indo-sentiment-analysis EU AI Act Compliance Profile

Model Card for IndoBERT Sentiment Analysis

This model is fine-tuned from indobenchmark/indobert-base-p1 for binary sentiment classification (Positive/Negative) on Indonesian text.

Model Details

Model Description

• Developed by: agufsamudra
• Model type: Text Classification
• Language(s): Indonesian (id)
• License: Apache-2.0
• Fine-tuned from model: indobenchmark/indobert-base-p1

Model Sources

• Repository: https://huggingface.co/agufsamudra/indo-sentiment-analysis

Uses

Direct Use

This model is intended for binary sentiment classification tasks in Indonesian language texts. It predicts whether a given text expresses positive or negative sentiment.

Out-of-Scope Use

The model is not designed to classify neutral sentiments or handle languages other than Indonesian.

Bias, Risks, and Limitations

• Bias: The model's performance is reliant on the quality and diversity of the training data. Biases in the dataset may influence predictions.
• Limitations: The model is limited to binary sentiment analysis and may not perform well on ambiguous or mixed-sentiment texts.

Recommendations

Users should validate predictions on a case-by-case basis for high-stakes applications.

How to Get Started with the Model

from transformers import BertTokenizer, BertForSequenceClassification  

# Load the tokenizer and model  
tokenizer = BertTokenizer.from_pretrained("agufsamudra/indo-sentiment-analysis")  
model = BertForSequenceClassification.from_pretrained("agufsamudra/indo-sentiment-analysis")  

# Example usage  
text = "Saya sangat puas dengan pelayanan ini!"  
inputs = tokenizer(text, return_tensors="pt", padding="max_length", truncation=True, max_length=128)  
outputs = model(**inputs)  
logits = outputs.logits  
prediction = logits.argmax(-1).item()  

label = "Positive" if prediction == 1 else "N

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Base Model