# Condensed: AutoMM for Text - Multilingual Problems

Summary: This tutorial demonstrates implementing multilingual text classification using AutoMM, focusing on cross-lingual sentiment analysis of Amazon product reviews. It provides code for setting up datasets across multiple languages (English, German, French, Japanese), implementing German BERT fine-tuning, and achieving zero-shot cross-lingual transfer using DeBERTa-V3. Key functionalities include multilingual preset configuration, parameter-efficient fine-tuning, and direct multilingual processing without translation services. The tutorial helps with tasks involving multilingual text classification, model fine-tuning, and cross-lingual transfer learning, particularly useful for developers working with multi-language sentiment analysis applications.

*This is a condensed version that preserves essential implementation details and context.*

Here's the condensed tutorial focusing on key implementation details and concepts:

# AutoMM for Multilingual Text Classification

## Key Implementation Details

### 1. Dataset Setup
- Uses Cross-Lingual Amazon Product Review Sentiment dataset
- Contains reviews in English, German, French, and Japanese
- Binary sentiment classification (0=negative, 1=positive)

```python
# Load and prepare data
train_de_df = pd.read_csv('amazon_review_sentiment_cross_lingual/de_train.tsv',
                          sep='\t', 
                          header=None, 
                          names=['label', 'text'])
test_de_df = pd.read_csv('amazon_review_sentiment_cross_lingual/de_test.tsv',
                          sep='\t', 
                          header=None, 
                          names=['label', 'text'])
```

### 2. German BERT Finetuning

```python
predictor = MultiModalPredictor(label='label')
predictor.fit(train_de_df,
              hyperparameters={
                  'model.hf_text.checkpoint_name': 'bert-base-german-cased',
                  'optimization.max_epochs': 2
              })
```

**Important Note**: Model performs well on German but poorly on English data.

### 3. Cross-lingual Transfer

```python
predictor = MultiModalPredictor(label='label')
predictor.fit(train_en_df,
              presets='multilingual',
              hyperparameters={
                  'optimization.max_epochs': 2
              })
```

**Key Features**:
- Uses `presets="multilingual"` to enable zero-shot transfer
- Automatically uses DeBERTa-V3 for state-of-the-art performance
- Works across multiple languages (English, German, Japanese) without additional training

## Best Practices

1. Use `presets='multilingual'` for cross-lingual applications
2. Consider parameter-efficient finetuning for better performance
3. Model can perform zero-shot transfer to unseen languages
4. No need for translation services - direct multilingual processing

## Critical Configurations

```python
# Basic configuration
hyperparameters = {
    'model.hf_text.checkpoint_name': 'bert-base-german-cased',  # For German-specific
    'optimization.max_epochs': 2
}

# Multilingual configuration
hyperparameters = {
    'optimization.max_epochs': 2
}
presets = 'multilingual'
```

**Note**: For advanced use cases, refer to the Parameter-Efficient Finetuning tutorial for better performance.