Spaces:
Runtime error
Runtime error
File size: 8,988 Bytes
77405f7 c76a6b2 9f7f573 77405f7 9f7f573 77405f7 9f7f573 c76a6b2 9f7f573 802f11a 2b9022f 802f11a abb1c69 802f11a 9f7f573 90966f7 9f7f573 c76a6b2 9f7f573 c76a6b2 77405f7 9f7f573 802f11a c76a6b2 f10673c c76a6b2 d3fc096 abb1c69 d3fc096 c76a6b2 f10673c c76a6b2 8eb9cdc c76a6b2 f10673c 3362a6a abb1c69 c76a6b2 abb1c69 c76a6b2 77405f7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 |
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from datasets import load_dataset
from sklearn.manifold import TSNE
import streamlit as st
from clarin_datasets.dataset_to_show import DatasetToShow
from clarin_datasets.utils import embed_sentence, PLOT_COLOR_PALETTE
class KpwrNerDataset(DatasetToShow):
def __init__(self):
DatasetToShow.__init__(self)
self.data_dict_named = None
self.dataset_name = "clarin-pl/kpwr-ner"
self.description = [
f"""
Dataset link: https://huggingface.co/datasets/{self.dataset_name}
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
}
self.data_dict_named = {}
for subset in self.subsets:
references = raw_dataset[subset]["ner"]
references_named = [
[
raw_dataset[subset].features["ner"].feature.names[label]
for label in labels
]
for labels in references
]
self.data_dict_named[subset] = pd.DataFrame(
{
"tokens": self.data_dict[subset]["tokens"],
"ner": references_named,
}
)
def show_dataset(self):
header = st.container()
description = st.container()
dataframe_head = st.container()
class_distribution = st.container()
most_common_tokens = st.container()
tsne_projection = st.container()
with header:
st.title(self.dataset_name)
with description:
st.header("Dataset description")
st.write(self.description[0])
st.subheader(self.description[1])
st.write(self.description[2])
full_dataframe = pd.concat(self.data_dict.values(), axis="rows")
tokens_all = full_dataframe["tokens"].tolist()
tokens_all = [x for subarray in tokens_all for x in subarray]
labels_all = pd.concat(self.data_dict_named.values(), axis="rows")[
"ner"
].tolist()
labels_all = [x for subarray in labels_all for x in subarray]
with dataframe_head:
st.header("First 10 observations of the chosen subset")
selected_subset = st.selectbox(
label="Select subset to see", options=self.subsets
)
df_to_show = self.data_dict[selected_subset].head(10)
st.dataframe(df_to_show)
st.text_area(label="LaTeX code", value=df_to_show.style.to_latex())
class_distribution_dict = {}
for subset in self.subsets:
all_labels_from_subset = self.data_dict_named[subset]["ner"].tolist()
all_labels_from_subset = [
x
for subarray in all_labels_from_subset
for x in subarray
if x != "O" and not x.startswith("I-")
]
all_labels_from_subset = pd.Series(all_labels_from_subset)
class_distribution_dict[subset] = (
all_labels_from_subset.value_counts(normalize=True)
.sort_index()
.reset_index()
.rename({"index": "class", 0: subset}, axis="columns")
)
class_distribution_df = pd.merge(
class_distribution_dict["train"],
class_distribution_dict["test"],
on="class",
)
with class_distribution:
st.header("Class distribution in each subset (without 'O' and 'I-*')")
st.dataframe(class_distribution_df)
st.text_area(
label="LaTeX code", value=class_distribution_df.style.to_latex()
)
# Most common tokens from selected class (without 0)
full_df_unzipped = pd.DataFrame(
{
"token": tokens_all,
"ner": labels_all,
}
)
full_df_unzipped = full_df_unzipped.loc[
(full_df_unzipped["ner"] != "O")
& ~(full_df_unzipped["ner"].str.startswith("I-"))
]
possible_options = sorted(full_df_unzipped["ner"].unique())
with most_common_tokens:
st.header(
"10 most common tokens from selected class (without 'O' and 'I-*')"
)
selected_class = st.selectbox(
label="Select class to show", options=possible_options
)
df_to_show = (
full_df_unzipped.loc[full_df_unzipped["ner"] == selected_class]
.groupby(["token"])
.count()
.reset_index()
.rename({"ner": "no_of_occurrences"}, axis=1)
.sort_values(by="no_of_occurrences", ascending=False)
.reset_index(drop=True)
.head(10)
)
st.dataframe(df_to_show)
st.text_area(label="LaTeX code", value=df_to_show.style.to_latex())
SHOW_TSNE_PROJECTION = False
if SHOW_TSNE_PROJECTION:
with tsne_projection:
st.header("t-SNE projection of the dataset")
subset_to_project = st.selectbox(
label="Select subset to project", options=self.subsets
)
tokens_unzipped = self.data_dict_named[subset_to_project]["tokens"].tolist()
tokens_unzipped = np.array([x for subarray in tokens_unzipped for x in subarray])
labels_unzipped = self.data_dict_named[subset_to_project]["ner"].tolist()
labels_unzipped = np.array([x for subarray in labels_unzipped for x in subarray])
df_unzipped = pd.DataFrame(
{
"tokens": tokens_unzipped,
"ner": labels_unzipped,
}
)
df_unzipped = df_unzipped.loc[
(df_unzipped["ner"] != "O")
& ~(df_unzipped["ner"].str.startswith("I-"))
]
tokens_unzipped = df_unzipped["tokens"].values
labels_unzipped = df_unzipped["ner"].values
mapping_dict = {name: number for number, name in enumerate(set(labels_unzipped))}
labels_as_ints = [mapping_dict[label] for label in labels_unzipped]
embedded_tokens = np.array(
[embed_sentence(x) for x in tokens_unzipped]
)
reducer = TSNE(
n_components=2
)
transformed_embeddings = reducer.fit_transform(embedded_tokens)
fig, ax = plt.subplots()
ax.scatter(
x=transformed_embeddings[:, 0],
y=transformed_embeddings[:, 1],
c=[
PLOT_COLOR_PALETTE[i] for i in labels_as_ints
]
)
st.pyplot(fig)
|