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. 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. Momentum Encoder: Incorporates MoCo (Momentum Contrast) to enhance in-batch negative sampling. 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. Below is a comparision table of the results I achieved compared…
Independent publisher
Dang Phuong Nam
namdp-ptit
Models in Library1
Datasets in Library0
Models on Hugging Face2
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Published tasks: Sentence similarity.
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