AI NEWS.ccAIが動く、未来が変わる
♡ 保存済み 0
AI NEWS・モデル情報能力を知り、根拠ある選択を。 ↗基本情報を先に収録・詳細を随時追加
THE AI MODEL DIRECTORY ここからモデルを探す

あなたに合う AIモデルを。

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。

人気の検索
84,991 件のモデル13202 社の提供元・公開者45 件の機能分類465件の比較可能な料金・30件の公開評価
Tテキスト生成▧画像生成▷動画生成⌘コード生成◖音声合成♫音楽生成⬡3Dモデリング▤文書分析
DISCOVER & COMPARE

AIモデルを探す 84991

注目のモデルを見つける

**CAMeLBERT-DA SA Model** is a Sentiment Analysis (SA) model that was built by fine-tuning the CAMeLBERT Dialectal Arabic (DA) model. For the fine-tuning, we used the ASTD, ArSAS, and SemEval datasets. Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models." * Our fine-tuning code can be found here. You can use the CAMeLBERT-DA SA model directly as part of our CAMeL Tools SA component (*recommended*) or as part of the transformers pipeline. To use the model with the CAMeL Tools SA component: *Note*: to download our models, you would need `transformers>=3.5.0`. Otherwise, you could download the models manually.

翻訳待ち・原文を表示

テキスト分析
入力 テキスト→ 出力 テキスト
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 ↗
出典・文書 ↗

**CAMeLBERT** is a collection of BERT models pre-trained on Arabic texts with different sizes and variants. We release pre-trained language models for Modern Standard Arabic (MSA), dialectal Arabic (DA), and classical Arabic (CA), in addition to a model pre-trained on a mix of the three. We also provide additional models that are pre-trained on a scaled-down set of the MSA variant (half, quarter, eighth, and sixteenth). The details are described in the paper *"The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models."* This model card describes **CAMeLBERT-Mix** (`bert-base-arabic-camelbert-mix`), a model pre-trained on a mixture of these variants: MSA, DA, and CA. You can use the released model for either masked language modeling or next sentence prediction. However, it is mostly intended to be fine-tuned on an NLP task, such as NER, POS tagging, sentiment analysis, dialect identification, and poetry classification. We release our fine-tuninig code here.

翻訳待ち・原文を表示

穴埋め・マスク
入力 テキスト→ 出力 テキスト
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 ↗
出典・文書 ↗

**CAMeLBERT-Mix NER Model** is a Named Entity Recognition (NER) model that was built by fine-tuning the CAMeLBERT Mix model. For the fine-tuning, we used the ANERcorp dataset. Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models. "* Our fine-tuning code can be found here. You can use the CAMeLBERT-Mix NER model directly as part of our CAMeL Tools NER component (*recommended*) or as part of the transformers pipeline. *Note*: to download our models, you would need `transformers>=3.5.0`. Otherwise, you could download the models manually.

翻訳待ち・原文を表示

テキスト分析
入力 テキスト→ 出力 テキスト
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 ↗
出典・文書 ↗

**CAMeLBERT Mix SA Model** is a Sentiment Analysis (SA) model that was built by fine-tuning the CAMeLBERT Mix model. For the fine-tuning, we used the ASTD, ArSAS, and SemEval datasets. Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models."* Our fine-tuning code can be found here. You can use the CAMeLBERT Mix SA model directly as part of our CAMeL Tools SA component (*recommended*) or as part of the transformers pipeline. To use the model with the CAMeL Tools SA component: *Note*: to download our models, you would need `transformers>=3.5.0`. Otherwise, you could download the models manually.

