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--- |
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language: pl |
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license: cc-by-4.0 |
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tags: |
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- ner |
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datasets: |
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- clarin-pl/kpwr-ner |
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metrics: |
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- f1 |
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- accuracy |
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- precision |
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- recall |
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widget: |
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- text: "Nazywam się Jan Kowalski i mieszkam we Wrocławiu." |
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example_title: "Example" |
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--- |
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# FastPDN |
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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. |
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Source code: https://gitlab.clarin-pl.eu/grupa-wieszcz/ner/fast-pdn |
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## How to use |
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Here is how to use this model to get Named Entities in text: |
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```python |
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from transformers import pipeline |
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ner = pipeline('ner', model='clarin-pl/FastPDN', aggregation_strategy='simple') |
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text = "Nazywam się Jan Kowalski i mieszkam we Wrocławiu." |
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ner_results = ner(text) |
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for output in ner_results: |
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print(output) |
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{'entity_group': 'nam_liv_person', 'score': 0.9996054, 'word': 'Jan Kowalski', 'start': 12, 'end': 24} |
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{'entity_group': 'nam_loc_gpe_city', 'score': 0.998931, 'word': 'Wrocławiu', 'start': 39, 'end': 48} |
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``` |
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Here is how to use this model to get the logits for every token in text: |
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```python |
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from transformers import AutoTokenizer, AutoModelForTokenClassification |
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tokenizer = AutoTokenizer.from_pretrained("clarin-pl/FastPDN") |
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model = AutoModelForTokenClassification.from_pretrained("clarin-pl/FastPDN") |
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text = "Nazywam się Jan Kowalski i mieszkam we Wrocławiu." |
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encoded_input = tokenizer(text, return_tensors='pt') |
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output = model(**encoded_input) |
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``` |
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## Training data |
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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). |
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## Pretraining |
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FastPDN models have been fine-tuned, thanks to pretrained models: |
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- [herbert-base-case](https://huggingface.co/allegro/herbert-base-cased) |
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- [distiluse-base-multilingual-cased-v1](sentence-transformers/distiluse-base-multilingual-cased-v1) |
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## Evaluation |
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Runs trained on `cen_n82` and `kpwr_n82`: |
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| name |test/f1|test/pdn2_f1|test/acc|test/precision|test/recall| |
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|---------|-------|------------|--------|--------------|-----------| |
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|distiluse| 0.53 | 0.61 | 0.95 | 0.55 | 0.54 | |
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| herbert | 0.68 | 0.78 | 0.97 | 0.7 | 0.69 | |
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## Authors |
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- Grupa Wieszcze CLARIN-PL |
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- Wiktor Walentynowicz |
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## Contact |
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- Norbert Ropiak (norbert.ropiak@pwr.edu.pl) |
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