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--- |
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language: |
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- "en" |
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license: "apache-2.0" |
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tags: |
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- "educational" |
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- "transformers" |
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- "custom-model" |
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datasets: |
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- "dummy-dataset" |
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metrics: |
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- "dummy-metric" |
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model-index: |
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- name: "MinimalTransformer" |
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results: |
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- task: |
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name: "Dummy Task" |
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type: "text-classification" |
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dataset: |
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name: "dummy-dataset" |
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type: "text-classification" |
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metrics: |
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- name: "Dummy Metric" |
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type: "accuracy" |
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value: 0.0 |
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--- |
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## Model Card for Custom Minimal Transformer |
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### Model Description |
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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`). |
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### Architecture |
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The model, `MinimalTransformer`, is a simplified transformer architecture consisting of: |
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- Multi-head attention mechanism (`nn.MultiheadAttention`). |
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- Layer normalization (`nn.LayerNorm`). |
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- A feed-forward network composed of linear layers and ReLU activation. |
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It demonstrates basic transformer concepts while being more lightweight and easier to understand than full-scale models like BERT or GPT. |
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### Training |
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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. |
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### Tokenizer |
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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. |
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### Usage |
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The model can be used for basic NLP tasks and demonstrations. To use the model: |
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- Load the saved model weights into the `MinimalTransformer` architecture. |
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- Tokenize input sentences using the provided tokenizer. |
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- Pass the tokenized input through the model for inference. |
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### Limitations and Bias |
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- The model's performance is limited due to its simplistic nature and the small training dataset. |
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- As it uses a pre-trained BERT tokenizer, any biases present in the BERT model may be transferred to this model. |
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### Acknowledgements |
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This model was created for educational purposes and is based on the PyTorch and Hugging Face Transformers libraries. |
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