Model Card for Custom Minimal Transformer
Model Description
This is a custom transformer model designed for educational purposes. It demonstrates the basic structure of a transformer model using PyTorch and integrates a pre-trained tokenizer from the Hugging Face library (bert-base-uncased).
Architecture
The model, MinimalTransformer, is a simplified transformer architecture consisting of:
- Multi-head attention mechanism (
nn.MultiheadAttention).
- Layer normalization (
nn.LayerNorm).
- A feed-forward network composed of linear layers and ReLU activation.
It demonstrates basic transformer concepts while being more lightweight and easier to understand than full-scale models like BERT or GPT.
Training
The model was trained on a small, manually created dataset consisting of simple sentences like "Hello world", "Transformers are great", and "PyTorch is fun". It's intended for basic demonstrations and not for achieving state-of-the-art results on complex tasks.
Tokenizer
The tokenizer used is the AutoTokenizer from Hugging Face, specifically the "bert-base-uncased" variant. It handles tokenization, adding special tokens, and converting tokens to their respective IDs in the BERT vocabulary.
Usage
The model can be used for basic NLP tasks and demonstrations. To use the model:
- Load the saved model weights into the
MinimalTransformer architecture.
- Tokenize input sentences using the provided tokenizer.
- Pass the tokenized input through the model for inference.
Limitations and Bias
- The model's performance is limited due to its simplistic nature and the small training dataset.
- As it uses a pre-trained BERT tokenizer, any biases present in the BERT model may be transferred to this model.
Acknowledgements
This model was created for educational purposes and is based on the PyTorch and Hugging Face Transformers libraries.
