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--- |
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license: mit |
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language: |
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- en |
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inference: true |
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--- |
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# AutoEncoder for Dimensionality Reduction |
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## Model Description |
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The `AutoEncoder` presented here is a neural network model based on an encoder-decoder architecture. It is designed to learn efficient representations (encodings) of the input data, typically for dimensionality reduction purposes. The encoder compresses the input into a lower-dimensional latent space, while the decoder reconstructs the input data from the latent representation. |
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This model is flexible and can be configured with different layer types such as linear layers, LSTMs, GRUs, or RNNs, and can handle bidirectional sequence processing. The model is configured to be used with the Hugging Face Transformers library, allowing for easy download and deployment. |
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## Intended Use |
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This `AutoEncoder` is suitable for unsupervised learning tasks where dimensionality reduction or feature learning is desired. Examples include anomaly detection, data compression, and preprocessing for other complex tasks such as feature reduction before classification. |
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## Basic Usage in Python |
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Here are some simple examples of how to use the `AutoEncoder` model in Python: |
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```python |
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from transformers import AutoConfig, AutoModel |
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config = AutoConfig.from_pretrained("amaye15/autoencoder", trust_remote_code = True) |
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# Let's say you want to change the input_dim and latent_dim |
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config.input_dim = 1024 # New input dimension |
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config.latent_dim = 64 # New latent dimension |
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# Similarly, update other parameters as needed |
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config.layer_types = 'gru' # Change layer types to 'gru' |
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config.dropout_rate = 0.2 # Update dropout rate |
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config.num_layers = 4 # Change the number of layers |
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config.compression_rate = 0.6 # Update compression rate |
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config.bidirectional = False # Change to unidirectional |
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### Change Configuration |
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model = AutoModel.from_config(config, trust_remote_code = True) |
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# Example input data (batch_size, seq_len, input_dim) |
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input_data = torch.rand((32, 10, 784)) # Adjust shape according to your needs |
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# Perform encoding and decoding |
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with torch.no_grad(): # Assuming inference only |
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output = model(input_data) |
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# The `output` is a dataclass with |
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output.logits |
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output.labels |
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output.hidden_state |
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output.loss |
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``` |
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