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| from datasets import load_dataset | |
| import streamlit as st | |
| from clarin_datasets.dataset_to_show import DatasetToShow | |
| class KpwrNerDataset(DatasetToShow): | |
| def __init__(self): | |
| DatasetToShow.__init__(self) | |
| self.dataset_name = "clarin-pl/kpwr-ner" | |
| self.description = """ | |
| KPWR-NER is a part the Polish Corpus of Wrocław University of Technology (Korpus Języka | |
| Polskiego Politechniki Wrocławskiej). Its objective is named entity recognition for fine-grained categories | |
| of entities. It is the ‘n82’ version of the KPWr, which means that number of classes is restricted to 82 ( | |
| originally 120). During corpus creation, texts were annotated by humans from various sources, covering many | |
| domains and genres. | |
| Tasks (input, output and metrics) | |
| Named entity recognition (NER) - tagging entities in text with their corresponding type. | |
| Input ('tokens' column): sequence of tokens | |
| Output ('ner' column): sequence of predicted tokens’ classes in BIO notation (82 possible classes, described | |
| in detail in the annotation guidelines) | |
| example: | |
| [‘Roboty’, ‘mają’, ‘kilkanaście’, ‘lat’, ‘i’, ‘pochodzą’, ‘z’, ‘USA’, ‘,’, ‘Wysokie’, ‘napięcie’, ‘jest’, | |
| ‘dużo’, ‘młodsze’, ‘,’, ‘powstało’, ‘w’, ‘Niemczech’, ‘.’] → [‘B-nam_pro_title’, ‘O’, ‘O’, ‘O’, ‘O’, ‘O’, | |
| ‘O’, ‘B-nam_loc_gpe_country’, ‘O’, ‘B-nam_pro_title’, ‘I-nam_pro_title’, ‘O’, ‘O’, ‘O’, ‘O’, ‘O’, ‘O’, | |
| ‘B-nam_loc_gpe_country’, ‘O’] | |
| """ | |
| def load_data(self): | |
| raw_dataset = load_dataset(self.dataset_name) | |
| self.data_dict = { | |
| subset: raw_dataset[subset].to_pandas() for subset in self.subsets | |
| } | |
| def show_dataset(self): | |
| header = st.container() | |
| description = st.container() | |
| dataframe_head = st.container() | |
| with header: | |
| st.title(self.dataset_name) | |
| with description: | |
| st.header("Dataset description") | |
| st.write(self.description) | |