# Named Entity Recognition Models¶

## Bert¶

Bert: Pre-training of deep bidirectional transformers for language understanding

### Abstract¶

We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).

### Dataset¶

#### Train Dataset¶

trainset text_num entity_num
CLUENER2020 10748 23338

#### Test Dataset¶

testset text_num entity_num
CLUENER2020 1343 2982

### Results and models¶

@article{devlin2018bert,