NeTS-lab/emg-10m-conv_test

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

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.