This is a very small model I use for testing my 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 (pip install flair)
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
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
