FastPDN

FastPolDeepNer is model for Named Entity Recognition, designed for easy use, training and configuration. The forerunner of this project is PolDeepNer2. The model implements a pipeline consisting of data processing and training using: hydra, pytorch, pytorch-lightning, transformers.

Source code: https://gitlab.clarin-pl.eu/grupa-wieszcz/ner/fast-pdn

How to use

Here is how to use this model to get Named Entities in text:

from transformers import pipeline
ner = pipeline('ner', model='clarin-pl/FastPDN', aggregation_strategy='simple')

text = "Nazywam się Jan Kowalski i mieszkam we Wrocławiu."
ner_results = ner(text)
for output in ner_results:
    print(output)

{'entity_group': 'nam_liv_person', 'score': 0.9996054, 'word': 'Jan Kowalski', 'start': 12, 'end': 24}
{'entity_group': 'nam_loc_gpe_city', 'score': 0.998931, 'word': 'Wrocławiu', 'start': 39, 'end': 48}

Here is how to use this model to get the logits for every token in text:

from transformers import AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("clarin-pl/FastPDN")
model = AutoModelForTokenClassification.from_pretrained("clarin-pl/FastPDN")

text = "Nazywam się Jan Kowalski i mieszkam we Wrocławiu."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)

Training data

The FastPDN model was trained on datasets (with 82 class versions) of kpwr and cen. Annotation guidelines are specified here.

Pretraining

FastPDN models have been fine-tuned, thanks to pretrained models:

• herbert-base-case
• distiluse-base-multilingual-cased-v1

Evaluation

Runs trained on cen_n82 and kpwr_n82:

name	test/f1	test/pdn2_f1	test/acc	test/precision	test/recall
distiluse	0.53	0.61	0.95	0.55	0.54
herbert	0.68	0.78	0.97	0.7	0.69

Authors

• Grupa Wieszcze CLARIN-PL
• Wiktor Walentynowicz

Contact

• Norbert Ropiak (norbert.ropiak@pwr.edu.pl)