align
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从语言到影像,从开源到 API。读懂模型能力、比较真实成本,
为你的下一个想法,找到合适的智能引擎。
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Type a word or a sentence and get the emoji that fits. Emo is tuned for to-dos, calendar entries, notes, and message drafts across **22 languages** (including CJK, Arabic, Thai, Hindi, and more). The model and tokenizer together are small (**5MB** on Apple via Core ML, **11MB** via LiteRT elsewhere) and runs in well under 2ms on device. > `"Dentist appointment"` → 🦷 · `"réserver un vol pour Tokyo"` → ✈️ · `"犬の散歩"` → 🐕 · `"จองโรงแรม"` → 🏨 - **Live demo:** desert-ant-labs/emo-demo: type a phrase, get emojis, fully in your browser. **Swift** (requirements)
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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。
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Strip names, addresses, emails, phone numbers, cards, IBANs and national IDs out of text before it leaves the device, across **27 languages** (see Languages). A token classifier plus a deterministic layer for structured IDs. The deployable model is **11.6MB** (4-bit Core ML on Apple) or **24.5MB** (int8 LiteRT `.tflite` on Android, Linux and the web). > `"Call Anna Kovács at anna@example.hu, IBAN GB29NWBK60161331926819"` → > `"Call [GIVEN_NAME] [SURNAME] at [EMAIL], IBAN [BANK_ACCOUNT]"` `GIVEN_NAME`, `SURNAME`, `STREET_NAME`, `BUILDING_NUMBER`, `SECONDARY_ADDRESS`, `CITY`, `STATE`, `ZIP_CODE`, `EMAIL`, `PHONE`, `CREDIT_CARD`, `BANK_ACCOUNT`, `ROUTING_NUMBER`, `IP_ADDRESS`, `URL`, `GOVERNMENT_ID`, `PASSPORT`, `DRIVERS_LICENSE`, `TAX_ID`, `SSN`.
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Turn a rough circle drawing into a clean shape, with fitted vector geometry ready to snap to. Built for PencilKit-style "smart shapes": draw, pause, and the rough stroke becomes a crisp shape. The model is tiny (**200KB** Core ML, **1.3MB** LiteRT) and runs in a few milliseconds on device. `line`, `rectangle`, `triangle`, `ellipse`, `star`, plus `none` (the reject class: scribbles, partial shapes, and other non-shape strokes). Squares and circles are covered by `rectangle` and `ellipse` (snapped when near-regular). Desert Ant Labs Source-Available License. Free for most apps, and a commercial license is required at scale. Full terms are at the link. Licensing: .
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On-device titles and descriptions: a short factual title and a one- to two-sentence description for any passage of text. Get a title and a one or two sentence description for any passage of text, on device. Title is fine-tuned on transcript clips, but it works on any prose. The register is deliberately plain, with no emoji, no hashtags and no clickbait, and a description identifies *this* passage rather than its topic.
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Find every "uh", "um" and "hmm" in an audio file, down to 20ms. Uhm runs on device, does not require ASR, and is trained on English with acoustic transfer to Spanish, French, German, and Dutch. `uhm.mlmodelc/` is a compiled Core ML model directory. The Swift SDK downloads it with the Hugging Face Hub snapshot API, so only changed files are re-fetched on model updates. `uhm.tflite` is what the Kotlin and JavaScript SDKs download; it is converted from `uhm.onnx` and matches it at 99.7 to 100% per-frame argmax. The shipped model is a DistilHuBERT fine-tune, the smaller and more precise Uhm runtime model; the older HuBERT-base tier is no longer published.
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可以调整厂商、分类或输入输出条件;新模型正持续收录。
调整月度 Token 用量,直观比较不同模型的预估 API 开支。
使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照;不计缓存优惠、阶梯价、推理额外输出与税费,请以来源的当前报价为准。
先区分输入、输出和缓存价格,再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费,不能直接与 Token 单价比较。
不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。
榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本,再选择模型。