README.md

metadata

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

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.