---
license: apache-2.0
language:
- tr
tags:
- tokenizer
- bpe
- e-commerce
- turkish
pretty_name: TR E-Commerce Customer Support Tokenizer
---

# TR E-Commerce Customer Support Tokenizer 🇹🇷

A custom-trained **Byte-Pair Encoding (BPE)** tokenizer optimized specifically for Turkish e-commerce customer support dialogues. Trained on the `Mer1Alii/TR-ECommerce-CustomerSupport-Instructions` corpus, this tokenizer drastically improves token efficiency and semantic comprehension for Turkish conversational AI.

---

## 1. The Challenge of Turkish Tokenization

Turkish is an **agglutinative language** with a rich morphological structure. Words are constructed by attaching multiple suffixes to a root (e.g., *kar-go-lar-ı-mız-dan*). 

Standard English-centric tokenizers (like GPT-2 or LLaMA) do not have these Turkish roots/suffixes in their pre-trained vocabularies. As a result, they fragment basic Turkish words into tiny, meaningless character groups. This leads to:
* **High Token Counts**: Turkish texts take up to 2.5x to 3x more tokens than English counterparts.
* **Context Window Waste**: Models hit their context limits much faster.
* **Poorer Semantic Learning**: The model spends capacity learning character-level combinations instead of word meanings.

This custom tokenizer solves these issues by learning a vocabulary derived directly from real Turkish customer service dialogues.

---

## 2. Tokenization Performance Benchmark

Here is a comparison of how different tokenizers split the sample Turkish e-commerce query:
**`"kargom teslim edilmedi iade istiyorum"`** *(my package was not delivered, I want a return)*

| Tokenizer | Tokenized Representation | Token Count | Efficiency Gain |
| :--- | :--- | :---: | :---: |
| **GPT-2 (Standard)** | `['k', 'arg', 'om', ' t', 'es', 'lim', ' ed', 'il', 'medi', ' i', 'ade', ' is', 't', 'iy', 'orum']` | **15** | Baseline |
| **Our Custom Tokenizer** | `['kargom', ' teslim', ' edil', 'medi', ' iade', ' istiyorum']` | **6** | **2.5x Fewer Tokens (60% Savings)** |

---

## 3. Specifications

* **Vocabulary Size**: 8192 ($2^{13}$ tokens)
* **Algorithm**: Byte-Level BPE (`ByteLevelBPETokenizer`)
* **Base Training Corpus**: 558 lines of Turkish e-commerce customer support dialogues (`Mer1Alii/TR-ECommerce-CustomerSupport-Instructions`)
* **Special Tokens Map**:
  * `<s>`: Beginning of Sequence (BOS)
  - `<pad>`: Padding (PAD)
  - `</s>`: End of Sequence (EOS)
  - `<unk>`: Unknown token (UNK)
  - `<mask>`: Masking token (MASK)

---

## 5. Quick Start (Usage)

You can load and use this tokenizer directly in Python using the Hugging Face `transformers` library:

```python
from transformers import AutoTokenizer

# Load custom tokenizer
tokenizer = AutoTokenizer.from_pretrained("Mer1Alii/TR-ECommerce-CustomerSupport-Tokenizer")

# Test Sentence
text = "kargom teslim edilmedi iade istiyorum"
tokens = tokenizer.encode(text)

print("Token IDs:", tokens)
print("Decoded Tokens:", tokenizer.convert_ids_to_tokens(tokens))
```

## Developer
**[Mert Ali Alkan](https://github.com/MertAlii)**