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# coding=utf-8
# author: xusong
# time: 2022/8/23 16:06
"""
## TODO:
- http get方式获取参数,
- iter_vocab 的 warmup
- add_special_token 开关
- theme 开关 light/dark
- token_id/tokens/bytes 开关
- 通过 javascript 添加 hover_text
- i18
- 给方法 + 缓存,避免重复调用
- 英文 utf-8编码
- 词典支持下载
- 中文字词统计,是否要包括 _ G 等字符
- baichuan的单字数量怎么两万多个?
- OOV
- feedback位置
plots
table
## related demo
- [](http://text-processing.com/demo/tokenize/)
- [gpt-tokenizer](https://gpt-tokenizer.dev/)
- [llama-tokenizer-js](https://belladoreai.github.io/llama-tokenizer-js/example-demo/build/)
- [](https://huggingface.co/spaces/Xenova/the-tokenizer-playground)
## 可视化
[ The, 2, QUICK, Brown, Foxes, jumped, over, the, lazy, dog's, bone ]
"""
import gradio as gr
from vocab import all_tokenizers
from util import *
# llama chatglm_6b gpt_nexo_20b baichuan baichuan_7b
examples = [
["空格测试: 2个空格 8个空格", "llama", "chatglm_6b"], # chatglm 有blank_n,
["标点测试:,。!?;", "baichuan_7b", "llama"],
["符号测试:🦙❤❥웃유♋☮✊☏☢☚✔☑♚▢♪✈✞÷↑↓▤▥⊙■□▣▽¿─│♥❣▬▫☿Ⓐ ✋✉☣☤", "baichuan_7b", "llama"],
["数字测试:(10086 + 98) = 100184", "baichuan_7b", "llama"],
["中文简体:宽带,繁体:樂來", "baichuan_7b", "llama"],
]
# jieba.enable_parallel() # flask中没办法parallel
def example_fn(example_idx):
return examples[example_idx]
"""Replace this text in the input field to see how tokenization works
华为智能音箱发布:华为发布mate60 pro手机"""
default_user_input = """Replace this text in the input field to see how tokenization works
华为发布mate60 pro手机"""
default_tokenizer_type_1 = "llama"
default_tokenizer_type_2 = "internlm_chat_7b"
default_stats_vocab_size_1, default_stats_zh_token_size_1 = basic_count(default_tokenizer_type_1)
default_stats_vocab_size_2, default_stats_zh_token_size_2 = basic_count(default_tokenizer_type_2)
default_stats_overlap_token_size = get_overlap_token_size(default_tokenizer_type_1, default_tokenizer_type_2)[0]
default_output_text_1, default_output_table_1, default_output_len_1 = tokenize(default_user_input, default_tokenizer_type_1, update=False)
default_output_text_2, default_output_table_2, default_output_len_2 = tokenize(default_user_input, default_tokenizer_type_2, update=False)
with gr.Blocks(css="style.css") as demo:
gr.HTML("""<h1 align="center">Tokenizer Arena ⚔️</h1>""")
# links: https://www.coderstool.com/utf8-encoding-decoding
# 功能:输入文本,进行分词
# 分词器:常见的分词器有集中,
# 背景:方便分词、看词粒度、对比
#
# Byte: 表示分词
with gr.Row():
gr.Markdown("## Input Text")
dropdown_examples = gr.Dropdown(
["空格测试", "标点测试", "符号测试", "数字测试"],
value="Examples",
type="index",
show_label=False,
container=False,
scale=0,
elem_classes="example-style"
)
user_input = gr.Textbox(
value=default_user_input,
label="Input Text",
lines=5,
show_label=False,
) # placeholder="Enter sentence here..."
