Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
json
Sub-tasks:
topic-classification
Languages:
Ukrainian
Size:
1K - 10K
DOI:
License:
| import json | |
| import polars as pl | |
| test = pl.read_ndjson("data/raw_test.jsonl") | |
| train = pl.read_ndjson("data/raw_train.jsonl") | |
| print(test) | |
| print(train) | |
| unique_test_labels = test.select("label").unique() | |
| unique_train_labels = train.select("label").unique() | |
| print("Unique labels in test data:", unique_test_labels) | |
| print("Unique labels in train data:", unique_train_labels) | |
| unique_concatenated = pl.concat([unique_test_labels, unique_train_labels]).unique() | |
| print("Unique labels in both test and train data:", unique_concatenated) | |
| print(unique_concatenated) | |
| labels = {} | |
| for idx, value in enumerate(unique_concatenated.rows()): | |
| print(idx, value[0]) | |
| labels[value[0]] = idx | |
| print(labels) | |
| with open("data/labels.json", "w") as f: | |
| json.dump(labels, f) | |