---
language: en
library_name: transformers
tags:
- emg
- morphology
- language-model
- causal-lm
- morpiece-tokenizer
license: apache-2.0
pipeline_tag: text-generation
---

# EMG Language Model

This is an EMG (Enhanced Morphological Generation) language model with MorPiece tokenizer.

## Model Details

- **Model Type**: Causal Language Model
- **Architecture**: EMG with morphological awareness
- **Tokenizer**: MorPiece (morphology-aware tokenization)
- **Parameters**: 79.75M
- **Vocabulary Size**: 60001

## Usage

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("your-username/your-model-name", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("your-username/your-model-name", trust_remote_code=True)

# Generate text
input_text = "The future of AI is"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)
```

## Model Architecture

The EMG model uses morphological awareness for better language understanding and generation.
The MorPiece tokenizer provides morphology-aware tokenization that better handles word formations.

## Training

This model was trained on conversational data with morphological enhancement.

## Limitations

- This model is designed for research purposes
- May not perform optimally on all downstream tasks without fine-tuning
- Requires trust_remote_code=True due to custom architecture

## Citation

If you use this model, please cite the original EMG paper and implementation.