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。
ONNX port of sentence-transformers/all-MiniLM-L6-v2 for text classification and similarity searches. Here's an example of performing inference using the model with FastEmbed.
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ONNX port of sentence-transformers/all-MiniLM-L6-v2 adjusted to return attention weights. This model is intended to be used for BM42 searches. Here's an example of performing inference using the model with FastEmbed.
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Quantized ONNX port of BAAI/bge-base-en-v1.5 for text classification and similarity searches. Here's an example of performing inference using the model with FastEmbed.
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ONNX port of BAAI/bge-large-en-v1.5 for text classification and similarity searches. Here's an example of performing inference using the model with FastEmbed.
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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。
Quantized ONNX port of BAAI/bge-small-en-v1.5 for text classification and similarity searches. Here's an example of performing inference using the model with FastEmbed.
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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。
Repository with files to perform BM25 searches with FastEmbed. > Note: This model is supposed to be used with Qdrant. Vectors have to be configured with Modifier.IDF.
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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。
ONNX port of sentence-transformers/clip-ViT-B-32. This model is intended to be used for image classification and similarity searches. Here's an example of performing inference using the model with FastEmbed.
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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
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
It creates 4-dimensional embeddings for each word stem, capturing the word's meaning. These meaning embeddings are combined into a bag-of-words (BoW) representation of the input text. The final sparse representation is calculated by weighting each word using the BM25 scoring formula. In the case of a word's absence in the miniCOIL vocabulary, word weight in sparse representation is purely based on the BM25 score. > Note: This model was designed with Qdrant's specifics in mind; miniCOIL sparse vectors in Qdrant have to be configured with Modifier.IDF. Otherwise, you'll have to personally calculate & scale the produced sparse representations by the IDF part of the BM25 formula.
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ONNX port of intfloat/multilingual-e5-large for text classification and similarity searches. Here's an example of performing inference using the model with FastEmbed.
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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。
Quantized ONNX port of sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 for text classification and similarity searches. Here's an example of performing inference using the model with FastEmbed.
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This repository contains an ONNX export of `Qwen/Qwen3-Embedding-0.6B`, converted by Qdrant for inference with FastEmbed. It encodes text into 1024-dimensional vectors, supports over 100 languages, and accepts up to 32768 input tokens. Embeddings are produced by taking the hidden state of the last non-padding token and L2-normalizing it, so the similarity score is the dot product of two embeddings. The quantized file stores its weights as 8-bit `MatMulNBits` and therefore requires `onnxruntime>=1.23`. Earlier versions only implement the 4-bit kernel and will fail to load it.
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This model is intended to be used for image classification and similarity searches. You can find the ONNX port implementation here Here's an example of performing inference using the model with FastEmbed.
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ONNX port of prithivida/Splade_PP_en_v1 for text classification and similarity searches. Here's an example of performing inference using the model with FastEmbed.
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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。
