mmBERT-base-norwegian
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探索 AI 模型 84997
BioLORD-2023
FremyCompanyThis model was trained using BioLORD, a new pre-training strategy for producing meaningful representations for clinical sentences and biomedical concepts. State-of-the-art methodologies operate by maximizing the similarity in representation of names referring to the same concept, and preventing collapse through contrastive learning. However, because biomedical names are not always self-explanatory, it sometimes results in non-semantic representations. BioLORD overcomes this issue by grounding its concept representations using definitions, as well as short descriptions derived from a multi-relational knowledge graph consisting of biomedical ontologies. Thanks to this grounding, our model produces more semantic concept representations that match more closely the hierarchical structure of ontologies. BioLORD-2023 establishes a new state of the art for text similarity on both clinical sentences (MedSTS) and biomedical concepts (EHR-Rel-B).
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查看详情 ↗BioLORD-2023-C
FremyCompanyThis model was trained using BioLORD, a new pre-training strategy for producing meaningful representations for clinical sentences and biomedical concepts. State-of-the-art methodologies operate by maximizing the similarity in representation of names referring to the same concept, and preventing collapse through contrastive learning. However, because biomedical names are not always self-explanatory, it sometimes results in non-semantic representations. BioLORD overcomes this issue by grounding its concept representations using definitions, as well as short descriptions derived from a multi-relational knowledge graph consisting of biomedical ontologies. Thanks to this grounding, our model produces more semantic concept representations that match more closely the hierarchical structure of ontologies. BioLORD-2023 establishes a new state of the art for text similarity on both clinical sentences (MedSTS) and biomedical concepts (EHR-Rel-B).
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查看详情 ↗BioLORD-2023-M
FremyCompanyThis model was trained using BioLORD, a new pre-training strategy for producing meaningful representations for clinical sentences and biomedical concepts. State-of-the-art methodologies operate by maximizing the similarity in representation of names referring to the same concept, and preventing collapse through contrastive learning. However, because biomedical names are not always self-explanatory, it sometimes results in non-semantic representations. BioLORD overcomes this issue by grounding its concept representations using definitions, as well as short descriptions derived from a multi-relational knowledge graph consisting of biomedical ontologies. Thanks to this grounding, our model produces more semantic concept representations that match more closely the hierarchical structure of ontologies. BioLORD-2023 establishes a new state of the art for text similarity on both clinical sentences (MedSTS) and biomedical concepts (EHR-Rel-B).
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查看详情 ↗BioLORD-2023-M-Dutch-InContext-v1
FremyCompanyThis model supports embedding biomedical entities in both English and Dutch, but allows the in-context embedding of concepts, using the following template: It also supports embedding mentions without context, particularly in English. **NOTE:** Unlike other models in the series, this model uses the [CLS] token to embed the mention. Journal of the American Medical Informatics Association, 2024 François Remy, Kris Demuynck, Thomas Demeester view online
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FRENCHFAKER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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freyavoiceFreyaTTS-small is a 183M-parameter Turkish text-to-speech model, and the open-source member of the FreyaTTS family. It is tokenizer-free at the character level (92 symbols, no phonemizer or G2P) and generates speech with a non-autoregressive conditional flow-matching DiT in the frozen AudioVAE2 latent space (25 Hz, 64-dim latents, 16 kHz encode / 48 kHz decode). Output is 48 kHz mono. **FreyaTTS-small** (this model) is our compact, Apache-2.0, self-hostable model, released in full: weights, inference code, and training pipeline. **FreyaTTS-large** is our production model, serving Turkish voice agents at Freya (YC S25) with higher naturalness and expressivity. It is available commercially rather than as open weights. For access, contact us at **dev@freyavoice.ai**.
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fridaycandour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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FriendliAI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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查看详情 ↗Meta-Llama-3.1-8B-Instruct-int8
FriendliAIThis repo contains the Llama 3 8B Instruct model quantized to INT8 by FriendliAI, significantly enhancing its inference efficiency while maintaining high accuracy. Check out FriendliAI documentation for more details. 1. Sign in Friendli Suite. 2. Go to **User Settings > Tokens** and click **'Create new token'**. 3. Save your created token value. Container secret is a credential to launch our Friendli Container images. You should pass the container secret as an environment variable to run the container image.
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FriendliAI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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查看详情 ↗暂未找到匹配模型
可以调整厂商、分类或输入输出条件；新模型正持续收录。
COST EXPLORER
好模型，也要算好成本。
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
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
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。