File size: 1,530 Bytes
d8bb207
 
 
029ce31
61e2793
d8bb207
 
 
 
 
 
a2b9e7c
d8bb207
 
25ca18c
 
 
 
fb12cae
25ca18c
 
0a3309a
25ca18c
 
 
 
 
0e4b446
 
 
 
 
 
 
 
 
d8bb207
 
 
 
0c7ce6f
 
 
2e3a2f1
73e9211
a2b9e7c
d8bb207
 
46539f6
0c7ce6f
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
---
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](https://habr.com/ru/company/sberbank/blog/).


* Task: Named Entity Recognition
* 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)



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.