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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

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Dataset used to train helpmefindaname/mini-sequence-tagger-conll03