huydang-dek21-embedding-v2
CODE4LIFEOFFICIAL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
入力 テキスト→ 出力 埋め込み
料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗DISCOVER & COMPARE
AIモデルを探す 2043
C2LLM-0.5B
codefuse-ai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
入力 テキスト→ 出力 埋め込み
料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗C2LLM-7B
codefuse-ai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
入力 テキスト→ 出力 埋め込み
料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗F2LLM-0.6B
codefuse-ai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
入力 テキスト→ 出力 埋め込み
料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗F2LLM-1.7B
codefuse-ai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
入力 テキスト→ 出力 埋め込み
料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗F2LLM-4B
codefuse-ai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
入力 テキスト→ 出力 埋め込み
料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗F2LLM-v2-0.6B
codefuse-aiF2LLM-v2 is a family of general-purpose, multilingual embedding models in 8 distinct sizes ranging from 80M to 14B. Trained on a curated composite of 60 million publicly available high-quality data, F2LLM-v2 supports more than 200 languages, with a particular emphasis on previously underserved mid- and low-resource languages. F2LLM-v2 is fully open. We release base models in 5 sizes, instruct models in 8 sizes, the training data, the training code, and intermediate checkpoints. The three smallest instruct models are pruned and trained from the 0.6B base model. The F2LLM-v2 family set a new state-of-the-art on a wide range of MTEB benchmarks, including Code, European, Scandinavian, German, French, Spanish, Polish, Dutch, Japanese, Vietnamese, Thai, Indic, Persian, among others.
翻訳待ち・原文を表示
入力 テキスト→ 出力 埋め込み
料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗F2LLM-v2-1.7B
codefuse-aiF2LLM-v2 is a family of general-purpose, multilingual embedding models in 8 distinct sizes ranging from 80M to 14B. Trained on a curated composite of 60 million publicly available high-quality data, F2LLM-v2 supports more than 200 languages, with a particular emphasis on previously underserved mid- and low-resource languages. F2LLM-v2 is fully open. We release base models in 5 sizes, instruct models in 8 sizes, the training data, the training code, and intermediate checkpoints. The three smallest instruct models are pruned and trained from the 0.6B base model. The F2LLM-v2 family set a new state-of-the-art on a wide range of MTEB benchmarks, including Code, European, Scandinavian, German, French, Spanish, Polish, Dutch, Japanese, Vietnamese, Thai, Indic, Persian, among others.
翻訳待ち・原文を表示
入力 テキスト→ 出力 埋め込み
料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗F2LLM-v2-14B
codefuse-ai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。
入力 テキスト→ 出力 埋め込み
料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗F2LLM-v2-160M
codefuse-ai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
入力 テキスト→ 出力 埋め込み
料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗F2LLM-v2-330M
codefuse-aiF2LLM-v2 is a family of general-purpose, multilingual embedding models in 8 distinct sizes ranging from 80M to 14B. Trained on a curated composite of 60 million publicly available high-quality data, F2LLM-v2 supports more than 200 languages, with a particular emphasis on previously underserved mid- and low-resource languages. F2LLM-v2 is fully open. We release base models in 5 sizes, instruct models in 8 sizes, the training data, the training code, and intermediate checkpoints. The three smallest instruct models are pruned and trained from the 0.6B base model. The F2LLM-v2 family set a new state-of-the-art on a wide range of MTEB benchmarks, including Code, European, Scandinavian, German, French, Spanish, Polish, Dutch, Japanese, Vietnamese, Thai, Indic, Persian, among others.
翻訳待ち・原文を表示
入力 テキスト→ 出力 埋め込み
料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗F2LLM-v2-4B
codefuse-aiF2LLM-v2 is a family of general-purpose, multilingual embedding models in 8 distinct sizes ranging from 80M to 14B. Trained on a curated composite of 60 million publicly available high-quality data, F2LLM-v2 supports more than 200 languages, with a particular emphasis on previously underserved mid- and low-resource languages. F2LLM-v2 is fully open. We release base models in 5 sizes, instruct models in 8 sizes, the training data, the training code, and intermediate checkpoints. The three smallest instruct models are pruned and trained from the 0.6B base model. The F2LLM-v2 family set a new state-of-the-art on a wide range of MTEB benchmarks, including Code, European, Scandinavian, German, French, Spanish, Polish, Dutch, Japanese, Vietnamese, Thai, Indic, Persian, among others.
翻訳待ち・原文を表示
入力 テキスト→ 出力 埋め込み
料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗F2LLM-v2-80M
codefuse-aiF2LLM-v2 is a family of general-purpose, multilingual embedding models in 8 distinct sizes ranging from 80M to 14B. Trained on a curated composite of 60 million publicly available high-quality data, F2LLM-v2 supports more than 200 languages, with a particular emphasis on previously underserved mid- and low-resource languages. F2LLM-v2 is fully open. We release base models in 5 sizes, instruct models in 8 sizes, the training data, the training code, and intermediate checkpoints. The three smallest instruct models are pruned and trained from the 0.6B base model. The F2LLM-v2 family set a new state-of-the-art on a wide range of MTEB benchmarks, including Code, European, Scandinavian, German, French, Spanish, Polish, Dutch, Japanese, Vietnamese, Thai, Indic, Persian, among others.
