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---
language: pl
license: cc-by-4.0
tags:
- ner
datasets:
- clarin-pl/kpwr-ner
metrics:
- f1
- accuracy
- precision
- recall
widget:
- text: "Nazywam się Jan Kowalski i mieszkam we Wrocławiu."
example_title: "Example"
---
# FastPDN
FastPolDeepNer is model for Named Entity Recognition, designed for easy use, training and configuration. The forerunner of this project is [PolDeepNer2](https://gitlab.clarin-pl.eu/information-extraction/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:
```python
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:
```python
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](https://clarin-pl.eu/dspace/bitstream/handle/11321/294/WytyczneKPWr-jednostkiidentyfikacyjne.pdf).
## Pretraining
FastPDN models have been fine-tuned, thanks to pretrained models:
- [herbert-base-case](https://huggingface.co/allegro/herbert-base-cased)
- [distiluse-base-multilingual-cased-v1](sentence-transformers/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)
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