vietnamese-document-embedding EU AI Act Compliance Profile

Model Description:

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.

Full Model Architecture

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 t

Your obligations as a vietnamese-document-embedding deployer

Track your progress

Sign up to track obligations, receive deadline reminders, and generate audit packages.

Tags

Languages

Library