fix embedding encode

[tastelikefeet]

PR type

• Bug Fix
• New Feature
• Document Updates
• More Models or Datasets Support

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[gemini-code-assist]

Summary of Changes

Hello @tastelikefeet, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request addresses a critical bug within the embedding encoding process, specifically concerning the handling of negative samples. The fix ensures that the necessary data structures (lists associated with negative_{key} in the _encoded dictionary) are correctly initialized before any data is appended, thereby preventing potential KeyError exceptions and ensuring the stable and accurate generation of embeddings.

Highlights

• Bug Fix: Resolved an issue in the _embedding_encode method where negative_{key} lists were not properly initialized before attempting to append encoded negative samples, which could lead to runtime errors.
• Code Robustness: Improved the robustness of the embedding encoding logic by ensuring dictionary keys for negative samples are present before data manipulation.

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[gemini-code-assist]

Code Review

This pull request fixes a potential KeyError in the _embedding_encode method. The original code did not ensure that a list was initialized for negative_{key} before attempting to append to it, which could happen if a key was present in a negative sample's encoding but not in the positive sample's. The fix adds a check to initialize the list if it doesn't exist. The change is correct and improves the robustness of the embedding encoding process. I've suggested a minor refactoring to make the code more concise using dict.setdefault.

[gemini-code-assist]

This correctly fixes a potential KeyError. For conciseness, you could use dict.setdefault to achieve the same result in a single line.

                    _encoded.setdefault(f'negative_{key}', []).append(negative_encoded[key])