翻訳待ち・原文を表示

テキスト分析
入力 テキスト→ 出力 テキスト
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 ↗
出典・文書 ↗

**CAMeLBERT** is a collection of BERT models pre-trained on Arabic texts with different sizes and variants. We release pre-trained language models for Modern Standard Arabic (MSA), dialectal Arabic (DA), and classical Arabic (CA), in addition to a model pre-trained on a mix of the three. We also provide additional models that are pre-trained on a scaled-down set of the MSA variant (half, quarter, eighth, and sixteenth). The details are described in the paper *"The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models."* This model card describes **CAMeLBERT-MSA** (`bert-base-arabic-camelbert-msa`), a model pre-trained on the entire MSA dataset. You can use the released model for either masked language modeling or next sentence prediction. However, it is mostly intended to be fine-tuned on an NLP task, such as NER, POS tagging, sentiment analysis, dialect identification, and poetry classification. We release our fine-tuninig code here.

翻訳待ち・原文を表示

穴埋め・マスク
入力 テキスト→ 出力 テキスト
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 ↗
出典・文書 ↗

**CAMeLBERT MSA NER Model** is a Named Entity Recognition (NER) model that was built by fine-tuning the CAMeLBERT Modern Standard Arabic (MSA) model. For the fine-tuning, we used the ANERcorp dataset. Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models. "* Our fine-tuning code can be found here. You can use the CAMeLBERT MSA NER model directly as part of our CAMeL Tools NER component (*recommended*) or as part of the transformers pipeline. To use the model with the CAMeL Tools NER component:

翻訳待ち・原文を表示

テキスト分析
入力 テキスト→ 出力 テキスト
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 ↗
出典・文書 ↗

AnimateDiff

camenduru

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。

分類準備中
入力 未確認→ 出力 未確認
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 ↗
出典・文書 ↗

DINOv3 is a family of versatile vision foundation models that outperforms the specialized state of the art across a broad range of settings, without fine-tuning. DINOv3 produces high-quality dense features that achieve outstanding performance on various vision tasks, significantly surpassing previous self- and weakly-supervised foundation models. These are Vision Transformer and ConvNeXt models trained following the method described in the DINOv3 paper. 12 models are provided: - 10 models pretrained on web data (LVD-1689M dataset) - 1 ViT-7B trained from scratch, - 5 ViT-S/S+/B/L/H+ models distilled from the ViT-7B, - 4 ConvNeXt-{T/S/B/L} models distilled from the ViT-7B, - 2 models pretrained on satellite data (SAT-493M dataset) - 1 ViT-7B trained from scratch - 1 ViT-L distilled from the ViT-7B

翻訳待ち・原文を表示

分類準備中
入力 未確認→ 出力 未確認
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 ↗
出典・文書 ↗

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

分類準備中
入力 未確認→ 出力 未確認
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 ↗
出典・文書 ↗

IDM-VTON-F16

camenduru

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

画像編集
入力 画像→ 出力 画像
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 ↗
出典・文書 ↗

TRELLIS.2-4B

camenduru

**TRELLIS.2** is a state-of-the-art large 3D generative model designed for high-fidelity **image-to-3D** generation. It leverages a novel "field-free" sparse voxel structure termed **O-Voxel** and a large-scale flow-matching transformer (4 Billion parameters). Unlike previous methods that rely on iso-surface fields (e.g., SDF, Flexicubes) which struggle with open surfaces or non-manifold geometry, TRELLIS can reconstruct and generate **arbitrary 3D assets** with complex topologies, sharp features, and full Physical-Based Rendering (PBR) materials—including transparency/translucency. *Note: Please refer to the official GitHub Repository for installation instructions and dependencies.*

翻訳待ち・原文を表示

分類準備中
入力 未確認→ 出力 未確認
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 ↗
出典・文書 ↗

Poseidon-B

camlab-ethz

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

分類準備中
入力 未確認→ 出力 未確認
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 ↗
出典・文書 ↗
COST EXPLORER

モデル選びに、費用の視点も。

コスト分析

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

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

選び方とよくある質問
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。

AI NEWS・情報から選択へ

ニュースで知り、ツールで効率化、モデルで可能性を。

AIニュースを読む ↗AIツールを探す ↗