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AI NEWS · 模型情报站发现能力边界，让每一次选择更有依据 ↗基础档案先收录 · 缺失资料持续补齐
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84,736 款模型档案13040 家厂商 / 发布者45 个能力分类464 款有可比报价 · 30 款有公开评测
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探索 AI 模型 13

为你发现值得关注的模型

Anita

DeepMount00

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。

基础资料已收录 · 缺失资料后续补充

向量与相似度
输入 文本→ 输出 向量
价格待核验多模态与渠道费用须分别核对
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Italian_NER_XXL

DeepMount00

**💡 Found this resource helpful?** Creating and maintaining open source AI models and datasets requires significant computational resources. If this work has been valuable to you, consider supporting my research to help me continue building tools that benefit the entire AI community. Every contribution directly funds more open source innovation! ☕ This is the initial release of our artificial intelligence model on Hugging Face. It is important to note that this version is just the beginning; the model will be constantly improved over time. **Currently, the model boasts an accuracy of 79%, but we plan to increase this regularly through monthly updates.** We are proud to announce that our model is currently the only one in Italy capable of identifying a wide range of **52** different categories. This capability distinctly sets it apart from other models available in the Italian landscape, offering an unprecedented level of versatility and breadth in entity recognition.

译文排队中 · 当前显示来源原文

文本分析
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价格待核验多模态与渠道费用须分别核对
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来源 / 文档 ↗

**💡 Found this resource helpful?** Creating and maintaining open source AI models and datasets requires significant computational resources. If this work has been valuable to you, consider supporting my research to help me continue building tools that benefit the entire AI community. Every contribution directly funds more open source innovation! ☕ Welcome to the second generation of our state-of-the-art Named Entity Recognition model for Italian text. Building on the success of our previous version, Italian_NER_XXL_v2 delivers significantly enhanced performance with an **accuracy of 87.5%** and **F1 score of 89.2%** - an improvement of over 8 percentage points from my previous model. Italian_NER_XXL_v2 remains the only model in Italy capable of identifying a comprehensive range of **52** different entity categories, maintaining our unique position in the Italian NLP landscape. This unparalleled breadth of entity recognition makes our model the premier choice for privacy, legal, and financial applications.

译文排队中 · 当前显示来源原文

文本分析
输入 文本→ 输出 文本
价格待核验多模态与渠道费用须分别核对
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来源 / 文档 ↗

Llama-3-8b-Ita

DeepMount00

**💡 Found this resource helpful?** Creating and maintaining open source AI models and datasets requires significant computational resources. If this work has been valuable to you, consider supporting my research to help me continue building tools that benefit the entire AI community. Every contribution directly funds more open source innovation! ☕ For a detailed comparison of model performance, check out the Leaderboard for Italian Language Models.

译文排队中 · 当前显示来源原文

文本生成
输入 文本→ 输出 文本
价格待核验多模态与渠道费用须分别核对
查看详情 ↗
来源 / 文档 ↗
COST EXPLORER

好模型，也要算好成本。

费用分析

调整月度 Token 用量，直观比较不同模型的预估 API 开支。

1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价

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。

MODEL HANDBOOK

读懂模型，再做选择

选型与常见问题
API 价格应该怎么比较？+

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。

开放权重，就代表可以免费商用吗？+

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。

排行榜第一，就是最适合我的模型吗？+

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。

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