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README.md

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+# BBC News Topic Classification
+
+Dataset on [BBC News Topic Classification](https://www.kaggle.com/yufengdev/bbc-text-categorization/data): 2225 articles, each labeled under one of 5 categories: business, entertainment, politics, sport or tech.

prepare.py

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+import pandas as pd
+from collections import Counter
+import json
+import random
+
+
+df = pd.read_csv("bbc-text.csv")
+df.fillna('', inplace=True)
+print(df)
+
+label2id = {label: idx for idx, label in enumerate(df['category'].unique())}
+
+rows = [{'text': row['text'].strip(),
+                    'label': label2id[row['category']],
+                    'label_text': row['category'],
+                    } for idx, row in df.iterrows()]
+
+random.seed(42)
+random.shuffle(rows)
+
+num_test = 1000
+splits = {'test': rows[0:num_test], 'train': rows[num_test:]}
+
+print("Train:", len(splits['train']))
+print("Test:", len(splits['test']))
+
+num_labels = Counter()
+
+for row in splits['test']:
+    num_labels[row['label']] += 1
+print(num_labels)
+
+for split in ['train', 'test']:
+    with open(f'{split}.jsonl', 'w') as fOut:
+        for row in splits[split]:
+            fOut.write(json.dumps(row)+"\n")