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探索 AI 模型 12
afro-xlmr-base
DavlanAfroXLMR-base was created by MLM adaptation of XLM-R-base model on 17 African languages (Afrikaans, Amharic, Hausa, Igbo, Malagasy, Chichewa, Oromo, Naija, Kinyarwanda, Kirundi, Shona, Somali, Sesotho, Swahili, isiXhosa, Yoruba, and isiZulu) covering the major African language families and 3 high-resource languages (Arabic, French, and English). language| XLM-R-miniLM| XLM-R-base |XLM-R-large| afro-xlmr-base | afro-xlmr-small | afro-xlmr-mini -|-|-|-|-|-|- amh |69.5|70.6|76.2|76.1|70.1|69.7 hau |74.5|89.5|90.5|91.2|91.4|87.7 ibo |81.9|84.8|84.1|87.4|86.6|83.5 kin |68.6|73.3|73.8|78.0|77.5|74.1 lug |64.7|79.7|81.6|82.9|83.2|77.4 luo |11.7|74.9|73.6|75.1|75.4|17.5 pcm |83.2|87.3|89.0|89.6|89.0|85.5 swa |86.3|87.4|89.4|88.6|88.7|86.0 wol |51.7|63.9|67.9|67.4|65.9|59.0 yor |72.0|78.3|78.9|82.1|81.3|75.1
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查看详情 ↗afro-xlmr-large
DavlanAfroXLMR-large was created by MLM adaptation of XLM-R-large model on 17 African languages (Afrikaans, Amharic, Hausa, Igbo, Malagasy, Chichewa, Oromo, Nigerian-Pidgin, Kinyarwanda, Kirundi, Shona, Somali, Sesotho, Swahili, isiXhosa, Yoruba, and isiZulu) covering the major African language families and 3 high-resource languages (Arabic, French, and English). language| XLM-R-miniLM| XLM-R-base |XLM-R-large | afro-xlmr-large | afro-xlmr-base | afro-xlmr-small | afro-xlmr-mini -|-|-|-|-|-|-|- amh |69.5|70.6|76.2|79.7|76.1|70.1|69.7 hau |74.5|89.5|90.5|91.4|91.2|91.4|87.7 ibo |81.9|84.8|84.1|87.7|87.4|86.6|83.5 kin |68.6|73.3|73.8|79.1|78.0|77.5|74.1 lug |64.7|79.7|81.6|86.7|82.9|83.2|77.4 luo |11.7|74.9|73.6|78.1|75.1|75.4|17.5 pcm |83.2|87.3|89.0|91.0|89.6|89.0|85.5 swa |86.3|87.4|89.4|90.4|88.6|88.7|86.0 wol |51.7|63.9|67.9|69.6|67.4|65.9|59.0 yor |72.0|78.3|78.9|85.2|82.1|81.3|75.1 avg |66.4|79.0|80.5|83.9|81.8|80.9|71.6
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价格待核验多模态与渠道费用须分别核对
查看详情 ↗afro-xlmr-large-75L
Davlan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 ↗afro-xlmr-large-76L
Davlan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 ↗afro-xlmr-mini
Davlan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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价格待核验多模态与渠道费用须分别核对
查看详情 ↗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
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价格待核验多模态与渠道费用须分别核对
查看详情 ↗Hugging Face's logo --- language: - ar - de - en - es - fr - it - lv - nl - pt - zh - multilingual --- **bert-base-multilingual-cased-ner-hrl** is a **Named Entity Recognition** model for 10 high resourced languages (Arabic, German, English, Spanish, French, Italian, Latvian, Dutch, Portuguese and Chinese) based on a fine-tuned mBERT base model. It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER). Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on an aggregation of 10 high-resourced languages You can use this model with Transformers *pipeline* for NER. This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. The training data for the 10 languages are from:
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查看详情 ↗Hugging Face's logo --- language: - ar - de - en - es - fr - it - lv - nl - pt - zh - multilingual --- **distilbert-base-multilingual-cased-ner-hrl** is a **Named Entity Recognition** model for 10 high resourced languages (Arabic, German, English, Spanish, French, Italian, Latvian, Dutch, Portuguese and Chinese) based on a fine-tuned Distiled BERT base model. It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER). Specifically, this model is a *distilbert-base-multilingual-cased* model that was fine-tuned on an aggregation of 10 high-resourced languages You can use this model with Transformers *pipeline* for NER. This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. The training data for the 10 languages are from:
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查看详情 ↗xlm-roberta-base-ner-hrl
DavlanHugging Face's logo --- language: - ar - de - en - es - fr - it - lv - nl - pt - zh - multilingual --- **xlm-roberta-base-ner-hrl** is a **Named Entity Recognition** model for 10 high resourced languages (Arabic, German, English, Spanish, French, Italian, Latvian, Dutch, Portuguese and Chinese) based on a fine-tuned XLM-RoBERTa base model. It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER). Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on an aggregation of 10 high-resourced languages You can use this model with Transformers *pipeline* for NER. This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. The training data for the 10 languages are from:
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查看详情 ↗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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价格待核验多模态与渠道费用须分别核对
查看详情 ↗Hugging Face's logo --- language: - ar - de - en - es - fr - it - lv - nl - pt - zh - multilingual --- **xlm-roberta-large-ner-hrl** is a **Named Entity Recognition** model for 10 high resourced languages (Arabic, German, English, Spanish, French, Italian, Latvian, Dutch, Portuguese and Chinese) based on a fine-tuned XLM-RoBERTa large model. It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER). Specifically, this model is a *xlm-roberta-large* model that was fine-tuned on an aggregation of 10 high-resourced languages You can use this model with Transformers *pipeline* for NER. This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. The training data for the 10 languages are from:
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价格待核验多模态与渠道费用须分别核对
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。