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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)
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