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bge-m3-korean

upskyy

This model is korsts and kornli finetuning model from BAAI/bge-m3. 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 Type:** Sentence Transformer - **Base model:** BAAI/bge-m3 - **Maximum Sequence Length:** **8192 tokens** - **Output Dimensionality:** 1024 tokens - **Similarity Function:** Cosine Similarity Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.

Embeddings & similarity

Input Text→ Output Embedding

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ko-reranker-8k

upskyy

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

Basic profile indexed · More details to follow

Category pending

Input Not verified→ Output Not verified

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komos

upskyy

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

Category pending

Input Not verified→ Output Not verified

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Cost analysis

Adjust monthly token usage to compare estimated API costs.

Monthly input tokens 10 MMonthly output tokens 2 M1 M = 1 million tokens · USD
Compares base text-token rates within the same channel

Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.

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Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.

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Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.

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Explore AI models 3

bge-m3-korean

ko-reranker-8k

komos

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