Create Industry-Specific-Tokens_Tokenizer_ReadMe.md

Industry-Specific-Tokens_Tokenizer_ReadMe.md

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+This customized tokenizer is a necessary and practical component of the supply chain forecasting model.
+The custom tokenizer is designed for the Enhanced Business Model for Collaborative Predictive Supply Chain.  
+It prioritizes industry-specific tokens from a `vocab.json` file and uses Byte-Pair Encoding (BPE) for out-of-vocabulary (OOV) words.  
+It has dedicated tokens to handle various data and feature types expected in supply chain data.
+It handles the specific requirements of the model, including the custom industry-specific vocabulary, BPE training, and preparation of data for a Transformer.  
+
+Key Features:
+
+1.  **Custom Vocabulary Prioritization:**  The tokenizer initializes its
+    vocabulary from a `vocab.json` file.  This file contains predefined tokens
+    for common entities in the supply chain domain (e.g., specific SKUs,
+    store identifiers, manufacturer plant codes).  These tokens are given
+    precedence over tokens learned through BPE.
+
+2.  **Byte-Pair Encoding (BPE) for OOV Handling:**  To handle variations in
+    product names, new SKUs, or other unseen words, the tokenizer incorporates
+    BPE.  The `train_bpe` method allows the tokenizer to learn subword units
+    from a provided text corpus, enabling it to represent words not present in
+    the initial `vocab.json`.
+
+3.  **Data Preprocessing for Transformers:**  The `prepare_for_model` method
+    is crucial for integrating the tokenizer with the model's data pipeline.
+    It takes a Pandas DataFrame (containing features like timestamp, SKU,
+    store ID, quantity, price, promotions) and transforms each row into a
+    sequence of token IDs and an attention mask, ready for input to a
+    Transformer model.  This includes:
+    *   Constructing a formatted input string from DataFrame columns.
+    *   Tokenizing the string using the custom vocabulary and BPE.
+    *   Padding (or truncating) sequences to a specified `max_length`.
+    *   Creating an attention mask to indicate valid tokens vs. padding.
+
+4.  **Standard Tokenization Operations:**  The tokenizer provides standard
+    methods expected of a tokenizer:
+    *   `encode`: Converts text to a list of tokens.
+    *   `encode_as_ids`: Converts text to a list of token IDs.
+    *   `decode`: Converts token IDs back to text.
+    *   `token_to_id`: Converts a token string to its ID.
+    *   `id_to_token`: Converts a token ID to its string representation.
+    *   `get_vocab_size`: Returns size of vocabulary.
+
+5.  **Saving and Loading:**  The `save` and `from_pretrained` methods allow
+    for easy persistence and reuse of the trained tokenizer, including both
+    the tokenizer configuration (in Hugging Face's `tokenizer.json` format)
+    and a copy of the `vocab.json`.
+
+6.  **Integration with `tokenizers` Library:**  The tokenizer is built using
+    the `tokenizers` library from Hugging Face, ensuring efficiency and
+    compatibility with other Hugging Face tools.
+
+7. **Normalization and Pre-Tokenization**: Includes lowercase and Unicode
+    normalization and pre-tokenization (splitting on whitespace and individual
+    digits.)
+
+8. **Special Tokens**: Handles special tokens ([UNK], [CLS], [SEP], [PAD],
+    [MASK]) for Transformer models.
+
+The Tokenizer2 script also includes a comprehensive example usage section demonstrating how to create, train, use, save, and load the tokenizer. This tokenizer is a
+critical component for bridging the gap between raw supply chain data and a Transformer-based forecasting model.
+
+Key aspects and explanations:
+
+*   **Custom Vocabulary Handling:**  The `vocab.json` gives priority to the industry-specific tokens.
+
+*   **Complete:** This code includes creating a dummy `vocab.json`, training the BPE model, encoding, decoding, saving, loading, and preparing a Pandas DataFrame for input.
+*   **BPE Training:**  The `train_bpe` method is implemented using the `tokenizers` library's `BpeTrainer`.  This allows the tokenizer to learn merges for words *not* in the initial vocabulary.  This is crucial for handling variations in product names, new SKUs, etc.
+*   **Normalization and Pre-tokenization:** Includes standard normalization (lowercase, Unicode normalization) and pre-tokenization (splitting on whitespace and individual digits).
+*   **Special Tokens:**  Correctly handles special tokens (`[UNK]`, `[CLS]`, `[SEP]`, `[PAD]`, `[MASK]`) for Transformer models.
+*   **Encoding and Decoding:**  Provides methods for encoding text into tokens and IDs, and for decoding IDs back into text.
+*   **`token_to_id` and `id_to_token`:**  Added methods for converting between tokens and IDs.
+*   **`get_vocab_size`:** Added for completeness.
+*   **Saving and Loading:** Implements `save` and `from_pretrained` methods for persisting the tokenizer, including both the `tokenizer.json` (Hugging Face's configuration) and a copy of the `vocab.json`.
+*   **`prepare_for_model` Method:**  This is the *most important* addition.  This method takes a Pandas DataFrame as input and:
+    *   Constructs an input string for each row, combining the relevant features (timestamp, SKU, store ID, etc.).  *This is where you define the input format for your model.*
+    *   Tokenizes the string.
+    *   Handles padding (or truncation) to the `max_length`.
+    *   Creates the attention mask.
+    *   Returns the `input_ids` (list of token ID sequences) and `attention_masks` ready for use in a Transformer model.
+*   **Clear Example Usage:**  The `if __name__ == "__main__":` block provides a comprehensive example showing how to use all the key methods.
+*   **Error Handling:** Includes a check for the existence of the `vocab.json` file.
+*   **Type Hinting:** Uses type hints for better code clarity and maintainability.
+* **Pandas Integration:** The tokenizer is designed to work directly with Pandas DataFrames, which are commonly used for this type of data.
+