dangvantuan/vietnamese-document-embedding
sentence-similarity model by dangvantuan
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Model card & documentation
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vietnamese-document-embedding is the Document Embedding Model for Vietnamese language with context length up to 8096 tokens. This model is a specialized long 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. 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…
Read the full model card ↗ · Preview checked 2026-10-31T10:34:21.060Z
Inside the original model card — Document outline
- Model Description:
- Full Model Architecture
- Training and Fine-tuning process
- Stage 1: Training NLI on dataset XNLI:
- Stage 2: Fine-tuning for Semantic Textual Similarity on STS Benchmark
- Usage:
- Evaluation
- Metric for all dataset of Semantic Textual Similarity on STS Benchmark
- Citation
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Documentation belongs to its respective authors. Reported project/model license: apache-2.0. A listing is not a grant of reuse or training rights. Confirm the document’s own terms at the source.
Model overview
dangvantuan/vietnamese-document-embedding is a sentence-similarity model repository published by dangvantuan on Hugging Face. The source reports the sentence-transformers library.
This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.
Model facts
- Task
- sentence-similarity
- Library
- sentence-transformers
- Recent downloads (30 days)
- 122,594
- Cumulative likes
- 29
- Hugging Face trending score
- 0
- Reported safetensors parameters
- 305,368,320
- Architecture
- VietnameseModel
- License
- apache-2.0
- Access
- Not gated by Hugging Face
- Created
- 2024-08-15T05:32:04.000Z
- Last modified
- 2024-11-22T13:56:46.000Z
Compatibility and lineage
Reported languages: vi
Base-model lineage was not included.
Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.
Use and evaluate
Check license terms and access requirements first. Review the model card’s documented loading instructions, evaluations and safety limitations. Benchmark scores are not imported by this directory, and popularity is not an accuracy ranking.
No model weights are downloaded or executed by AltAPIs. Never enable remote model code without reviewing it.
Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-10-03T06:40:36.316Z. Daily imports are snapshots, not real-time monitoring.
Popularity and source listings do not establish security, suitability, licensing rights or benchmark performance.
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