bueble-lm-2b
flair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 ↗DISCOVER & COMPARE
探索 AI 模型 17
chunk-english
flair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 ↗ner-dutch-large
flairSo, the entities "*George Washington*" (labeled as a **person**) and "*Washington*" (labeled as a **location**) are found in the sentence "*George Washington ging naar Washington*".
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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 ↗ner-english
flairThis is the standard 4-class NER model for English that ships with Flair. Based on Flair embeddings and LSTM-CRF. So, the entities "*George Washington*" (labeled as a **person**) and "*Washington*" (labeled as a **location**) are found in the sentence "*George Washington went to Washington*".
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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 ↗ner-english-fast
flairThis is the fast 4-class NER model for English that ships with Flair. Based on Flair embeddings and LSTM-CRF. So, the entities "*George Washington*" (labeled as a **person**) and "*Washington*" (labeled as a **location**) are found in the sentence "*George Washington went to Washington*".
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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 ↗ner-english-large
flairThis is the large 4-class NER model for English that ships with Flair. Based on document-level XLM-R embeddings and FLERT. So, the entities "*George Washington*" (labeled as a **person**) and "*Washington*" (labeled as a **location**) are found in the sentence "*George Washington went to Washington*".
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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。
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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 ↗This is the large 18-class NER model for English that ships with Flair. Based on document-level XLM-R embeddings and FLERT. So, the entities "*September 1st*" (labeled as a **date**), "*George*" (labeled as a **person**), "*1 dollar*" (labeled as a **money**) and "Game of Thrones" (labeled as a **work of art**) are found in the sentence "*On September 1st George Washington won 1 dollar while watching Game of Thrones*".
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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 ↗ner-french
flairSo, the entities "*George Washington*" (labeled as a **person**) and "*Washington*" (labeled as a **location**) are found in the sentence "*George Washington est allé à Washington*".
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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 ↗ner-german
flairSo, the entities "*George Washington*" (labeled as a **person**) and "*Washington*" (labeled as a **location**) are found in the sentence "*George Washington ging nach Washington*".
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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 ↗ner-german-large
flairThis is the large 4-class NER model for German that ships with Flair. Based on document-level XLM-R embeddings and FLERT. So, the entities "*George Washington*" (labeled as a **person**) and "*Washington*" (labeled as a **location**) are found in the sentence "*George Washington ging nach Washington*".
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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 ↗ner-german-legal
flairSo, the entities "*W.*" (labeled as a **person**) and "*§ 36 Abs. 7 IfSG*" (labeled as a **Gesetz**) are found in the sentence "*Herr W. verstieß gegen § 36 Abs. 7 IfSG.*".
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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 ↗ner-multi
flairThis is the standard 4-class NER model for 4 CoNLL-03 languages that ships with Flair. Also kind of works for related languages like French. F1-Score: **92,16** (CoNLL-03 English), **87,33** (CoNLL-03 German revised), **88,96** (CoNLL-03 Dutch), **86,65** (CoNLL-03 Spanish) Based on Flair embeddings and LSTM-CRF.
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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 ↗ner-spanish-large
flairThis is the large 4-class NER model for Spanish that ships with Flair. Based on document-level XLM-R embeddings and FLERT. So, the entities "*George Washington*" (labeled as a **person**) and "*Washington*" (labeled as a **location**) are found in the sentence "*George Washington fue a Washington*".
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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 ↗pos-english
flairThis is the standard part-of-speech tagging model for English that ships with Flair. Based on Flair embeddings and LSTM-CRF. So, the word "*I*" is labeled as a **pronoun** (PRP), "*love*" is labeled as a **verb** (VBP) and "*Berlin*" is labeled as a **proper noun** (NNP) in the sentence "*I love Berlin*".
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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 ↗pos-english-fast
flairSo, the word "*I*" is labeled as a **pronoun** (PRP), "*love*" is labeled as a **verb** (VBP) and "*Berlin*" is labeled as a **proper noun** (NNP) in the sentence "*I love Berlin*".
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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 ↗upos-multi-fast
flairF1-Score: **92,88** (12 UD Treebanks covering English, German, French, Italian, Dutch, Polish, Spanish, Swedish, Danish, Norwegian, Finnish and Czech) So, the words "*Ich*" and "*they*" are labeled as **pronouns** (PRON), while "*liebe*" and "*say*" are labeled as **verbs** (VERB) in the multilingual sentence "*Ich liebe Berlin, as they say*".
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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。