| --- |
| language: |
| - "en" |
| license: "apache-2.0" |
| tags: |
| - "educational" |
| - "transformers" |
| - "custom-model" |
| datasets: |
| - "dummy-dataset" |
| metrics: |
| - "dummy-metric" |
| model-index: |
| - name: "MinimalTransformer" |
| results: |
| - task: |
| name: "Dummy Task" |
| type: "text-classification" |
| dataset: |
| name: "dummy-dataset" |
| type: "text-classification" |
| metrics: |
| - name: "Dummy Metric" |
| type: "accuracy" |
| value: 0.0 |
| --- |
| |
| ## 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. |
|
|
