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"""
TODO:
- 统计 tokenizer_impl
- 统计 OOV
- 统计 reversal
- 增加 math，code
"""
import gradio as gr
from compression_util import get_compression_leaderboard, common_corpuses
with gr.Blocks() 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("## 🛠️ Setting") # ⚙
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"
"- `corpus`: tokenization is performed on the selected subsets of [cc100](https://huggingface.co/datasets/cc100) corpus.\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")
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()
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