Spaces:
Running
Running
File size: 8,466 Bytes
7c73423 0d55475 7c73423 a4208a2 7c73423 47e1616 7c73423 47e1616 7c73423 47e1616 7c73423 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 |
"""
TODO:
- 统计 tokenizer_impl
- 统计 OOV
- 统计 reversal
- 增加 math，code
## balance
- 高压缩率 VS vocab_size:
- 高压缩率，就意味着，编码后的token数少，那么 token长度 就会长，--> vocab_size 就会太大
- 高压缩率 VS 无损
- s
- OOV
- OOV 多，那么生成的 UNK 可能多(一个char 一个UNK) --> token 数目多 -> 压缩率低
- OOV 多，那么生成的 UNK 可能少（） --> token 数目多 -> 压缩率低
"""
import gradio as gr
from compression_util import get_compression_leaderboard, common_corpuses
# From the perspective of compression
# exactly reconstructed from compressed tokens
docs = """## 📖 What is a good tokenizer?
From a compression perspective, a good tokenizer should be lossless, and keep high compression rate (less tokens).
The encoding and decoding process can be formulated as
```python
token_ids = tokenizer.encode(input_text) # compressed tokens
decoded_text = tokenizer.decode(token_ids) # reconstructed text
```
- **Lossless** <br>
Lossless tokenization preserves the exact original text, i.e. `decoded_text = input_text`.
- Most lossy tokenizers get many out-of-vocabulary tokens. 👉 Check the [oov of bert-base-uncased](https://huggingface.co/spaces/eson/tokenizer-arena/blob/main/stats/compression_rate/google-bert.bert-base-cased%20%40%20cc100.zh-Hans.diff.json).
- Some other tokenizers have no oov, but still be lossy due to text normalization. For example qwen performs [unicode normalization](https://github.com/huggingface/transformers/blob/v4.42.3/src/transformers/models/qwen2/tokenization_qwen2.py#L338),
which may bring some [slight difference](https://huggingface.co/spaces/eson/tokenizer-arena/raw/main/stats/compression_rate/Qwen.Qwen1.5-1.8B%20@%20cc100.ja.diff.json) to the reconstructed text.
- **Compression Rate** <br>
There are mainly two types of metric to represent the `input_text`:
- `char-level`: the number of characters in the given text
- `byte-level`: the number of bytes in the given text.
To evaluate compression rate, simple metrics can be "how many chars per token" or "how many bytes per token". <br>
In this leaderboard, we adopt more frequently used metric: "how many chars per token" and
"how many billion tokens per gigabytes corpus", i.e. `char/token` and `b_tokens/g_bytes`.
💬 [Discussions is Welcome](https://huggingface.co/spaces/eson/tokenizer-arena/discussions)
"""
# theme = gr.themes.Monochrome()
theme = gr.themes.Default()
# theme.set(accordion_text_weight=600) # 暂不支持
with gr.Blocks(theme=theme) as demo:
# gr.Markdown("## Convertor")
# with gr.Accordion("Convertor", open=False):
# gr.Markdown("Tokenize {} corpus")
# with gr.Row(elem_classes="no-border"):
# gr.Button("File Size", min_width=50)
# file_size = gr.Textbox(
# show_label=False,
# min_width=50,
# # elem_classes="textbox-as-text"
# )
# gr.Dropdown(
# choices=['MB', 'GB', 'TB'],
# show_label=False,
# min_width=15,
# # elem_classes="textbox-as-text"
# )
# # gr.Markdown('<h2 align="center">≈</h2>')
# # gr.HTML('<h2 style="margin: auto;">≈</h2>')
# gr.Button(
# "≈",
# min_width=10,
# elem_classes="button-white h2-font"
#
# )
#
# gr.Button(
# "Tokens",
# min_width=50
# )
# gr.Textbox(
# show_label=False,
# min_width=50
# )
# gr.Dropdown(
# ['million', 'billion', 'trillion'],
# show_label=False,
# min_width=15,
# elem_classes="button-white"
# )
gr.Markdown(docs)
gr.Markdown("## 🛠️ Setting") # ⚙
gr.Markdown("We perform tokenization on different corpus, and calculate the compression rate."
