モデル情報

stella-pl-retrieval-8k

sdadas · stella-pl-retrieval-8k

This is an embedding model based on stella_en_1.5B_v5 and further fine-tuned for retrieval tasks in Polish. It transforms texts into 1024-dimensional vectors. The model training consisted of two stages: - In the first stage, we adapted the model to support the Polish language using the multilingual knowledge distillation method, leveraging a diverse corpus of 20 million Polish-English text pairs. - The original Stella model and the output of the first stage were limited to a short context of 512 tokens. In the second stage, we extended the context to 8192 tokens and then fine-tuned the model using contrastive loss on a dataset comprising 1.5 million queries. Positive and negative passages for each query have been selected with the help of BAAI/bge-reranker-v2.5-gemma2-lightweight reranker. The model was trained for three epochs with a batch size of 1024 queries. For retrieval, queries should be prefixed with **"Instruct: Given a web search query, retrieve relevant passages that answer the query.\nQuery: "**. For symmetric tasks such as semantic similarity, both texts should be prefixed with **"Instruct: Retrieve semantically similar text.\nQuery: "**.

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情報更新日時2026/11/2 19:15:48
OVERVIEW

モデル紹介

This is an embedding model based on stella_en_1.5B_v5 and further fine-tuned for retrieval tasks in Polish. It transforms texts into 1024-dimensional vectors. The model training consisted of two stages: - In the first stage, we adapted the model to support the Polish language using the multilingual knowledge distillation method, leveraging a diverse corpus of 20 million Polish-English text pairs. - The original Stella model and the output of the first stage were limited to a short context of 512 tokens. In the second stage, we extended the context to 8192 tokens and then fine-tuned the model using contrastive loss on a dataset comprising 1.5 million queries. Positive and negative passages for each query have been selected with the help of BAAI/bge-reranker-v2.5-gemma2-lightweight reranker. The model was trained for three epochs with a batch size of 1024 queries. For retrieval, queries should be prefixed with **"Instruct: Given a web search query, retrieve relevant passages that answer the query.\nQuery: "**. For symmetric tasks such as semantic similarity, both texts should be prefixed with **"Instruct: Retrieve semantically similar text.\nQuery: "**.

主な特徴

特点摘要待依据来源补齐,不凭模型名称推断能力。

制約と注意点

具体的な制約を判断する出典が不足しています。実際の性能、遅延、データ方針、ライセンス、提供期間を確認してください。

PRICING & COST

出典別料金と費用分析

料金はチャネル・地域・設定によって異なります。各チャネルの料金は個別に表示し、提供元の統一公式料金とは見なしません。

出典・チャネル入力出力課金単位収集日時
確認可能な料金がありません。出典を確認してください。

月額の基本トークン費用を試算

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試算はキャッシュ割引、段階料金、マルチモーダル追加料金、検索、税金、追加推論出力を含みません。

INDEPENDENT BENCHMARKS

公開評価と実行設定

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。

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よくある質問

オープンウェイトなら無料で商用利用できますか?

必ずしもそうではありません。バージョン別のライセンス、商用条件、利用方針を確認してください。自前運用には計算資源と保守費用も必要です。

これはリアルタイムの請求料金ですか?

収集日時付きの料金スナップショットです。最新のバージョン、地域、割引、課金方式は各チャネルで確認してください。

出典と追跡記録

许可证:gemma。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。