File size: 6,393 Bytes
77405f7
 
 
9f7f573
 
77405f7
9f7f573
 
 
77405f7
 
 
 
9f7f573
 
 
 
 
2b9d84c
9f7f573
2b9d84c
2b9022f
 
 
2b9d84c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
abb1c69
2b9d84c
9f7f573
 
90966f7
 
 
 
2b9d84c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9f7f573
 
2b9d84c
 
 
 
77405f7
2b9d84c
 
 
 
 
 
 
 
 
 
 
 
abb1c69
 
 
 
 
 
2b9d84c
 
 
 
 
 
 
abb1c69
2b9d84c
 
 
 
abb1c69
 
 
2b9d84c
 
 
 
 
 
 
 
 
 
 
 
 
 
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
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
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 (
    PLOT_COLOR_PALETTE,
    embed_sentence
)


class NkjpPosDataset(DatasetToShow):
    def __init__(self):
        DatasetToShow.__init__(self)
        self.data_dict_named = None
        self.dataset_name = "clarin-pl/nkjp-pos"
        self.description = [
            f"""
            Dataset link: https://huggingface.co/datasets/{self.dataset_name}
            
            NKJP-POS is a part the National Corpus of Polish (Narodowy Korpus Języka Polskiego). 
            Its objective is part-of-speech tagging, e.g. nouns, verbs, adjectives, adverbs, etc. During the creation of 
            corpus, texts of were annotated by humans from various sources, covering many domains and genres. 
            """,
            "Tasks (input, output and metrics)",
            """
            Part-of-speech tagging (POS tagging) - tagging words in text with their corresponding part of speech.

            Input ('tokens' column): sequence of tokens
            
            Output ('pos_tags' column): sequence of predicted tokens’ classes (35 possible classes, described in detail in the annotation guidelines)
            
            Measurements: F1-score (seqeval)
            
            Example:
            
            Input: ['Zarejestruj', 'się', 'jako', 'bezrobotny', '.']
            
            Input (translated by DeepL): Register as unemployed.
            
            Output: ['impt', 'qub', 'conj', 'subst', 'interp']
            """,
        ]

    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]["pos_tags"]
            references_named = [
                [
                    raw_dataset[subset].features["pos_tags"].feature.names[label]
                    for label in labels
                ]
                for labels in references
            ]
            self.data_dict_named[subset] = pd.DataFrame(
                {
                    "tokens": self.data_dict[subset]["tokens"],
                    "tags": references_named,
                }
            )

    def show_dataset(self):
        header = st.container()
        description = st.container()
        dataframe_head = st.container()
        class_distribution = 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])

        with dataframe_head:
            st.header("First 10 observations of the chosen subset")
            subset_to_show = st.selectbox(
                label="Select subset to see", options=self.subsets
            )
            df_to_show = (
                self.data_dict[subset_to_show].head(10).drop("id", axis="columns")
            )
            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]["tags"].tolist()
            all_labels_from_subset = [
                x for subarray in all_labels_from_subset for x in subarray
            ]
            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")
            st.dataframe(class_distribution_df)
            st.text_area(
                label="LaTeX code", value=class_distribution_df.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]["tags"].tolist()
                labels_unzipped = np.array([x for subarray in labels_unzipped for x in subarray])
                df_unzipped = pd.DataFrame(
                    {
                        "tokens": tokens_unzipped,
                        "tags": labels_unzipped,
                    }
                )
                tokens_unzipped = df_unzipped["tokens"].values
                labels_unzipped = df_unzipped["tags"].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)