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--- |
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language: |
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- en |
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inference: false |
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pipeline_tag: false |
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datasets: |
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- conll2003 |
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- wnut_17 |
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- jnlpba |
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- conll2012 |
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- BTC |
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- dfki-nlp/few-nerd |
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tags: |
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- PyTorch |
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model-index: |
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- name: "bert-base-NER-reptile-5-datasets" |
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results: |
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- task: |
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name: few-shot-ner |
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type: named-entity-recognition |
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dataset: |
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name: few-nerd-inter |
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type: named-entity-recognition |
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metrics: |
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- name: 5 way 1~2 shot |
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type: f1 |
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value: 56.12 |
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- name: 5-way 5~10-shot |
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type: f1 |
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value: 62.7 |
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- name: 10-way 1~2-shot |
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type: f1 |
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value: 50.3 |
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- name: 10-way 5~10-shot |
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type: f1 |
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value: 58.82 |
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--- |
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# BERT base uncased model pre-trained on 5 NER datasets |
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Model was trained by _SberIDP_. The pretraining process and technical details are described [in this article](https://habr.com/ru/company/sberbank/blog/649609/). |
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* Task: Named Entity Recognition |
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* Base model: [bert-base-uncased](https://huggingface.co/bert-base-uncased) |
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* 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) |
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* 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) |
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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. |
