
# Hungarian named entity recognition model with four entity types: PER ORG LOC MISC

  - Pretrained model used: SZTAKI-HLT/hubert-base-cc 
  - Finetuned on NYTK-NerKor Corpus
  	
## Limitations

- max_seq_length = 448

## See [https://huggingface.co/novakat/nerkor-cars-onpp-hubert](https://huggingface.co/novakat/nerkor-cars-onpp-hubert) for a much more elaborate Hungarian named entity model.


{"language": ["hu"], "license": "gpl", "tags": ["token-classification"], "metrics": ["F1"], "widget": [{"text": "A j\u00f3t\u00e9konys\u00e1gi szervezet \u00e1ltal id\u00e9zett Forbes-adatok szerint a vil\u00e1g t\u00edz leggazdagabb embere: Elon Musk (Tesla, SpaceX), Jeff Bezos (Amazon, Blue Origin), Bernard Arnault \u00e9s csal\u00e1dja (LVMH, azaz Louis Vuitton \u00e9s Mo\u00ebt Hennessy), Bill Gates (Microsoft), Larry Ellison (Oracle), Larry Page (Google), Sergey Brin (Google), Mark Zuckerberg (Facebook), Steve Ballmer (Microsoft) \u00e9s Warren Buffett (befektet\u0151). Mik\u00f6zben vagyonuk egy\u00fcttesen 700 milli\u00e1rdr\u00f3l m\u00e1sf\u00e9l ezer milli\u00e1rd doll\u00e1rra n\u0151tt 2020 m\u00e1rciusa \u00e9s 2021 novembere k\u00f6z\u00f6tt, jelent\u0151s elt\u00e9r\u00e9sek vannak k\u00f6z\u00f6tt\u00fck: Musk vagyona t\u00f6bb mint 1000 sz\u00e1zal\u00e9kos, m\u00edg Gates\u00e9 szer\u00e9nyebb, 30 sz\u00e1zal\u00e9kos n\u00f6veked\u00e9st mutatott."}], "inference": {"parameters": {"aggregation_strategy": "first"}}}
transformers pytorch safetensors bert token-classification hu license:gpl autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #bert #token-classification #hu #license-gpl #autotrain_compatible #endpoints_compatible #region-us 


# Hungarian named entity recognition model with four entity types: PER ORG LOC MISC

  - Pretrained model used: SZTAKI-HLT/hubert-base-cc 
  - Finetuned on NYTK-NerKor Corpus
  	
## Limitations

- max_seq_length = 448

## See URL for a much more elaborate Hungarian named entity model.


# Hungarian named entity recognition model with four entity types: PER ORG LOC MISC

  - Pretrained model used: SZTAKI-HLT/hubert-base-cc 
  - Finetuned on NYTK-NerKor Corpus ## Limitations

- max_seq_length = 448 ## See URL for a much more elaborate Hungarian named entity model.
TAGS
#transformers #pytorch #safetensors #bert #token-classification #hu #license-gpl #autotrain_compatible #endpoints_compatible #region-us 
 # Hungarian named entity recognition model with four entity types: PER ORG LOC MISC

  - Pretrained model used: SZTAKI-HLT/hubert-base-cc 
  - Finetuned on NYTK-NerKor Corpus ## Limitations

- max_seq_length = 448 ## See URL for a much more elaborate Hungarian named entity model.
passage: TAGS
#transformers #pytorch #safetensors #bert #token-classification #hu #license-gpl #autotrain_compatible #endpoints_compatible #region-us 
# Hungarian named entity recognition model with four entity types: PER ORG LOC MISC

  - Pretrained model used: SZTAKI-HLT/hubert-base-cc 
  - Finetuned on NYTK-NerKor Corpus## Limitations

- max_seq_length = 448## See URL for a much more elaborate Hungarian named entity model.