"")
with gr.Accordion("Please select the corpus and measure of compression rate.", open=True):
# file size 💽 🖴, tokens 🧮
# Total amount of disk used
with gr.Row():
with gr.Column():
compress_rate_corpus = gr.Dropdown(
common_corpuses, # , "code"
value=["cc100/en", "cc100/zh-Hans", "cc100/fr", "cc100/es"],
label="corpus",
multiselect=True
# info=""
)
# unit of file_size: gigabyte terabyte
# unit of token_num: million billion trillion
# The most common units of measurement include length (meter, inch, foot), weight (gram, kilogram, pound), volume (liter, gallon, milliliter), time (second, minute, hour)
compress_rate_unit = gr.Radio(
["b_tokens/g_bytes", "t_tokens/t_bytes"],
value="b_tokens/g_bytes",
label="measure", # evaluation metric
)
gr.Markdown(
# "Note:\n\n explanation"
# "Supported languages are (20): arabic (ar), bulgarian (bg), german (de), modern greek (el), english (en), spanish (es), french (fr), hindi (hi), italian (it), japanese (ja), dutch (nl), polish (pl), portuguese (pt), russian (ru), swahili (sw), thai (th), turkish (tr), urdu (ur), vietnamese (vi), and chinese (zh)."
# " arabic (ar), english (en), spanish (es), french (fr), italian (it), japanese (ja), portuguese (pt), russian (ru), and chinese (zh)."
"- `corpus`: tokenization is performed on the selected subsets of [cc100](https://huggingface.co/datasets/statmt/cc100) corpus.\n"
"- measure\n"
" - `b_tokens/g_bytes` measures how many billion tokens per gigabytes corpus.\n"
" - `t_tokens/t_bytes` measures how many trillion tokens per terabytes corpus.\n"
# "- `g_bytes/b_tokens` measures how many gigabytes corpus per billion tokens.\n"
# "- `t_bytes/t_tokens` measures how many terabytes corpus per trillion tokens.\n"
" - `char/token` measures how many chars per token on the tokenized corpus.\n"
" - `oov_ratio`: out-of-vocabulary ratio on the selected corpus, 👉 get [oov charset](https://huggingface.co/spaces/eson/tokenizer-arena/raw/main/stats/compression_rate.json)\n\n"
"You can reproduce this procedure with [compression_util.py](https://huggingface.co/spaces/eson/tokenizer-arena/blob/main/compression_util.py)."
)
gr.Markdown("## 🏆 Compression Rate Leaderboard\n"
"The leaderboard aim to evaluate tokenizer performance on different languages.\n"
"Lower `oov_ratio` refers to less out-of-vocabulary tokens.\n"
"Higher `char/token` means less words be segmented into subwords."
)
search_bar = gr.Textbox(
placeholder="🔍 Search by tokenizer or organization (e.g., 'llama', 'openai') and press ENTER...",
show_label=False,
elem_id="search-bar",
)
compress_rate_table = gr.Dataframe(datatype="html")
# func call
compress_rate_corpus.change(
get_compression_leaderboard,
inputs=[compress_rate_corpus, compress_rate_unit, search_bar],
outputs=compress_rate_table
)
compress_rate_unit.change(
get_compression_leaderboard,
inputs=[compress_rate_corpus, compress_rate_unit, search_bar],
outputs=compress_rate_table
)
# file_size.change(
# get_all_compress_rate,
# outputs=compress_rate_table
# )
search_bar.submit(
get_compression_leaderboard,
inputs=[
compress_rate_corpus,
compress_rate_unit,
search_bar,
],
outputs=compress_rate_table
)
demo.load(
get_compression_leaderboard,
inputs=[compress_rate_corpus, compress_rate_unit],
outputs=compress_rate_table
)
if __name__ == "__main__":
demo.launch()
|