SentenceTransformer This is a sentence transformers model trained on the train set dataset. It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. Model Details Learning other languages besides Chinese and English is insufficient, so additional learning is needed to optimize use of other languages. This model is additionally trained on the Korean dataset. Model Description Model Type: Sentence Transformer Transformer Encoder Maximum Sequence Length: 8192 tokens Output Dimensionality: 1024 tokens Similarity Function: Cosine Similarity Model Sources Documentation: Sentence Transformers Documentation Repository: Sentence Transformers on GitHub Hugging Face: Sentence Transformers on Hugging Face Full Model Architecture Usage Direct Usage (Sentence Transformers) First install the Sentence Transformers library: Then you can load this model and run inference. Click to see the direct usage in Transformers Click to expand Evaluation Metrics ndcg, mrr, map metrics are metrics that consider ranking, while accuracy, precision, and recall are metrics…
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