RWKV7-G1j-13.3B-20260831
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RWKV7-G1j-2.9B-20260831
fla-hub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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View details ↗RWKV7-G1j-7.2B-20260831
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View details ↗transformer-1.3B-100B
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View details ↗Flab-Nxcode-5.7B-instruct
Flab-Pruner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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View details ↗Llama3-Chinese-8B-Instruct
FlagAlphaLlama3-Chinese-8B-Instruct基于Llama3-8B中文微调对话模型，由Llama中文社区和AtomEcho（原子回声）联合研发，我们会持续提供更新的模型参数，模型训练过程见 https://llama.family。 模型的部署、训练、微调等方法详见Llama中文社区GitHub仓库：https://github.com/LlamaFamily/Llama-Chinese
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View details ↗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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View details ↗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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View details ↗Qwen3.5-18B-REAP-A3B-Coding-GGUF
Flagstone8878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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View details ↗amplify-120m
flair-bio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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View details ↗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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View details ↗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。
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View details ↗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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View details ↗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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View details ↗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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View details ↗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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View details ↗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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View details ↗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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View details ↗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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View details ↗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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View details ↗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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View details ↗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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View details ↗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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View details ↗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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