Custom WordPiece Tokenizer (Trained on WikiText-103 Raw v1)

Model Overview

• Custom [CLS] and [SEP] special tokens.
• WordPiece subword segmentation with ## prefix for subwords.
• Template-based post-processing for both single and paired sequences.
• Configured decoding using the WordPiece decoder for seamless reconstruction of original text.

Training Details

Dataset

• Name: WikiText-103 Raw v1
• Source: High-quality, long-form Wikipedia articles.
• Split Used: train
• Size: ~103 million tokens
• Loading Method: Streaming mode for efficient large-scale training without local storage bottlenecks.

Tokenizer Configuration

• Model Type: WordPiece
• Vocabulary Size: 60,000 (medium-scale for general-purpose LLMs)
• Lowercasing: Enabled
• Special Tokens:
  • [CLS] — Classification token
  • [SEP] — Separator token
  • [UNK] — Unknown token
  • [PAD] — Padding token
  • [MASK] — Masking token (MLM tasks)
• Post-Processing Template:
  • Single Sequence: [CLS] $A [SEP]
  • Paired Sequences: [CLS] $A [SEP] $B [SEP]
• Decoder: WordPiece decoder with ## prefix handling.

Training Method

• Corpus Source: Streaming iterator from WikiText-103 Raw v1 (train split)
• Batch Size: 1000 lines per batch
• Trainer: WordPieceTrainer from Hugging Face tokenizers library
• Special Tokens Added: [CLS], [SEP], [UNK], [PAD], [MASK]

Intended Uses & Limitations

Intended Uses

• Pre-tokenization for training Transformer-based LLMs.
• Downstream NLP tasks:
  • Language modeling
  • Text classification
  • Question answering
  • Summarization

Limitations

• Trained exclusively on English Wikipedia text — performance may degrade in informal, domain-specific, or multilingual contexts.
• May inherit biases present in Wikipedia data.

License

Citation