Add components to NKJP POS dataset

clarin_datasets/nkjp_pos_dataset.py

@@ -8,16 +8,103 @@ from clarin_datasets.dataset_to_show import DatasetToShow
 class NkjpPosDataset(DatasetToShow):
     def __init__(self):
         DatasetToShow.__init__(self)
+        self.data_dict_named = None
         self.dataset_name = "clarin-pl/nkjp-pos"
-        self.description = """
-        
-        """
+        self.description = [
+            """
+            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):
-        pass
+        header = st.container()
+        description = st.container()
+        dataframe_head = st.container()
+        class_distribution = 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()
+            )