Update README.md

README.md

@@ -45,32 +45,10 @@ input_data = torch.rand((32, 10, 784))  # Adjust shape according to your needs
 with torch.no_grad():  # Assuming inference only
     output = model(input_data)
 
-### To-Do
-# The `output` is a dictionary with 'encoder_final' and 'decoder_final' keys
-# encoded_representation = output['encoder_final']
-# reconstructed_data = output['decoder_final']
-```
-
-## Training Data
-*Omitted - to be filled in with details about the training data used for the model.*
-
-## Training Procedure
-*Omitted - to be filled in with details about the training procedure, including optimization strategies, loss functions, and regularization techniques.*
-
-## Performance
-*Omitted - to be filled in with performance metrics on relevant evaluation datasets or benchmarks.*
-
-## Limitations
-The performance of the `AutoEncoder` is highly dependent on the architecture configuration and the quality and quantity of the training data. As with any autoencoder, there is no guarantee that the model will learn useful or interpretable features without proper tuning and validation.
 
-## Authors
-*Omitted - to be filled in with the names of the model's creators or maintainers.*
-
-## Ethical Considerations
-When using this model, consider the biases that may be present in the training data, as the model will inevitably learn these biases. Care should be taken to avoid using the model in situations where these biases could lead to unfair or discriminatory outcomes.
-
-## Citation
-*Omitted - to be filled in with citation details if the model is part of a published work or if there is a specific way to cite the use of the model.*
-
-
-The provided Python code is a basic example showing how to instantiate the model, how to create some dummy input data, and how to run data through the model to get the encoded and reconstructed output. Please ensure you have the required dependencies installed and adapt the code according to your specific setup and requirements.
+# The `output` is a dataclass with
+output.logits
+output.labels
+output.hidden_state
+output.loss
+```