Dataset Card for bert-jp-sentiment-20250731-1035

Dataset Summary

This dataset contains Japanese e-commerce product review sentiment data for fine-tuning BERT models. The data combines historical training data with new human-verified sentiment labels from the RLHF feedback loop.

• Created: 2025-07-31T10:35:07.149235
• Version: 1.0
• Total samples: 113529

Dataset Structure

Data Instances

Each instance contains:

• text: Japanese review text
• labels: Sentiment label (1-5, where 1=Very Negative, 5=Very Positive)
• source: Data source (historical, user_feedback, ai_feedback, manual)
• validator_id: ID of human validator (for human-verified data)
• verification_id: ID of the verification record in the database

Data Splits

Split	Size
train	100914
validation	12615

Label Distribution

Training Set

• 1 (Very Negative): 18530
• 2 (Negative): 17260
• 3 (Neutral): 25542
• 4 (Positive): 19104
• 5 (Very Positive): 20478

Validation Set

• 1 (Very Negative): 2316
• 2 (Negative): 2158
• 3 (Neutral): 3193
• 4 (Positive): 2388
• 5 (Very Positive): 2560

Human Verification

• Total human-verified samples: 8755
• Number of validators: 4
• Verification sources: ai_feedback

Source Distribution

• historical: 104774
• ai_feedback: 8755

Preprocessing

The following preprocessing steps were applied:

• Text stripping and empty removal
• Encoding issue cleaning
• Stratified splitting

Recommended Usage

Model Training

from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForSequenceClassification

# Load dataset
dataset = load_dataset("HALDATA/bert-jp-sentiment-20250731-1035" if self.organization else metadata['dataset_name'])

# The labels are 1-5. They need to be mapped to 0-4 for training.
dataset = dataset.map(lambda example: {"labels": example["labels"] - 1})

# Load recommended model
model_name = "tohoku-nlp/bert-large-japanese-v2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=5)

# Tokenize data
def tokenize_function(examples):
    return tokenizer(examples["text"], truncation=True, padding=True, max_length=128)

tokenized_datasets = dataset.map(tokenize_function, batched=True)

Training Configuration

Recommended hyperparameters:

• Learning rate: 2e-5
• Batch size: 16
• Epochs: 3
• Max sequence length: 128
• Warmup ratio: 0.1

Dataset Creation

Source Data

1. Historical Data: Previous training datasets from production models
2. Human-Verified Data: New sentiment labels validated by human annotators through:
   • User feedback corrections
   • AI validation discrepancies
   • Manual annotation

Quality Control

• Text encoding validation
• Duplicate detection and removal
• Class balance analysis
• Validator consistency checks
• Minimum samples per class verification

Ethical Considerations

• All data has been anonymized and contains no personally identifiable information
• Human validators followed consistent annotation guidelines
• Data sources are clearly tracked for transparency

Limitations

• Dataset is specific to Japanese e-commerce product reviews
• Sentiment categories reflect the original annotation scheme (1-5 scale)
• May contain domain-specific terminology and expressions

License

This dataset is intended for research and development purposes.

Citation

@dataset{japanese_sentiment_2025,
  title = {Japanese E-commerce Sentiment Dataset},
  author = {RLHF Training Pipeline},
  year = {2025},
  url = {https://huggingface.co/datasets/HALDATA/bert-jp-sentiment-20250731-1035}
}