vietnamese-embedding

Model overview

**vietnamese-embedding** is the Embedding Model for Vietnamese language. This model is a specialized sentence-embedding trained specifically for the Vietnamese language, leveraging the robust capabilities of PhoBERT, a pre-trained language model based on the RoBERTa architecture. The model utilizes PhoBERT to encode Vietnamese sentences into a 768-dimensional vector space, facilitating a wide range of applications from semantic search to text clustering. The embeddings capture the nuanced meanings of Vietnamese sentences, reflecting both the lexical and contextual layers of the language. 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: - Dataset: ViNLI-SimCSE-supervised - Method: Trained using the SimCSE approach which employs a supervised contrastive learning framework. The model was optimized using Triplet Loss to effectively learn from high-quality annotated sentence pairs. - Dataset: XNLI-vn - Method: Continued fine-tuning using Multi-Negative Ranking Loss. This stage focused on improving the model's ability to discern and rank nuanced differences in sentence semantics. - 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. - Dataset: STSB-vn with generate silver sample from gold sample - Method: Employed an advanced strategy using Augmented SBERT with Pair Sampling Strategies, integrating both Cross-Encoder and Bi-Encoder models. This stage further refined the embeddings by enriching the training data dynamically, enhancing the model's robustness and accuracy in understanding and processing complex Vietnamese language constructs. The performance is measured using Pearson and Spearman correlation: - On dev | Model | Pearson correlation | Spearman correlation | #params | | ------------- | ------------- | ------------- |------------

Key features

特点摘要待依据来源补齐，不凭模型名称推断能力。

Limitations & considerations

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Source prices & cost analysis

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Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

Frequently asked questions

Sources & traceability

许可证：apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。