# gr.Examples(
# examples,
# None,
# )
gr.Markdown("## Tokenization")
with gr.Row():
with gr.Column(scale=6):
with gr.Group():
tokenizer_type_1 = gr.Dropdown(
all_tokenizers,
value=default_tokenizer_type_1,
label="Tokenizer 1",
)
with gr.Group():
"""
<div class="stat"><div class="stat-value">69</div><div class="stat-label">Characters</div></div>
"""
with gr.Row():
stats_vocab_size_1 = gr.TextArea(
value=default_stats_vocab_size_1,
label="VocabSize",
lines=1,
elem_classes="statistics"
)
stats_zh_token_size_1 = gr.TextArea(
value=default_stats_zh_token_size_1,
label="ZH char/word",
lines=1,
elem_classes="statistics"
)
stats_overlap_token_size_1 = gr.TextArea(
value=default_stats_overlap_token_size,
label="Overlap Tokens",
lines=1,
elem_classes="statistics"
)
# stats_3 = gr.TextArea(
# label="Compress Rate",
# lines=1,
# elem_classes="statistics"
# )
# https://www.onlinewebfonts.com/icon/418591
gr.Image("images/VS.svg", scale=1, show_label=False,
show_download_button=False, container=False,
show_share_button=False)
with gr.Column(scale=6):
with gr.Group():
tokenizer_type_2 = gr.Dropdown(
all_tokenizers,
value=default_tokenizer_type_2,
label="Tokenizer 2",
)
with gr.Group():
with gr.Row():
stats_vocab_size_2 = gr.TextArea(
value=default_stats_vocab_size_2,
label="VocabSize",
lines=1,
elem_classes="statistics"
)
stats_zh_token_size_2 = gr.TextArea(
value=default_stats_zh_token_size_2,
label="ZH char/word", # 中文字/词
lines=1,
elem_classes="statistics"
)
# stats_6 = gr.TextArea(
# label="Compress Rate",
# lines=1,
# elem_classes="statistics"
# )
stats_overlap_token_size_2 = gr.TextArea(
value=default_stats_overlap_token_size,
label="Overlap Tokens",
lines=1,
elem_classes="statistics"
)
# TODO: 图 表 压缩率
with gr.Row():
with gr.Column():
output_text_1 = gr.Highlightedtext(
value=default_output_text_1,
label=f"Tokens: {default_output_len_1}",
show_legend=True,
elem_classes="space-show"
)
with gr.Column():
output_text_2 = gr.Highlightedtext(
value=default_output_text_2,
label=f"Tokens: {default_output_len_2}",
show_legend=True,
elem_classes="space-show"
)
with gr.Row():
output_table_1 = gr.Dataframe(
value=default_output_table_1,
headers=["TokenID", "Byte", "Text"],
datatype=["str", "str", "str"],
# elem_classes="space-show", # 给整个Dataframe加这个css不起作用,因此直接修改cell-wrap
)
output_table_2 = gr.Dataframe(
value=default_output_table_2,
headers=["TokenID", "Token", "Text"],
datatype=["str", "str", "str"],
)
tokenizer_type_1.change(tokenize, [user_input, tokenizer_type_1],
[output_text_1, output_table_1])
# 下面两个好像可以合并
tokenizer_type_1.change(basic_count, [tokenizer_type_1], [stats_vocab_size_1, stats_zh_token_size_1])
tokenizer_type_1.change(get_overlap_token_size, [tokenizer_type_1, tokenizer_type_2],
[stats_overlap_token_size_1, stats_overlap_token_size_2])
user_input.change(tokenize_pair,
[user_input, tokenizer_type_1, tokenizer_type_2],
[output_text_1, output_table_1, output_text_2, output_table_2])
tokenizer_type_2.change(tokenize, [user_input, tokenizer_type_2],
[output_text_2, output_table_2])
tokenizer_type_2.change(basic_count, [tokenizer_type_2], [stats_vocab_size_2, stats_zh_token_size_2])
tokenizer_type_2.change(get_overlap_token_size, [tokenizer_type_1, tokenizer_type_2],
[stats_overlap_token_size_1, stats_overlap_token_size_2])
dropdown_examples.change(
example_fn,
dropdown_examples,
[user_input, tokenizer_type_1, tokenizer_type_2]
)
# start up 初始化
# user_input.update(user_input.value + "___")
if __name__ == "__main__":
demo.queue(max_size=20).launch()
# demo.launch()
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