# Advanced Tokenizer Benchmark Results

## Overview
This document presents comprehensive benchmark results for the Advanced Tokenizer, evaluating its performance across various scenarios including long texts, short texts, mixed lengths, repeated patterns, multilingual content, and special characters.

## Test Environment
- **CPU**: i5 11th Gen
- **RAM**: 8GB
- **OS**: Windows
- **Python**: 3.12.3
- **Dependencies**: See `requirements.txt`

## Test Scenarios

### 1. Training Performance
| Metric | Value |
|--------|-------|
| Training Time | 330.38 seconds (5.5 minutes) |
| Vocabulary Size | 77 tokens |
| Text Coverage | 100.00% |
| Unknown Token Rate | 0.00% |

### 2. Performance by Scenario

#### Long Texts
- **Description**: Processing of lengthy documents
- **Optimal Batch Size**: 300
- **Memory Growth**: ~9.1MB
- **Tokenization Speed**:
  - Single Thread: 1,173.22 tokens/sec
  - Parallel: 483.16 tokens/sec
  - Streaming: 593.85 tokens/sec
- **Subword Quality**:
  - Avg Subwords/Word: 3,916.29
  - Repetition Rate: 0.09

#### Short Texts
- **Description**: Processing of short text snippets
- **Optimal Batch Size**: 300
- **Memory Growth**: ~1.1MB
- **Tokenization Speed**:
  - Single Thread: 8,941.65 tokens/sec
  - Parallel: 2,047.72 tokens/sec
  - Streaming: 3,810.99 tokens/sec
- **Subword Quality**:
  - Avg Subwords/Word: 72.17
  - Repetition Rate: 0.08

#### Mixed Lengths
- **Description**: Mixture of short and long texts
- **Optimal Batch Size**: 400
- **Memory Growth**: ~9.4MB
- **Tokenization Speed**:
  - Single Thread: 3,528.38 tokens/sec
  - Parallel: 1,539.43 tokens/sec
  - Streaming: 1,851.51 tokens/sec
- **Subword Quality**:
  - Avg Subwords/Word: 737.48
  - Repetition Rate: 0.08

#### Repeated Patterns
- **Description**: Text with repeated patterns
- **Optimal Batch Size**: 300
- **Memory Growth**: ~0.56MB
- **Tokenization Speed**:
  - Single Thread: 9,842.39 tokens/sec
  - Parallel: 3,431.39 tokens/sec
  - Streaming: 3,698.73 tokens/sec
- **Subword Quality**:
  - Avg Subwords/Word: 5.22
  - Repetition Rate: 0.09

#### Multilingual
- **Description**: Multilingual text processing
- **Optimal Batch Size**: 200
- **Memory Growth**: ~0.55MB
- **Tokenization Speed**:
  - Single Thread: 10,469.14 tokens/sec
  - Parallel: 3,927.18 tokens/sec
  - Streaming: 4,148.02 tokens/sec
- **Subword Quality**:
  - Avg Subwords/Word: 5.49
  - Repetition Rate: 0.08

#### Special Characters
- **Description**: Text with special characters
- **Optimal Batch Size**: 400
- **Memory Growth**: ~0.16MB
- **Tokenization Speed**:
  - Single Thread: 4,813.74 tokens/sec
  - Parallel: 2,161.70 tokens/sec
  - Streaming: 2,360.64 tokens/sec
- **Subword Quality**:
  - Avg Subwords/Word: 15.38
  - Repetition Rate: 0.03

## Performance Analysis

### Speed Comparison
| Scenario | Single Thread (tokens/sec) | Parallel (tokens/sec) | Streaming (tokens/sec) |
|----------|---------------------------|----------------------|------------------------|
| Long Texts | 1,173.22 | 483.16 | 593.85 |
| Short Texts | 8,941.65 | 2,047.72 | 3,810.99 |
| Mixed Lengths | 3,528.38 | 1,539.43 | 1,851.51 |
| Repeated Patterns | 9,842.39 | 3,431.39 | 3,698.73 |
| Multilingual | 10,469.14 | 3,927.18 | 4,148.02 |
| Special Chars | 4,813.74 | 2,161.70 | 2,360.64 |

### Memory Efficiency
| Scenario | Memory Growth (MB) |
|----------|-------------------|
| Long Texts | 9.10 |
| Short Texts | 1.10 |
| Mixed Lengths | 9.40 |
| Repeated Patterns | 0.56 |
| Multilingual | 0.55 |
| Special Chars | 0.16 |

## Key Findings

1. **Optimal Performance**:
   - Best performance achieved in Multilingual scenario (10,469.14 tokens/sec)
   - Most memory-efficient with Special Characters (0.16MB growth)
   - Single-threaded processing consistently outperforms parallel processing

2. **Consistency**:
   - 100% vocabulary coverage across all scenarios
   0.00% unknown tokens in all tests
   - Low token variance (0.00-0.08) indicating high consistency

3. **Resource Usage**:
   - Memory usage remains efficient across all scenarios
   - Processing time scales predictably with input size

## Recommendations

1. **For Best Performance**:
   - Use single-threaded processing for most use cases
   - Batch sizes of 300-400 generally provide optimal performance
   - For multilingual content, use a batch size of 200

2. **Optimization Opportunities**:
   - Investigate parallel processing overhead
   - Optimize batch processing for long texts
   - Review subword tokenization for long texts (high subwords/word ratio)

3. **Production Deployment**:
   - Monitor memory usage with very large documents
   - Consider implementing a caching layer for repeated patterns
   - Regularly update the vocabulary based on your specific domain

## Test Methodology

1. **Training**:
   - Trained on a corpus of 1,000 lines of text
   - Vocabulary size limited to 77 tokens
   - Training completed in 330.38 seconds

2. **Testing**:
   - Each scenario tested with multiple batch sizes
   - Memory usage measured using Python's memory profiler
   - Speed tests conducted with timeit (10 repetitions)
   - Results averaged across multiple runs

3. **Metrics Collected**:
   - Tokenization speed (tokens/second)
   - Memory usage (MB)
   - Subword quality metrics
   - Processing consistency

## Conclusion
The Advanced Tokenizer demonstrates excellent performance across all tested scenarios, with particular strengths in handling multilingual content and special characters. The consistent 100% vocabulary coverage and efficient memory usage make it suitable for a wide range of NLP applications.
