Table of contents
- Introduce
- Usage
- Performance
- Contact
- Support The Project
- Citation
Introduce
ViDense is a VietNamese Embedding Model. Fine-tuned and enhanced with tailored methods, ViDense incorporates
advanced
techniques to optimize performance for text embeddings in various applications.
Model Configuration and Methods:
- Base Model: FacebookAI/xlm-roberta-large
- Trained for 10 epochs with a train batch size of 2048.
- Utilizes a 3-phase training approach, where the best checkpoint from each phase serves as the base model for the next.
- Position Encoding: Rotary Position Encoding
- Attention: Blockwise Parallel Transformer
- Pooling: Mean Pooling
- Momentum Encoder: Incorporates MoCo (Momentum Contrast) to enhance in-batch
negative sampling.
- Rank Encoder: Introduces a Rank Encoder to account for transitive positive relationships. By considering positives
of positives as relevant to the anchor, it reranks the corpus using the Spearman metric and integrates Spearman
weights into the loss calculation for improved ranking.
- Loss Function: Cross Entropy Loss (Sim Scores) + MSE Loss (Spearman Scores)
Usage
pip install -U transformers
import torch
from transformers import AutoModel, AutoTokenizer
def avg_pooling(attention_mask, outputs):
last_hidden = outputs.last_hidden_state
return (last_hidden * attention_mask.unsqueeze(-1)).sum(1) / attention_mask.sum(-1).unsqueeze(-1)
tokenizer = AutoTokenizer.from_pretrained('namdp-ptit/ViDense')
model = AutoModel.from_pretrained('namdp-ptit/ViDense')
sentences = [
'Tỉnh nào có diện tích lớn nhất Việt Nam',
'Tỉnh nào có diện tích nhỏ nhất Việt Nam',
'Tỉnh nào có diện tích rộng nhất Việt Nam'
]
inputs = tokenizer(sentences, return_tensors='pt', padding=True)
with torch.no_grad():
outputs = model(**inputs)
outputs = avg_pooling(inputs['attention_mask'], outputs)
cosine_sim_1 = torch.nn.functional.cosine_similarity(
outputs[0].unsqueeze(0),
outputs[1].unsqueeze(0)
)
cosine_sim_2 = torch.nn.functional.cosine_similarity(
outputs[0].unsqueeze(0),
outputs[2].unsqueeze(0)
)
print(cosine_sim_1.item()) # 0.056096598505973816
print(cosine_sim_2.item()) # 0.9861876964569092
Performance
Below is a comparision table of the results I achieved compared to some other embedding models on three
benchmarks: ZAC, WebFaq, OwiFaq, ViQuAD2.0, ViLegal
with metric Recall@3
Here are the information of these 3 benchmarks:
- ZAC: merge train and test into a new benchmark, ~ 3200 queries, ~ 330K documents in corpus.
- WebFAQ and OwiFaq: merge train and test into a new benchmark, ~ 124K queries, ~ 124K documents in corpus.
- ViQuAD2.0: merge train, validation and test into a new benchmark, ~ 39.6K queries, ~ 39.6K documents in corpus.
- ViLegal: ~ 144K queries, ~ 144K documents in corpus.
Contact
Email: [email protected]
LinkedIn: Dang Phuong Nam
Facebook: Phương Nam
Support The Project
If you find this project helpful and wish to support its ongoing development, here are some ways you can contribute:
- Star the Repository: Show your appreciation by starring the repository. Your support motivates further
development
and enhancements.
- Contribute: I welcome your contributions! You can help by reporting bugs, submitting pull requests, or
suggesting new features.
- Donate: If you’d like to support financially, consider making a donation. You can donate through:
- Vietcombank: 9912692172 - DANG PHUONG NAM
Thank you for your support!
Citation
Please cite as
@misc{ViDense,
title={ViDense: An Embedding Model for Vietnamese Long Context},
author={Nam Dang Phuong},
year={2025},
publisher={Huggingface},
}
Beta
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used queries
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