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:
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)