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metadata
title: Tokenizer Arena
emoji: ⚡
colorFrom: red
colorTo: gray
sdk: gradio
sdk_version: 4.31.4
app_file: app.py
pinned: false
datasets:
- cc100
压缩率 Compress Rate
在 cc-100 数据集,每个语言取1万条数据,测试不同tokenizer的压缩率。
压缩率示例: llama3扩充了词典,具有更高的压缩比。同样1T字节的简体中文语料,llama分词后是 0.56万亿个token,llama3只需要0.31万亿个token。
tokenizer | vocab_size | t_bytes/t_tokens | t_tokens/t_bytes | n_chars/n_tokens |
---|---|---|---|---|
llama | 32000 | 1.8 | 0.56 | 0.7 |
llama3 | 128000 | 3.2 | 0.31 | 1.24 |
可通过以下脚本进行复现
python utils/compress_rate_util.py
英文压缩率
在英文数据集 cc100-en 计算压缩率tokenizer | vocab_size | g_bytes/b_tokens | b_tokens/g_bytes | t_bytes/t_tokens | t_tokens/t_bytes | n_chars/n_tokens |
---|---|---|---|---|---|---|
amber | 32000 | 3.56 | 0.28 | 3.47 | 0.29 | 3.81 |
aya_101 | 250100 | 3.3 | 0.3 | 3.22 | 0.31 | 3.53 |
baichuan | 64000 | 3.74 | 0.27 | 3.65 | 0.27 | 4 |
baichuan2 | 125696 | 3.89 | 0.26 | 3.8 | 0.26 | 4.17 |
简体中文压缩率
在简体中文数据集 cc100-zh-Hans 计算压缩率tokenizer | vocab_size | g_bytes/b_tokens | b_tokens/g_bytes | t_bytes/t_tokens | t_tokens/t_bytes | n_chars/n_tokens |
---|---|---|---|---|---|---|
amber | 32000 | 1.84 | 0.54 | 1.8 | 0.56 | 0.7 |
aya_101 | 250100 | 3.89 | 0.26 | 3.79 | 0.26 | 1.47 |
baichuan | 64000 | 3.92 | 0.26 | 3.82 | 0.26 | 1.48 |
Reference
- Getting the most out of your tokenizer for pre-training and domain adaptation
- Efficient and Effective Text Encoding for Chinese LLaMA and Alpaca
blog - https://help.openai.com/en/articles/4936856-what-are-tokens-and-how-to-count-them - https://huggingface.co/docs/transformers/tokenizer_summary#sentencepiece - https://www.huaxiaozhuan.com/%E5%B7%A5%E5%85%B7/huggingface_transformer/chapters/1_tokenizer.html - https://zhuanlan.zhihu.com/p/652520262 - https://github.com/QwenLM/Qwen/blob/main/tokenization_note_zh.md - https://tonybaloney.github.io/posts/cjk-chinese-japanese-korean-llm-ai-best-practices.html
- demo
- paper
- ss