readme: ---
  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)
  