--- title: Tokenizer Arena emoji: ⚡ colorFrom: red colorTo: gray sdk: gradio sdk_version: 3.41.2 app_file: app.py pinned: false --- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference ## ss ## TODO - 搜索栏 - ## 统计 ## vocabsize - 增大能提到压缩率，副作用是增大计算量和内存 （getting the most out of your tokenizer for pre-training and） - https://huggingface.co/spaces/yenniejun/tokenizers-languages ## gradio app - https://arena.lmsys.org/ ## lang ## number ## diff ## Compress Rate **简介** we tokenize in cc-100 | tokenizer | vocab_size | g_bytes/b_tokens | t_bytes/t_tokens | b_tokens/g_bytes | |:----------------------------|-------------:|-------------------:|-------------------:|-------------------:| | amber | 32000 | 1.84 | 1.8 | 0.54 | | aya_101 | 250100 | 3.89 | 3.79 | 0.26 | | baichuan | 64000 | 3.92 | 3.82 | 0.26 | | baichuan2 | 125696 | 4.53 | 4.42 | 0.22 | | bert_base_cased | 28996 | 2.73 | 2.66 | 0.37 | | bert_base_chinese | 21128 | 2.74 | 2.67 | 0.37 | | bert_base_uncased | 30522 | 2.73 | 2.67 | 0.37 | | bloom | 250680 | 4.28 | 4.18 | 0.23 | | byt5_small | 256 | 0.93 | 0.91 | 1.08 | | character_glm_6b | 64794 | 4.2 | 4.1 | 0.24 | | chatglm2_6b | 64794 | 4.2 | 4.1 | 0.24 | | chatglm3_6b | 64798 | 4.2 | 4.1 | 0.24 | | chatglm_6b | 150344 | 4.65 | 4.54 | 0.22 | | chatyuan_large_v2 | 32128 | 4.34 | 4.24 | 0.23 | | chinese_llama | 49953 | 3.93 | 3.84 | 0.25 | | chinese_llama2 | 55296 | 3.92 | 3.83 | 0.26 | | code_davinci_002 | 50281 | 1.31 | 1.28 | 0.77 | | crystal_coder | 32000 | 1.86 | 1.81 | 0.54 | | deepseek_coder_33b_instruct | 32000 | 3.4 | 3.32 | 0.29 | | deepseek_llm_7b_base | 100000 | 4.05 | 3.96 | 0.25 | | falcon_180b | 65024 | 2.18 | 2.13 | 0.46 | | falcon_7b | 65024 | 2.18 | 2.13 | 0.46 | | fastchat_t5_3b | 32000 | 13.7 | 13.38 | 0.07 | | flan_t5_base | 32100 | 14.13 | 13.8 | 0.07 | | gemma_7b | 256000 | 3.82 | 3.73 | 0.26 | | gpt2 | 50257 | 1.31 | 1.28 | 0.77 | | gpt2_chinese | 21128 | 2.73 | 2.66 | 0.37 | | gpt_35_turbo | 100277 | 2.26 | 2.21 | 0.44 | | gpt_4 | 100277 | 2.26 | 2.21 | 0.44 | | gpt_nexo_20b | 50254 | 2.01 | 1.96 | 0.5 | | internlm2_chat_7b | 92544 | 4.23 | 4.13 | 0.24 | | internlm2_math_7b | 92544 | 4.23 | 4.13 | 0.24 | | internlm_chat_7b | 103168 | 4.23 | 4.14 | 0.24 | | internlm_xcomposer_7b | 103168 | 4.23 | 4.14 | 0.24 | | kplug | 10261 | 2.72 | 2.65 | 0.37 | | llama | 32000 | 1.84 | 1.8 | 0.54 | | llama2 | 32000 | 1.84 | 1.8 | 0.54 | | mistral_7b | 32000 | 2.36 | 2.3 | 0.42 | | mixtral_8_7b | 32000 | 2.36 | 2.3 | 0.42 | | mobilebert_uncased | 30522 | 2.73 | 2.67 | 0.37 | | moss | 106029 | 4.4 | 4.3 | 0.23 | | mt5_large | 250100 | 3.89 | 3.79 | 0.26 | | olmo_7b | 50280 | 2.01 | 1.96 | 0.5 | | orion_14b_chat | 84608 | 4.63 | 4.52 | 0.22 | | phi_1 | 50257 | 1.31 | 1.28 | 0.77 | | phi_2 | 50257 | 1.31 | 1.28 | 0.77 | | pko_t5_large | 50258 | 0.97 | 0.95 | 1.03 | | prompt_clue | 32128 | 4.34 | 4.24 | 0.23 | | qwen1_5_14b_chat | 151643 | 4.16 | 4.06 | 0.24 | | qwen_1_8b_chat | 151851 | 4.16 | 4.06 | 0.24 | | qwen_72b_chat | 151851 | 4.16 | 4.06 | 0.24 | | qwen_7b_chat | 151851 | 4.16 | 4.06 | 0.24 | | roberta_chinese_clue | 8021 | 2.7 | 2.64 | 0.37 | | skywork_13b_base | 65519 | 3.69 | 3.61 | 0.27 | | skywork_13b_math | 65519 | 3.69 | 3.61 | 0.27 | | solar_10_7b | 32000 | 2.36 | 2.3 | 0.42 | | starchat_alpha | 49152 | 2.78 | 2.72 | 0.36 | | switch_c_2048 | 32100 | 14.13 | 13.8 | 0.07 | | t5_base | 32100 | 14.13 | 13.8 | 0.07 | | t5_large | 32100 | 14.13 | 13.8 | 0.07 | | t5_small | 32100 | 14.13 | 13.8 | 0.07 | | text_davinci_003 | 50281 | 1.31 | 1.28 | 0.77 | | tigerbot_13b_chat_v2 | 60512 | 4.25 | 4.15 | 0.24 | | tigerbot_70b_chat_v4_4k | 65107 | 4.25 | 4.15 | 0.24 | | wizardcoder_15b_v1 | 49152 | 2.78 | 2.72 | 0.36 | | wizardcoder_python_7b_v1 | 32000 | 1.84 | 1.8 | 0.54 | | wizardlm_7b_v1 | 32000 | 1.84 | 1.8 | 0.54 | | wizardmath_70b_v1 | 32000 | 1.84 | 1.8 | 0.54 | | xlm_roberta | 250002 | 3.96 | 3.86 | 0.25 | | yi_34b | 64000 | 4.17 | 4.07 | 0.24 | | yi_6b | 64000 | 4.17 | 4.07 | 0.24 | | yi_vl34b | 64000 | 4.11 | 4.02 | 0.24 | | zephyr_7b_beta | 32000 | 2.36 | 2.3 | 0.42 | **结论** larger vocabulary sizes ## Reference - Getting the most out of your tokenizer for pre-training and domain adaptation - Efficient and Effective Text Encoding for Chinese LLaMA and Alpaca - https://huggingface.co/spaces/Xenova/the-tokenizer-playground