ComfyUI Node: 🔍 FW Token Count Ranker

🔍 FW Token Count Ranker:

The TokenCountRanker is a specialized node designed to analyze and rank text segments and individual words based on their token count. This node is particularly useful for tasks that require an understanding of the tokenization process, such as optimizing text input for models that have token limits or analyzing the complexity of text data. By sorting segments and words by their token count, the TokenCountRanker helps you identify which parts of the text are more complex or dense in terms of token usage. This can be beneficial for refining text inputs to ensure they are within the token limits of specific models or for gaining insights into the structure and composition of the text. The node leverages the tokenization capabilities of a CLIP model to perform its analysis, making it a powerful tool for text processing in AI applications.

🔍 FW Token Count Ranker Input Parameters:

clip

The clip parameter is a reference to a CLIP model instance that is used for tokenizing the text. This parameter is crucial as it provides the tokenization functionality needed to break down the text into tokens. The CLIP model should be compatible with the text input to ensure accurate tokenization. There are no specific minimum, maximum, or default values for this parameter, but it must be a valid CLIP model instance.

text

The text parameter is the string input that you want to analyze and rank based on token count. This parameter is essential as it provides the content that will be tokenized and evaluated. The text can be any string, and there are no inherent restrictions on its length or content. However, the complexity and length of the text will directly impact the tokenization process and the resulting token counts.

🔍 FW Token Count Ranker Output Parameters:

Sorted Segments

The Sorted Segments output provides a list of text segments sorted by their token count in descending order. Each segment is accompanied by its respective token count, allowing you to quickly identify which segments are more token-dense. This output is valuable for understanding the distribution of tokens across different parts of the text and can help in optimizing text inputs for models with token limitations.

Sorted Words

The Sorted Words output offers a list of individual words sorted by their token count in descending order. Similar to the sorted segments, each word is paired with its token count, providing insights into which words are more complex or token-heavy. This output is useful for detailed text analysis and can aid in refining text inputs by highlighting words that may need simplification or adjustment.

🔍 FW Token Count Ranker Usage Tips:

• Use the TokenCountRanker to analyze text inputs before feeding them into models with strict token limits. This can help you identify and adjust segments or words that may cause the input to exceed the token limit.
• Leverage the sorted outputs to gain insights into the complexity of your text data. This can be particularly useful for tasks such as text summarization or simplification, where understanding token distribution is key.

🔍 FW Token Count Ranker Common Errors and Solutions:

Invalid CLIP Model

• Explanation: This error occurs when the clip parameter is not a valid CLIP model instance, leading to issues in the tokenization process.
• Solution: Ensure that the clip parameter is correctly set to a compatible CLIP model instance. Verify that the model is properly initialized and accessible within your environment.

Text Tokenization Failure

• Explanation: This error arises when the text input cannot be tokenized, possibly due to incompatible text encoding or unsupported characters.
• Solution: Check the text input for any unusual characters or encoding issues. Ensure that the text is in a format compatible with the CLIP model's tokenizer. Consider preprocessing the text to remove unsupported characters or convert it to a compatible encoding.