scripts/hf_model_config.py

#!/usr/bin/env python3
"""
Hugging Face model configuration for TagTransformer
"""

from transformers import PretrainedConfig
from typing import Dict, Any, Optional

class TagTransformerConfig(PretrainedConfig):
    """Configuration class for TagTransformer model"""
    
    model_type = "tag_transformer"
    
    def __init__(
        self,
        src_vocab_size: int = 1000,
        trg_vocab_size: int = 1000,
        embed_dim: int = 256,
        nb_heads: int = 4,
        src_hid_size: int = 1024,
        src_nb_layers: int = 4,
        trg_hid_size: int = 1024,
        trg_nb_layers: int = 4,
        dropout_p: float = 0.1,
        tie_trg_embed: bool = True,
        label_smooth: float = 0.1,
        max_length: int = 100,
        nb_attr: int = 0,
        **kwargs
    ):
        super().__init__(**kwargs)
        
        self.src_vocab_size = src_vocab_size
        self.trg_vocab_size = trg_vocab_size
        self.embed_dim = embed_dim
        self.nb_heads = nb_heads
        self.src_hid_size = src_hid_size
        self.src_nb_layers = src_nb_layers
        self.trg_hid_size = trg_hid_size
        self.trg_nb_layers = trg_nb_layers
        self.dropout_p = dropout_p
        self.tie_trg_embed = tie_trg_embed
        self.label_smooth = label_smooth
        self.max_length = max_length
        self.nb_attr = nb_attr

class TagTransformerForMorphologicalReinflection:
    """Hugging Face model wrapper for TagTransformer"""
    
    def __init__(self, config: TagTransformerConfig):
        self.config = config
        self.model = None
        
    def from_pretrained(self, model_path: str):
        """Load model from pretrained checkpoint"""
        import torch
        from transformer import TagTransformer
        
        # Load configuration
        config = TagTransformerConfig.from_pretrained(model_path)
        
        # Create model
        model = TagTransformer(
            src_vocab_size=config.src_vocab_size,
            trg_vocab_size=config.trg_vocab_size,
            embed_dim=config.embed_dim,
            nb_heads=config.nb_heads,
            src_hid_size=config.src_hid_size,
            src_nb_layers=config.src_nb_layers,
            trg_hid_size=config.trg_hid_size,
            trg_nb_layers=config.trg_nb_layers,
            dropout_p=config.dropout_p,
            tie_trg_embed=config.tie_trg_embed,
            label_smooth=config.label_smooth,
            nb_attr=config.nb_attr,
            src_c2i={},  # Will be loaded separately
            trg_c2i={},  # Will be loaded separately
            attr_c2i={},
        )
        
        # Load state dict
        state_dict = torch.load(f"{model_path}/pytorch_model.bin", map_location='cpu')
        model.load_state_dict(state_dict)
        
        self.model = model
        return self
    
    def save_pretrained(self, save_path: str):
        """Save model in Hugging Face format"""
        import torch
        import json
        from pathlib import Path
        
        save_path = Path(save_path)
        save_path.mkdir(parents=True, exist_ok=True)
        
        # Save model state dict
        torch.save(self.model.state_dict(), save_path / "pytorch_model.bin")
        
        # Save configuration
        self.config.save_pretrained(save_path)
        
        # Save vocabularies if available
        if hasattr(self.model, 'src_c2i') and self.model.src_c2i:
            with open(save_path / "src_vocab.json", "w") as f:
                json.dump(self.model.src_c2i, f, indent=2)
        
        if hasattr(self.model, 'trg_c2i') and self.model.trg_c2i:
            with open(save_path / "tgt_vocab.json", "w") as f:
                json.dump(self.model.trg_c2i, f, indent=2)
    
    def generate(self, input_ids, max_length: int = 100, **kwargs):
        """Generate predictions for morphological reinflection"""
        import torch
        
        self.model.eval()
        with torch.no_grad():
            # Simple greedy generation
            # This is a simplified version - you might want to implement beam search
            output = self.model(input_ids, **kwargs)
            predictions = torch.argmax(output, dim=-1)
            
        return predictions

def create_model_card(model_name: str, dataset_name: str, task: str = "morphological-reinflection") -> str:
    """Create a model card for Hugging Face Hub"""
    
    model_card = f"""---
license: mit
tags:
- morphological-reinflection
- transformer
- nlp
- linguistics
datasets:
- {dataset_name}
metrics:
- accuracy
- bleu
model-index:
- name: {model_name}
  results:
  - task:
      type: morphological-reinflection
      name: Morphological Reinflection
    dataset:
      type: {dataset_name}
      name: {dataset_name}
    metrics:
    - type: accuracy
      value: 0.0
      name: Accuracy
    - type: bleu
      value: 0.0
      name: BLEU Score
---

# {model_name}

This model is a TagTransformer for morphological reinflection tasks. It can transform words from one morphological form to another based on linguistic features.

## Model Description

- **Model type**: TagTransformer
- **Task**: Morphological Reinflection
- **Language**: Multiple languages (depends on training data)
- **Architecture**: Encoder-Decoder Transformer with special feature embeddings

## Usage

```python
from transformers import AutoModel, AutoTokenizer

# Load model and tokenizer
model = AutoModel.from_pretrained("{model_name}")
tokenizer = AutoTokenizer.from_pretrained("{model_name}")

# Example usage
input_text = "example input"
output = model.generate(input_text)
```

## Training Data

This model was trained on the {dataset_name} dataset.

## Training Procedure

The model was trained using:
- Optimizer: AdamW
- Learning rate: 0.001
- Batch size: 400
- Mixed precision training
- Gradient accumulation

## Evaluation

The model achieves the following results on the test set:
- Accuracy: TBD
- BLEU Score: TBD

## Limitations and Bias

This model may have limitations in:
- Handling rare morphological patterns
- Cross-lingual generalization
- Domain-specific terminology

## Citation

```bibtex
@misc{{{model_name.lower().replace('-', '_')},
  title={{{model_name}}},
  author={{Your Name}},
  year={{2024}},
  publisher={{Hugging Face}},
  howpublished={{\\url{{https://huggingface.co/{model_name}}}}}
}}
```
"""
    
    return model_card