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gte-small

Supabase

_Fork of https://huggingface.co/thenlper/gte-small with ONNX weights to be compatible with Transformers.js. See JavaScript usage._ The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including GTE-large, GTE-base, and GTE-small. The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including **information retrieval**, **semantic textual similarity**, **text reranking**, etc. Performance of GTE models were compared with other popular text embedding models on the MTEB benchmark. For more detailed comparison results, please refer to the MTEB leaderboard.

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Monthly input tokens 10 MMonthly output tokens 2 M1 M = 1 million tokens · USD
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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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How should API prices be compared?+

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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gte-small

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