
# Hungarian Named Entity Recognition Model with huBERT

For further models, scripts and details, see [our demo site](https://juniper.nytud.hu/demo/nlp).

  - Pretrained model used: SZTAKI-HLT/hubert-base-cc
  - Finetuned on [NYTK-NerKor](https://github.com/nytud/NYTK-NerKor)
  - NE categories are: PER, LOC, MISC, ORG
  	
## Limitations

- max_seq_length = 128

## Results

F-score: **90.18%**

## Usage with pipeline

```python
from transformers import pipeline

ner = pipeline(task="ner", model="NYTK/named-entity-recognition-nerkor-hubert-hungarian")
input_text = "A Kovácsné Nagy Erzsébet nagyon jól érzi magát a Nokiánál, azonban a Németországból érkezett Kovács Péter nehezen boldogul a beilleszkedéssel."

print(ner(input_text, aggregation_strategy="simple"))
```

## Citation
If you use this model, please cite the following paper:

```
@inproceedings {yang-language-models,
    title = {Training language models with low resources: RoBERTa, BART and ELECTRA experimental models for Hungarian},
	booktitle = {Proceedings of 12th IEEE International Conference on Cognitive Infocommunications (CogInfoCom 2021)},
	year = {2021},
	publisher = {IEEE},
	address = {Online},
	author = {Yang, Zijian Győző and Váradi, Tamás},
	pages = {279--285}
}

```
{"language": ["hu"], "license": "apache-2.0", "tags": ["token-classification"], "metrics": ["f1"], "widget": [{"text": "A Kov\u00e1csn\u00e9 Nagy Erzs\u00e9bet nagyon j\u00f3l \u00e9rzi mag\u00e1t a Noki\u00e1n\u00e1l, azonban a N\u00e9metorsz\u00e1gb\u00f3l \u00e9rkezett Kov\u00e1cs P\u00e9ter nehezen boldogul a beilleszked\u00e9ssel."}]}
transformers pytorch bert token-classification hu license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #token-classification #hu #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 


# Hungarian Named Entity Recognition Model with huBERT

For further models, scripts and details, see our demo site.

  - Pretrained model used: SZTAKI-HLT/hubert-base-cc
  - Finetuned on NYTK-NerKor
  - NE categories are: PER, LOC, MISC, ORG
  	
## Limitations

- max_seq_length = 128

## Results

F-score: 90.18%

## Usage with pipeline



If you use this model, please cite the following paper:


# Hungarian Named Entity Recognition Model with huBERT

For further models, scripts and details, see our demo site.

  - Pretrained model used: SZTAKI-HLT/hubert-base-cc
  - Finetuned on NYTK-NerKor
  - NE categories are: PER, LOC, MISC, ORG ## Limitations

- max_seq_length = 128 ## Results

F-score: 90.18% ## Usage with pipeline



If you use this model, please cite the following paper:
TAGS
#transformers #pytorch #bert #token-classification #hu #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # Hungarian Named Entity Recognition Model with huBERT

For further models, scripts and details, see our demo site.

  - Pretrained model used: SZTAKI-HLT/hubert-base-cc
  - Finetuned on NYTK-NerKor
  - NE categories are: PER, LOC, MISC, ORG ## Limitations

- max_seq_length = 128 ## Results

F-score: 90.18% ## Usage with pipeline



If you use this model, please cite the following paper:
passage: TAGS
#transformers #pytorch #bert #token-classification #hu #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# Hungarian Named Entity Recognition Model with huBERT

For further models, scripts and details, see our demo site.

  - Pretrained model used: SZTAKI-HLT/hubert-base-cc
  - Finetuned on NYTK-NerKor
  - NE categories are: PER, LOC, MISC, ORG## Limitations

- max_seq_length = 128## Results

F-score: 90.18%## Usage with pipeline



If you use this model, please cite the following paper: