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BertTokenizer4D

Delphi implementation of the BERT tokenizer

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README

BertTokenizer4D

A Delphi implementation of the BERT tokenizer — TBertTokenizer — inspired by the .NET FastBertTokenizer. It supports WordPiece tokenization and converts text into token ID sequences ready for input into BERT models via ONNX.

📦 Features

  • Load vocabulary from file (vocab.txt) or stream
  • Load tokenizer configuration from Hugging Face (tokenizer.json)
  • Converts raw text into token ID arrays
  • Decodes token IDs back into readable text
  • Compatible with TONNXRuntime for ONNX inference

📁 Project Structure

  • Main unit: Src/BertTokenizer/BertTokenizer.pas
  • Core class: TBertTokenizer

🚀 Quick Start

pascal
uses
  BertTokenizer, BertTokenizer.Extensions;

procedure LoadTokenizerAndEncode(const APath: string);
begin
  var Tokenizer := TBertTokenizer.Create;
  try 
    Tokenizer.LoadFromHuggingFace('TaylorAI/bge-micro-v2');
    var Tokens := Tokenizer.Encode('Hello, world!');
    // TokenIds can now be passed to a model using TONNXRuntime
  finally
    Tokenizer.Free;
  end;
end;

🧠 Public API

Method Description
LoadVocabulary(FileName, ...) Loads vocabulary from a vocab.txt file
LoadVocabularyFromStream(Stream, ...) Loads vocabulary from a stream
LoadTokenizerJson(FileName) Loads tokenizer from a Hugging Face tokenizer.json
LoadTokenizerJsonFromStream(Stream) Loads tokenizer from a JSON stream
Encode(Text) Tokenizes input text and returns an array of token IDs
Decode(Tokens) Decodes an array of token IDs back to text
uses BertTokenizer.Extensions
LoadFromHuggingFace(HuggingFaceRepo) Loads tokenizer from Hugging Face repo by name

✅ Supported Tokenizer Formats

  • vocab.txt — standard BERT vocabulary file
  • tokenizer.json — Hugging Face tokenizer format (WordPiece-based)

✅ Dependencies

  • Delphi 10.2 Tokyo+
  • No external dependencies required for core functionality
  • Optional test suite using DUnitX

🧪 Test Coverage

This project includes unit tests using DUnitX.

🤖 BERT + ONNX Integration

The output of Encode is compatible with ONNX BERT models. You can use TONNXRuntime to run inference on tokenized input (input_ids).

📄 License

MIT License — free to use, modify, and distribute.