
# EstBERT_NER

## Model description 

EstBERT_NER is a fine-tuned EstBERT model that can be used for Named Entity Recognition. This model was trained on the Estonian NER dataset created by [Tkachenko et al](https://www.aclweb.org/anthology/W13-2412.pdf). It can recognize three types of entities: locations (LOC), organizations (ORG) and persons (PER). 

## How to use 

You can use this model with Transformers pipeline for NER. Post-processing of results may be necessary as the model occasionally tags subword tokens as entities. 

```
from transformers import BertTokenizer, BertForTokenClassification
from transformers import pipeline

tokenizer = BertTokenizer.from_pretrained('tartuNLP/EstBERT_NER')
bertner = BertForTokenClassification.from_pretrained('tartuNLP/EstBERT_NER')

nlp = pipeline("ner", model=bertner, tokenizer=tokenizer)
sentence = 'Eesti Ekspressi teada on Eesti Pank uurinud Hansapanga tehinguid , mis toimusid kaks aastat tagasi suvel ja mille käigus voolas panka ligi miljardi krooni ulatuses kahtlast raha .'

ner_results = nlp(sentence)
print(ner_results)
```
```
[{'word': 'Eesti', 'score': 0.9964128136634827, 'entity': 'B-ORG', 'index': 1}, {'word': 'Ekspressi', 'score': 0.9978809356689453, 'entity': 'I-ORG', 'index': 2}, {'word': 'Eesti', 'score': 0.9988121390342712, 'entity': 'B-ORG', 'index': 5}, {'word': 'Pank', 'score': 0.9985784292221069, 'entity': 'I-ORG', 'index': 6}, {'word': 'Hansapanga', 'score': 0.9979034662246704, 'entity': 'B-ORG', 'index': 8}]

```



## BibTeX entry and citation info

```
@misc{tanvir2020estbert,
      title={EstBERT: A Pretrained Language-Specific BERT for Estonian}, 
      author={Hasan Tanvir and Claudia Kittask and Kairit Sirts},
      year={2020},
      eprint={2011.04784},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```
{"language": "et", "license": "cc-by-4.0", "widget": [{"text": "Eesti President on Alar Karis."}]}
transformers pytorch jax safetensors bert token-classification et arxiv:2011.04784 license:cc-by-4.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #jax #safetensors #bert #token-classification #et #arxiv-2011.04784 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us 


# EstBERT_NER

## Model description 

EstBERT_NER is a fine-tuned EstBERT model that can be used for Named Entity Recognition. This model was trained on the Estonian NER dataset created by Tkachenko et al. It can recognize three types of entities: locations (LOC), organizations (ORG) and persons (PER). 

## How to use 

You can use this model with Transformers pipeline for NER. Post-processing of results may be necessary as the model occasionally tags subword tokens as entities. 






## BibTeX entry and citation info


# EstBERT_NER ## Model description 

EstBERT_NER is a fine-tuned EstBERT model that can be used for Named Entity Recognition. This model was trained on the Estonian NER dataset created by Tkachenko et al. It can recognize three types of entities: locations (LOC), organizations (ORG) and persons (PER). ## How to use 

You can use this model with Transformers pipeline for NER. Post-processing of results may be necessary as the model occasionally tags subword tokens as entities. ## BibTeX entry and citation info
TAGS
#transformers #pytorch #jax #safetensors #bert #token-classification #et #arxiv-2011.04784 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us 
 # EstBERT_NER ## Model description 

EstBERT_NER is a fine-tuned EstBERT model that can be used for Named Entity Recognition. This model was trained on the Estonian NER dataset created by Tkachenko et al. It can recognize three types of entities: locations (LOC), organizations (ORG) and persons (PER). ## How to use 

You can use this model with Transformers pipeline for NER. Post-processing of results may be necessary as the model occasionally tags subword tokens as entities. ## BibTeX entry and citation info
passage: TAGS
#transformers #pytorch #jax #safetensors #bert #token-classification #et #arxiv-2011.04784 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us 
# EstBERT_NER## Model description 

EstBERT_NER is a fine-tuned EstBERT model that can be used for Named Entity Recognition. This model was trained on the Estonian NER dataset created by Tkachenko et al. It can recognize three types of entities: locations (LOC), organizations (ORG) and persons (PER).## How to use 

You can use this model with Transformers pipeline for NER. Post-processing of results may be necessary as the model occasionally tags subword tokens as entities.## BibTeX entry and citation info