AI NEWS.ccAI is happening. The future is updating.
♡ Saved tools 0
AI NEWS · Model intelligenceExplore capabilities. Make informed choices. ↗Profiles indexed first · Details continuously enriched
THE AI MODEL DIRECTORY Start exploring models here

Find your ideal AI model.

From language to images, open weights to APIs. Understand capabilities and compare real costs.
Find the right engine for your next idea.

Popular searches
84,999 model profiles13202 providers / publishers45 capability categories465 with comparable prices · 30 benchmarked
TText generation▧Image generation▷Video generation⌘Code generation◖Speech synthesis♫Music generation⬡3D modeling▤Document analysis
DISCOVER & COMPARE

Explore AI models 2428

Discover models worth your attention

DeBERTa Emotion Predictor は、ファインチューニング済みの DeBERTa モデルを用いて日本語テキストの感情推定を行う Python パッケージです。8 つの感情（Joy, Sadness, Anticipation, Surprise, Anger, Fear, Disgust, Trust）に対するそれぞれのモデルを利用し、各テキストに対する感情の予測ラベルと肯定クラスの確信度を簡単に取得できます。 This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License. To view a copy of this license, visit https://creativecommons.org/licenses/by-sa/4.0/ This project is based on: - DeBERTa (https://huggingface.co/microsoft/deberta-v3-large), licensed under the MIT License. - DeBERTa Japanese Model (https://huggingface.co/globis-university/deberta-v3-japanese-large), licensed under the CC BY-SA 4.0 License.

Text analysis
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

DeBERTa Emotion Predictor は、ファインチューニング済みの DeBERTa モデルを用いて日本語テキストの感情推定を行う Python パッケージです。8 つの感情（Joy, Sadness, Anticipation, Surprise, Anger, Fear, Disgust, Trust）に対するそれぞれのモデルを利用し、各テキストに対する感情の予測ラベルと肯定クラスの確信度を簡単に取得できます。 This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License. To view a copy of this license, visit https://creativecommons.org/licenses/by-sa/4.0/ This project is based on: - DeBERTa (https://huggingface.co/microsoft/deberta-v3-large), licensed under the MIT License. - DeBERTa Japanese Model (https://huggingface.co/globis-university/deberta-v3-japanese-large), licensed under the CC BY-SA 4.0 License.

Text analysis
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

DeBERTa Emotion Predictor は、ファインチューニング済みの DeBERTa モデルを用いて日本語テキストの感情推定を行う Python パッケージです。8 つの感情（Joy, Sadness, Anticipation, Surprise, Anger, Fear, Disgust, Trust）に対するそれぞれのモデルを利用し、各テキストに対する感情の予測ラベルと肯定クラスの確信度を簡単に取得できます。 This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License. To view a copy of this license, visit https://creativecommons.org/licenses/by-sa/4.0/ This project is based on: - DeBERTa (https://huggingface.co/microsoft/deberta-v3-large), licensed under the MIT License. - DeBERTa Japanese Model (https://huggingface.co/globis-university/deberta-v3-japanese-large), licensed under the CC BY-SA 4.0 License.

Text analysis
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

DeBERTa Emotion Predictor は、ファインチューニング済みの DeBERTa モデルを用いて日本語テキストの感情推定を行う Python パッケージです。8 つの感情（Joy, Sadness, Anticipation, Surprise, Anger, Fear, Disgust, Trust）に対するそれぞれのモデルを利用し、各テキストに対する感情の予測ラベルと肯定クラスの確信度を簡単に取得できます。 This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License. To view a copy of this license, visit https://creativecommons.org/licenses/by-sa/4.0/ This project is based on: - DeBERTa (https://huggingface.co/microsoft/deberta-v3-large), licensed under the MIT License. - DeBERTa Japanese Model (https://huggingface.co/globis-university/deberta-v3-japanese-large), licensed under the CC BY-SA 4.0 License.

Text analysis
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

DeBERTa Emotion Predictor は、ファインチューニング済みの DeBERTa モデルを用いて日本語テキストの感情推定を行う Python パッケージです。8 つの感情（Joy, Sadness, Anticipation, Surprise, Anger, Fear, Disgust, Trust）に対するそれぞれのモデルを利用し、各テキストに対する感情の予測ラベルと肯定クラスの確信度を簡単に取得できます。 This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License. To view a copy of this license, visit https://creativecommons.org/licenses/by-sa/4.0/ This project is based on: - DeBERTa (https://huggingface.co/microsoft/deberta-v3-large), licensed under the MIT License. - DeBERTa Japanese Model (https://huggingface.co/globis-university/deberta-v3-japanese-large), licensed under the CC BY-SA 4.0 License.

Text analysis
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

DeBERTa Emotion Predictor は、ファインチューニング済みの DeBERTa モデルを用いて日本語テキストの感情推定を行う Python パッケージです。8 つの感情（Joy, Sadness, Anticipation, Surprise, Anger, Fear, Disgust, Trust）に対するそれぞれのモデルを利用し、各テキストに対する感情の予測ラベルと肯定クラスの確信度を簡単に取得できます。 This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License. To view a copy of this license, visit https://creativecommons.org/licenses/by-sa/4.0/ This project is based on: - DeBERTa (https://huggingface.co/microsoft/deberta-v3-large), licensed under the MIT License. - DeBERTa Japanese Model (https://huggingface.co/globis-university/deberta-v3-japanese-large), licensed under the CC BY-SA 4.0 License.

Text analysis
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

DeBERTa Emotion Predictor は、ファインチューニング済みの DeBERTa モデルを用いて日本語テキストの感情推定を行う Python パッケージです。8 つの感情（Joy, Sadness, Anticipation, Surprise, Anger, Fear, Disgust, Trust）に対するそれぞれのモデルを利用し、各テキストに対する感情の予測ラベルと肯定クラスの確信度を簡単に取得できます。 This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License. To view a copy of this license, visit https://creativecommons.org/licenses/by-sa/4.0/ This project is based on: - DeBERTa (https://huggingface.co/microsoft/deberta-v3-large), licensed under the MIT License. - DeBERTa Japanese Model (https://huggingface.co/globis-university/deberta-v3-japanese-large), licensed under the CC BY-SA 4.0 License.

Text analysis
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

DeBERTa Emotion Predictor は、ファインチューニング済みの DeBERTa モデルを用いて日本語テキストの感情推定を行う Python パッケージです。8 つの感情（Joy, Sadness, Anticipation, Surprise, Anger, Fear, Disgust, Trust）に対するそれぞれのモデルを利用し、各テキストに対する感情の予測ラベルと肯定クラスの確信度を簡単に取得できます。 This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License. To view a copy of this license, visit https://creativecommons.org/licenses/by-sa/4.0/ This project is based on: - DeBERTa (https://huggingface.co/microsoft/deberta-v3-large), licensed under the MIT License. - DeBERTa Japanese Model (https://huggingface.co/globis-university/deberta-v3-japanese-large), licensed under the CC BY-SA 4.0 License.

Text analysis
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

**A 24M-parameter MiniLM model that tags English text with IAB content and audience categories, small enough to run on the device: a 25 MB int8 ONNX file that classifies a 128-token text in 4–10 ms on a laptop CPU.** Give it an article, a page summary, or any short English text and it returns IAB Tech Lab **content** categories (704 labels, IAB Content Taxonomy 3.0) and **audience** segments (1,567 labels, IAB Audience Taxonomy 1.1) with a score for each. In a blind, 10,000-text comparison judged by GPT-5.5, its labels were preferred over **GPT-5.4-nano**'s in **66% of decided cases**, and on independent human labels it scores **0.406 F1 vs 0.375** for nano. It never returns a label outside the taxonomy. This is the exact bundle ZeroGPU runs on edge devices (browsers, Node.js / Docker workers, Android) in production. It is laid out for transformers.js and also runs directly in onnxruntime.

Text analysis
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

**The on-device build of `ZeroGPU/zlm-v1-iab-domain-classifier`: a 149M-parameter ModernBERT model that classifies a web destination into IAB content and audience categories from its URL alone, packaged as a 153 MB int8 ONNX file that runs in the browser.** This repository is the exact bundle ZeroGPU serves to edge devices (browsers, Node.js / Docker workers, Android) in production, laid out for transformers.js and onnxruntime. For the full-precision model, the PyTorch checkpoint, the training details and the complete benchmark, see the main repository. - **One output.** `logits` has shape `[batch, 2271]`: the 704 IAB content logits followed by the 1,567 IAB audience logits. Labels in `config.json` carry a head prefix (`content|Sports`, `audience|Interest | Sports | Basketball |`), so a standard text-classification pipeline can run it. - **Per-channel int8 quantization** (QUInt8, dynamic) of the full-precision graph. - **64-token input window**, the length the model was trained with (`max_seq_length` in `postprocess.json`). - **Head-level thresholds:** content 0.60, audience 0.50 (`postprocess.json`).

Text analysis
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

**A 139M-parameter DeBERTa moderation model that flags unsafe English text and scores it on the 13 categories of the OpenAI moderation taxonomy, calibrated and small enough to run on the device (int8 ONNX, 185 MB).** For each input the model returns a binary **unsafe** verdict plus a calibrated score for each category: `hate`, `hate/threatening`, `harassment`, `harassment/threatening`, `self-harm`, `self-harm/intent`, `self-harm/instructions`, `sexual`, `sexual/minors`, `violence`, `violence/graphic`, `illicit`, `illicit/violent`. It runs locally with no network call. This is the exact bundle ZeroGPU runs on edge devices (browsers, Node.js / Docker workers, Android). It is laid out for transformers.js and also runs directly in onnxruntime.

Text analysis
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗
COST EXPLORER

Great models. Clear costs.

Cost analysis

Adjust monthly token usage to compare estimated API costs.

1 M = 1 million tokens · USD
Compares base text-token rates within the same channel

Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.

MODEL HANDBOOK

Understand before you choose

Selection guide & FAQ
How should API prices be compared?+

Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.

Do open weights mean free commercial use?+

Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.

Is the top-ranked model the best for me?+

Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.

AI NEWS · From information to decisions

Follow trends. Find tools. Explore models.

Explore AI news ↗Discover AI tools ↗