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2025-02-20 01:02:10
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.Debugging

ostºÍfallback Indians often occur when there's a mismatch between the code and the setup. Let's break down the steps to resolve this:

  1. Check the entities: Entities like - and ¡¯ might not be recognized if the tokenizer isn't configured properly. Ensure your tokenizer handles such cases.
  2. Review the tokenization process: Make sure tokens aren't getting split in unintended ways. For example, ensuring "don¡¯t" stays as a single token.
  3. Examine input and output dimensions: Mismatches in tensor shapes commonly cause these errors. Verify that the input tensors' size matches the expected input size of your model.
  4. Update your libraries: Sometimes, bugs are fixed in newer versions. Ensure you're using the latest versions of TensorFlow and related libraries.
  5. Utilize debug tools: Tools like TensorBoard can help visualize the graph and identify where issues arise.
  6. Look at the traceback: The error message usually points to where the issue occurs. Follow the traceback to pinpoint the exact location.
  7. Validate data preprocessing: Ensure that data is preprocessed correctly without introducing unintended anomalies.
  8. Test with a minimal example: Simplify your code to a minimal example that reproduces the error. This can help isolate the problem.

By methodically going through these steps, you can identify and fix the underlying issue causing the Indian and fallback errors in your model.

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