
# BERT base uncased model pre-trained on 5 NER datasets

Model was trained by _SberIDP_. The pretraining process and technical details are described [in this article](https://habr.com/ru/company/sberbank/blog/649609/).


* Task: Named Entity Recognition
* Base model: [bert-base-uncased](https://huggingface.co/bert-base-uncased)
* Training Data is 5 datasets: [CoNLL-2003](https://aclanthology.org/W03-0419.pdf), [WNUT17](http://noisy-text.github.io/2017/emerging-rare-entities.html), [JNLPBA](http://www.geniaproject.org/shared-tasks/bionlp-jnlpba-shared-task-2004), [CoNLL-2012 (OntoNotes)](https://aclanthology.org/W12-4501.pdf), [BTC](https://www.derczynski.com/papers/btc.pdf)
* Testing was made in Few-Shot scenario on [Few-NERD dataset](https://github.com/thunlp/Few-NERD) using the model as a backbone for [StructShot](https://arxiv.org/abs/2010.02405)



The model is pretrained for NER task using [Reptile](https://openai.com/blog/reptile/) and can be finetuned for new entities with only a small amount of samples.
{"language": ["en"], "tags": ["PyTorch"], "datasets": ["conll2003", "wnut_17", "jnlpba", "conll2012", "BTC", "dfki-nlp/few-nerd"], "inference": false, "pipeline_tag": false, "model-index": [{"name": "bert-base-NER-reptile-5-datasets", "results": [{"task": {"type": "named-entity-recognition", "name": "few-shot-ner"}, "dataset": {"name": "few-nerd-inter", "type": "named-entity-recognition"}, "metrics": [{"type": "f1", "value": 56.12, "name": "5 way 1~2 shot"}, {"type": "f1", "value": 62.7, "name": "5-way 5~10-shot"}, {"type": "f1", "value": 50.3, "name": "10-way 1~2-shot"}, {"type": "f1", "value": 58.82, "name": "10-way 5~10-shot"}]}]}]}
transformers pytorch bert token-classification PyTorch en dataset:conll2003 dataset:wnut_17 dataset:jnlpba dataset:conll2012 dataset:BTC dataset:dfki-nlp/few-nerd arxiv:2010.02405 model-index autotrain_compatible region:us
TAGS
#transformers #pytorch #bert #token-classification #PyTorch #en #dataset-conll2003 #dataset-wnut_17 #dataset-jnlpba #dataset-conll2012 #dataset-BTC #dataset-dfki-nlp/few-nerd #arxiv-2010.02405 #model-index #autotrain_compatible #region-us 


# BERT base uncased model pre-trained on 5 NER datasets

Model was trained by _SberIDP_. The pretraining process and technical details are described in this article.


* Task: Named Entity Recognition
* Base model: bert-base-uncased
* Training Data is 5 datasets: CoNLL-2003, WNUT17, JNLPBA, CoNLL-2012 (OntoNotes), BTC
* Testing was made in Few-Shot scenario on Few-NERD dataset using the model as a backbone for StructShot



The model is pretrained for NER task using Reptile and can be finetuned for new entities with only a small amount of samples.
# BERT base uncased model pre-trained on 5 NER datasets

Model was trained by _SberIDP_. The pretraining process and technical details are described in this article.


* Task: Named Entity Recognition
* Base model: bert-base-uncased
* Training Data is 5 datasets: CoNLL-2003, WNUT17, JNLPBA, CoNLL-2012 (OntoNotes), BTC
* Testing was made in Few-Shot scenario on Few-NERD dataset using the model as a backbone for StructShot



The model is pretrained for NER task using Reptile and can be finetuned for new entities with only a small amount of samples.
TAGS
#transformers #pytorch #bert #token-classification #PyTorch #en #dataset-conll2003 #dataset-wnut_17 #dataset-jnlpba #dataset-conll2012 #dataset-BTC #dataset-dfki-nlp/few-nerd #arxiv-2010.02405 #model-index #autotrain_compatible #region-us 
 # BERT base uncased model pre-trained on 5 NER datasets

Model was trained by _SberIDP_. The pretraining process and technical details are described in this article.


* Task: Named Entity Recognition
* Base model: bert-base-uncased
* Training Data is 5 datasets: CoNLL-2003, WNUT17, JNLPBA, CoNLL-2012 (OntoNotes), BTC
* Testing was made in Few-Shot scenario on Few-NERD dataset using the model as a backbone for StructShot



The model is pretrained for NER task using Reptile and can be finetuned for new entities with only a small amount of samples.
passage: TAGS
#transformers #pytorch #bert #token-classification #PyTorch #en #dataset-conll2003 #dataset-wnut_17 #dataset-jnlpba #dataset-conll2012 #dataset-BTC #dataset-dfki-nlp/few-nerd #arxiv-2010.02405 #model-index #autotrain_compatible #region-us 
# BERT base uncased model pre-trained on 5 NER datasets

Model was trained by _SberIDP_. The pretraining process and technical details are described in this article.


* Task: Named Entity Recognition
* Base model: bert-base-uncased
* Training Data is 5 datasets: CoNLL-2003, WNUT17, JNLPBA, CoNLL-2012 (OntoNotes), BTC
* Testing was made in Few-Shot scenario on Few-NERD dataset using the model as a backbone for StructShot



The model is pretrained for NER task using Reptile and can be finetuned for new entities with only a small amount of samples.