94 | 30.50 | 35.70 |
| GreenNode-Embedding (Our Work) | ||||
| M3-GN-VN | 48.31 | 64.60 | 70.83 | 76.46 |
| M3-GN-VN-Mixed | 47.94 | 64.24 | 70.43 | 76.14 |
Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.0.1
- Transformers: 4.42.4
- PyTorch: 2.3.1
- Accelerate: 0.33.0
- Datasets: 2.20.0
- Tokenizers: 0.19.1