dangvantuan/vietnamese-embedding-LongContext

Model Description:

vietnamese-embedding-LongContext is the Embedding Model for Vietnamese language with context length up to 8096 tokens. This model is a specialized text-embedding trained specifically for the Vietnamese language, which is built upon gte-multilingual and trained using the Multi-Negative Ranking Loss, Matryoshka2dLoss and SimilarityLoss.

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: VietnameseModel 
  (1): Pooling({'word_embedding_dimension': 768, '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()
)

Training and Fine-tuning process

The model underwent a rigorous four-stage training and fine-tuning process, each tailored to enhance its ability to generate precise and contextually relevant sentence embeddings for the Vietnamese language. Below is an outline of these stages:

Stage 1: Training NLI on dataset XNLI:

• Dataset: XNLI-vn
• Method: Training using Multi-Negative Ranking Loss and Matryoshka2dLoss. This stage focused on improving the model's ability to discern and rank nuanced differences in sentence semantics.

Stage 2: Fine-tuning for Semantic Textual Similarity on STS Benchmark

• Dataset: STSB-vn
• Method: Fine-tuning specifically for the semantic textual similarity benchmark using Siamese BERT-Networks configured with the 'sentence-transformers' library. This stage honed the model's precision in capturing semantic similarity across various types of Vietnamese texts.

Usage:

Using this model becomes easy when you have sentence-transformers installed:

pip install -U sentence-transformers

Then you can use the model like this:

from sentence_transformers import SentenceTransformer
sentences = ["Hà Nội là thủ đô của Việt Nam", "Đà Nẵng là thành phố du lịch"]


model = SentenceTransformer('dangvantuan/vietnamese-embedding-LongContext', trust_remote_code=True)
embeddings = model.encode(sentences)
print(embeddings)

Evaluation

The model can be evaluated as follows on the Vienamese data of stsb.

from sentence_transformers import SentenceTransformer
from sentence_transformers.readers import InputExample
from datasets import load_dataset
def convert_dataset(dataset):
    dataset_samples=[]
    for df in dataset:
        score = float(df['score'])/5.0  # Normalize score to range 0 ... 1
        inp_example = InputExample(texts=[df['sentence1'], df['sentence2']], label=score)
        dataset_samples.append(inp_example)
    return dataset_samples

# Loading the dataset for evaluation
vi_sts = load_dataset("doanhieung/vi-stsbenchmark")["train"]
df_dev = vi_sts.filter(lambda example: example['split'] == 'dev')
df_test = vi_sts.filter(lambda example: example['split'] == 'test')

# Convert the dataset for evaluation

# For Dev set:
dev_samples = convert_dataset(df_dev)
val_evaluator = EmbeddingSimilarityEvaluator.from_input_examples(dev_samples, name='sts-dev')
val_evaluator(model, output_path="./")

# For Test set:
test_samples = convert_dataset(df_test)
test_evaluator = EmbeddingSimilarityEvaluator.from_input_examples(test_samples, name='sts-test')
test_evaluator(model, output_path="./")

Metric for all dataset of Semantic Textual Similarity on STS Benchmark

Spearman score

Model	[STSB]	[STS12]	[STS13]	[STS14]	[STS15]	[STS16]	[SICK]	Mean
dangvantuan/vietnamese-embedding	84.84	79.04	85.30	81.38	87.06	79.95	79.58	82.45
dangvantuan/vietnamese-embedding-LongContext	85.25	75.77	83.82	81.69	88.48	81.5	78.2	82.10

Citation

@article{reimers2019sentence,
   title={Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks},
   author={Nils Reimers, Iryna Gurevych},
   journal={https://arxiv.org/abs/1908.10084},
   year={2019}
}


@article{zhang2024mgte,
  title={mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval},
  author={Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Wen and Dai, Ziqi and Tang, Jialong and Lin, Huan and Yang, Baosong and Xie, Pengjun and Huang, Fei and others},
  journal={arXiv preprint arXiv:2407.19669},
  year={2024}
}

@article{li2023towards,
  title={Towards general text embeddings with multi-stage contrastive learning},
  author={Li, Zehan and Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan},
  journal={arXiv preprint arXiv:2308.03281},
  year={2023}
}

@article{li20242d,
  title={2d matryoshka sentence embeddings},
  author={Li, Xianming and Li, Zongxi and Li, Jing and Xie, Haoran and Li, Qing},
  journal={arXiv preprint arXiv:2402.14776},
  year={2024}
}