metadata
language:
- en
inference: false
pipeline_tag: false
datasets:
- conll2003
- wnut_17
- jnlpba
- conll2012
- BTC
- dfki-nlp/few-nerd
tags:
- PyTorch
model-index:
- name: bert-base-NER-reptile-5-datasets
results:
- task:
name: few-shot-ner
type: named-entity-recognition
dataset:
name: few-nerd-inter
type: named-entity-recognition
metrics:
- name: 5 way 1~2 shot
type: f1
value: 56.12
- name: 5-way 5~10-shot
type: f1
value: 62.7
- name: 10-way 1~2-shot
type: f1
value: 50.3
- name: 10-way 5~10-shot
type: f1
value: 58.82
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.