---
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.