YagiASAFAS/indonesia-news-classification-bert

Model Card for Indonesian News Classification Model

Model Description

This model is fine-tuned for the specific task of classifying Indonesian news articles (data were extracted from iqballx/indonesian_news_datasets) into predefined categories. It was trained using a dataset that was created by translating Indonesian news articles into English using a Neural Machine Translation (NMT) system and then labeling them with niksmer/ManiBERT, a model trained to classify political texts. The resulting dataset contains parallel corpora of Indonesian and English news texts alongside their corresponding categories.

Training Data

The training data consists of articles from the iqballx/indonesian_news_datasets which were translated to English and then labeled using the niksmer/ManiBERT model. The dataset includes various categories, capturing a wide array of topics.

Evaluation

The model was evaluated on a held-out test set, and its performance was measured in terms of accuracy. During the training process, the model's accuracy improved across multiple epochs, with the following accuracy scores achieved: 61.71% after the first epoch, 64.62% after the second epoch, 65.64% after the third epoch, and 65.27% after the fourth epoch. These results demonstrate the model's ability to consistently make correct classifications across different categories, indicating its robust performance.

Limitations and Bias

As with any machine learning model, it is important to recognize potential limitations and biases. The translation step could introduce errors or nuances that affect the labeling accuracy. Additionally, the ManiBERT model used for initial labeling was trained on political texts, which may limit its effectiveness on non-political news or introduce political bias.

How to Use the Model

To classify an Indonesian news article, you can use the script below:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model_name = "YagiASAFAS/indonesia-news-classification-bert"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Write Indonesian Text
inputs = tokenizer("[Indonesian Text]", return_tensors="pt")

outputs = model(**inputs)
predictions = torch.nn.functional.softmax(outputs.logits, dim=1)

id2label = model.config.id2label

predicted_class_index = torch.argmax(predictions, dim=1).item()

predicted_class_index

predicted_category = id2label.get(predicted_class_index)

print("Predicted Category:", predicted_category)

Label Mapping

Label ID	Label Text
0	Agriculture and Farmers
1	Anti-Growth Economy and Sustainability
2	Anti-Imperialism
3	Centralisation: Positive
4	Civic Mindedness: Positive
5	Constitutionalism: Negative
6	Constitutionalism: Positive
7	Controlled Economy
8	Corporatism/ Mixed Economy
9	Culture: Positive
10	Decentralisation: Positive
11	Democracy
12	Economic Goals
13	Economic Growth: Positive
14	Economic Orthodoxy
15	Economic Planning
16	Education Expansion
17	Education Limitation
18	Environmental Protection
19	Equality: Positive
20	European Community/Union or Latin America Integration: Negative
21	European Community/Union or Latin America Integration: Positive
22	Foreign Special Relationships: Negative
23	Foreign Special Relationships: Positive
24	Free Market Economy
25	Freedom and Human Rights
26	Governmental and Administrative Efficiency
27	Incentives: Positive
28	Internationalism: Negative
29	Internationalism: Positive
30	Labour Groups: Negative
31	Labour Groups: Positive
32	Law and Order
33	Market Regulation
34	Marxist Analysis: Positive
35	Military: Negative
36	Military: Positive
37	Multiculturalism: Negative
38	Multiculturalism: Positive
39	National Way of Life: Negative
40	National Way of Life: Positive
41	Nationalisation
42	Non-economic Demographic Groups
43	None
44	Peace
45	Political Authority
46	Political Corruption
47	Protectionism: Negative
48	Protectionism: Positive
49	Technology and Infrastructure: Positive
50	Traditional Morality: Negative
51	Traditional Morality: Positive
52	Underprivileged Minority Groups
53	Welfare State Expansion
54	Welfare State Limitation