翻訳待ち・原文を表示
入力 テキスト→ 出力 埋め込み
料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗F2LLM-v2-8B
codefuse-ai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
入力 テキスト→ 出力 埋め込み
料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗FLM-2-52B-Instruct-2407
CofeAI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
入力 テキスト→ 出力 埋め込み
料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗Tele-FLM
CofeAITele-FLM (aka FLM-2) is a 52B open-sourced multilingual large language model that features a stable, efficient pre-training paradigm and enhanced factual judgement capabilities. Built upon the decoder-only transformer architecture, it has been trained on approximately 2T tokens. Tele-FLM demonstrates superior performances at its scale, and sometimes surpass larger models. In addition to sharing the model weights, we provide the core designs, engineering practices, and training details, anticipating their benefits for both academic and industrial communities. - **Developed by:** BAAI & TeleAI - **Language(s):** English; Chinese; Other languages - **License:** Apache 2.0 Although we've made extensive efforts to thoroughly clean and filter the training corpus for the model, due to the open nature of the dataset, the model may still have picked up on some unsafe examples. Consequently, the model may still generate unexpected content, including but not limited to discrimination, bias, or offensive language. We would like to strongly advise users not to spread any unsafe content generated by the model. The project developers cannot be held responsible for any repercussions stemming from the dissemination of harmful information.
翻訳待ち・原文を表示
入力 テキスト→ 出力 埋め込み
料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗https://huggingface.co/jinaai/jina-embeddings-v2-base-en with ONNX weights to be compatible with Transformers.js.
翻訳待ち・原文を表示
入力 テキスト→ 出力 埋め込み
料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗LaBSE-en-ru
cointegratedThis is a truncated version of sentence-transformers/LaBSE, which is, in turn, a port of LaBSE by Google. The current model has only English and Russian tokens left in the vocabulary. Thus, the vocabulary is 10% of the original, and number of parameters in the whole model is 27% of the original, without any loss in the quality of English and Russian embeddings. The model has been truncated in this notebook. You can adapt it for other languages (like EIStakovskii/LaBSE-fr-de), models or datasets.
翻訳待ち・原文を表示
入力 テキスト→ 出力 埋め込み
料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗rubert-tiny2
cointegratedThis is an updated version of cointegrated/rubert-tiny: a small Russian BERT-based encoder with high-quality sentence embeddings. This post in Russian gives more details. The differences from the previous version include: - a larger vocabulary: 83828 tokens instead of 29564; - larger supported sequences: 2048 instead of 512; - sentence embeddings approximate LaBSE closer than before; - meaningful segment embeddings (tuned on the NLI task) - the model is focused only on Russian. The model should be used as is to produce sentence embeddings (e.g. for KNN classification of short texts) or fine-tuned for a downstream task.
翻訳待ち・原文を表示
入力 テキスト→ 出力 埋め込み
料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗SONAR_200_text_encoder
cointegratedThis is a port of the multilingual SONAR text encoder (https://huggingface.co/facebook/SONAR) to the `transformers` format from `fairseq2`. Its embeddings are expected be equal to those the official implementation (https://github.com/facebookresearch/SONAR), but the latter stays the source of truth. The encoder supports the same 202 languages as NLLB-200 (see also the source model card and FLORES-200 lang code mapping).
翻訳待ち・原文を表示
入力 テキスト→ 出力 埋め込み
料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗miewid-msv2
conservationxlabsMiewID-msv2 is a feature extractor trained for re-identification using contrastive learning on a large, high-quality dataset of 54 wildlife species - terrestrial and aquatic - including fins, flukes, flanks, faces. Intended use is re-identification of individuals from different species by matching against a database of grount-truth samples. Model features can also be used for species classification by retrieval. View more usage examples at https://github.com/WildMeOrg/wbia-plugin-miew-id/tree/main/wbia_miew_id/examples
翻訳待ち・原文を表示
入力 テキスト→ 出力 埋め込み
料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗miewid-msv3
conservationxlabsMiewID-msv3 is a feature extractor trained for re-identification using contrastive learning on a large, high-quality dataset of 64 wildlife species - terrestrial and aquatic - including fins, flukes, flanks, faces. - **Model Type:** Wildlife re-identification feature backbone - **Model Stats:** - Params (M): 51.11 - GMACs: 24.38 - Activations (M): 91.11 - Image size: 440 x 440 - **Repository:** https://github.com/WildMeOrg/wbia-plugin-miew-id - **Backbone:** https://huggingface.co/timm/efficientnetv2_rw_m.agc_in1k
翻訳待ち・原文を表示
入力 テキスト→ 出力 埋め込み
料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗halong_embedding
contextboxai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
入力 テキスト→ 出力 埋め込み
料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗MyAwesomeModel-TestRepo
contextcup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
入力 テキスト→ 出力 埋め込み
料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗該当するモデルがありません
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。
COST EXPLORER
モデル選びに、費用の視点も。
月間トークン数を調整してAPI費用を比較。
1 M = 100万トークン・米ドル
同一チャネルの基本テキスト料金を比較
同一チャネルの基本テキスト料金を比較
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。
MODEL HANDBOOK
モデルを理解して選ぶ
API料金はどう比較しますか？+
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。
オープンウェイトは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。
ランキング1位が最適ですか？+
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。