言語から画像、オープンウェイトからAPIまで。能力と実際の費用を比較。
次のアイデアに合うAIを見つけましょう。
The code is distributed under the CC BY-NC-SA 4.0 License. See LICENSE for more information. For the checkpoints, make sure to agree to the license of all the public training datasets and base checkpoints we used, in addition to CC-BY-NC-SA 4.0. See section: Our Hyperparameters for details. Gihub page: https://github.com/naver/dust3r/ Project page: https://dust3r.europe.naverlabs.com/ First, install dust3r. To load the model:
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Efficient SPLADE model for passage retrieval. This architecture uses two distinct models for query and document inference. This is the **doc** one, please also download the **query** one (https://huggingface.co/naver/efficient-splade-VI-BT-large-query). For additional details, please visit: * paper: https://dl.acm.org/doi/10.1145/3477495.3531833 * code: https://github.com/naver/splade | | MRR@10 (MS MARCO dev) | R@1000 (MS MARCO dev) | Latency (PISA) ms | Latency (Inference) ms | --- | --- | --- | --- | --- | | `naver/efficient-splade-V-large` | 38.8 | 98.0 | 29.0 | 45.3 | `naver/efficient-splade-VI-BT-large` | 38.0 | 97.8 | 31.1 | 0.7 This is a Asymmetric SPLADE Sparse Encoder model. It maps sentences & paragraphs to a 30522-dimensional sparse vector space and can be used for semantic search and sparse retrieval. - **Model Type:** SPLADE Sparse Encoder - **Maximum Sequence Length:** 512 tokens (256 for evaluation reproduction) - **Output Dimensionality:** 30522 dimensions - **Similarity Function:** Dot Product
翻訳待ち・原文を表示
Efficient SPLADE model for passage retrieval. This architecture uses two distinct models for query and document inference. This is the **query** one, please also download the **doc** one (https://huggingface.co/naver/efficient-splade-VI-BT-large-doc). For additional details, please visit: * paper: https://dl.acm.org/doi/10.1145/3477495.3531833 * code: https://github.com/naver/splade | | MRR@10 (MS MARCO dev) | R@1000 (MS MARCO dev) | Latency (PISA) ms | Latency (Inference) ms | --- | --- | --- | --- | --- | | `naver/efficient-splade-V-large` | 38.8 | 98.0 | 29.0 | 45.3 | `naver/efficient-splade-VI-BT-large` | 38.0 | 97.8 | 31.1 | 0.7 This is a Asymmetric SPLADE Sparse Encoder model. It maps sentences & paragraphs to a 30522-dimensional sparse vector space and can be used for semantic search and sparse retrieval. - **Model Type:** SPLADE Sparse Encoder - **Maximum Sequence Length:** 512 tokens (256 for evaluation reproduction) - **Output Dimensionality:** 30522 dimensions - **Similarity Function:** Dot Product
翻訳待ち・原文を表示
The code is distributed under the CC BY-NC-SA 4.0 License. See LICENSE for more information. For the checkpoints, make sure to agree to the license of all the public training datasets and base checkpoints we used, in addition to CC-BY-NC-SA 4.0. The mapfree dataset license in particular is very restrictive. For more information, check CHECKPOINTS_NOTICE. First, install mast3r. To load the model:
翻訳待ち・原文を表示
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。
基本情報を収録・詳細は順次追加
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。
SPLADE model for passage retrieval. For additional details, please visit: * paper: https://arxiv.org/abs/2205.04733 * code: https://github.com/naver/splade This is a SPLADE Sparse Encoder model. It maps sentences & paragraphs to a 30522-dimensional sparse vector space and can be used for semantic search and sparse retrieval. - **Model Type:** SPLADE Sparse Encoder - **Base model:** Luyu/co-condenser-marco - **Maximum Sequence Length:** 512 tokens (256 for evaluation reproduction) - **Output Dimensionality:** 30522 dimensions - **Similarity Function:** Dot Product
翻訳待ち・原文を表示
SPLADE model for passage retrieval. For additional details, please visit: * paper: https://arxiv.org/abs/2205.04733 * code: https://github.com/naver/splade This is a SPLADE Sparse Encoder model. It maps sentences & paragraphs to a 30522-dimensional sparse vector space and can be used for semantic search and sparse retrieval. - **Model Type:** SPLADE Sparse Encoder - **Base model:** Luyu/co-condenser-marco - **Maximum Sequence Length:** 512 tokens (256 for evaluation reproduction) - **Output Dimensionality:** 30522 dimensions - **Similarity Function:** Dot Product
翻訳待ち・原文を表示
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。
基本情報を収録・詳細は順次追加
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。
基本情報を収録・詳細は順次追加
SPLADE model for passage retrieval. For additional details, please visit: * paper: https://arxiv.org/abs/2109.10086 * code: https://github.com/naver/splade This is a SPLADE Sparse Encoder model. It maps sentences & paragraphs to a 30522-dimensional sparse vector space and can be used for semantic search and sparse retrieval. - **Model Type:** SPLADE Sparse Encoder - **Base model:** distilbert/distilbert-base-uncased - **Maximum Sequence Length:** 512 tokens (256 for evaluation reproduction) - **Output Dimensionality:** 30522 dimensions - **Similarity Function:** Dot Product
翻訳待ち・原文を表示
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。
For more details, see our arXiv companion book: https://arxiv.org/abs/2403.06789 To use SPLADE, please visit our GitHub repository: https://github.com/naver/splade This is a SPLADE Sparse Encoder model. It maps sentences & paragraphs to a 30522-dimensional sparse vector space and can be used for semantic search and sparse retrieval.
翻訳待ち・原文を表示
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。
基本情報を収録・詳細は順次追加
提供元・分類・入出力の条件を変更してください。新しいモデルを随時追加しています。
月間トークン数を調整してAPI費用を比較。
収集済みの比較可能な100万トークン当たり米ドル料金です。キャッシュ割引、段階料金、追加推論出力、税金は含みません。最新料金は出典で確認してください。
入力・出力・キャッシュ料金を分け、同じ用途と使用量で総費用を比較します。画像・音声・動画は枚数、時間、解像度で課金される場合があります。
必ずしもそうではありません。バージョンごとのライセンス、商用制限、ポリシー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。
順位はテスト、バージョン、評価方法によります。実際の業務データで精度、遅延、安定性、費用を比較してください。
