readme: ---
  tags:
  - flair
  - token-classification
  - sequence-tagger-model
  language: en
  datasets:
  - conll2003
  widget:
  - text: "George Washington went to Washington"
  ---
  
  This is a very small model I use for testing my [ner eval dashboard](https://github.com/helpmefindaname/ner-eval-dashboard)
  
  
  
  F1-Score: **48,73** (CoNLL-03)
  
  Predicts 4 tags:
  
  | **tag**                        | **meaning** |
  |---------------------------------|-----------|
  | PER         | person name | 
  | LOC         | location name | 
  | ORG         | organization name | 
  | MISC         | other name | 
  
  Based on huggingface minimal testing embeddings
  
  ---
  
  ### Demo: How to use in Flair
  
  Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
  
  ```python
  from flair.data import Sentence
  from flair.models import SequenceTagger
  # load tagger
  tagger = SequenceTagger.load("helpmefindaname/mini-sequence-tagger-conll03")
  # make example sentence
  sentence = Sentence("George Washington went to Washington")
  # predict NER tags
  tagger.predict(sentence)
  # print sentence
  print(sentence)
  # print predicted NER spans
  print('The following NER tags are found:')
  # iterate over entities and print
  for entity in sentence.get_spans('ner'):
      print(entity)
  ```
  
  This yields the following output:
  ```
  Span [1,2]: "George Washington"   [− Labels: PER (1.0)]
  Span [5]: "Washington"   [− Labels: LOC (1.0)]
  ```
  
  So, the entities "*George Washington*" (labeled as a **person**) and "*Washington*" (labeled as a **location**) are found in the sentence "*George Washington went to Washington*". 
  
  
  ---
  
  ### Training: Script to train this model
  
  The following command was used to train this model: 
  where `examples\ner\run_ner.py` refers to [this script](https://github.com/flairNLP/flair/blob/master/examples/ner/run_ner.py)
  
  ```
  python examples\ner\run_ner.py --model_name_or_path hf-internal-testing/tiny-random-bert --dataset_name CONLL_03 --learning_rate 0.002 --mini_batch_chunk_size 1024 --batch_size 64 --num_epochs 100
  ```
  
  
  
  ---