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add more tokenizers

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  1. .gitattributes +1 -0
  2. README.md +9 -0
  3. app.py +1 -7
  4. app_v1.py +0 -196
  5. config.py +3 -0
  6. examples.py +37 -0
  7. requirements.txt +1 -1
  8. util.py +1 -1
  9. utils/_vocab.zh.jsonl +0 -1189
  10. utils/fn_util.py +0 -0
  11. utils/text_util.py +19 -1
  12. utils/vocab.jd.txt.v2 +0 -10268
  13. utils/zh_util.py +74 -24
  14. vocab/README.md +3 -0
  15. vocab/__init__.py +23 -4
  16. vocab/baichuan/Baichuan-7B/tokenization_baichuan.py +5 -5
  17. vocab/baichuan/__init__.py +9 -1
  18. vocab/baichuan/demo.py +4 -1
  19. vocab/baichuan/error.md +8 -0
  20. vocab/baichuan2/Baichuan2-7B-Chat/special_tokens_map.json +30 -0
  21. vocab/baichuan2/Baichuan2-7B-Chat/tokenization_baichuan.py +251 -0
  22. vocab/baichuan2/Baichuan2-7B-Chat/tokenizer.model +3 -0
  23. vocab/baichuan2/Baichuan2-7B-Chat/tokenizer_config.json +36 -0
  24. vocab/baichuan2/__init__.py +13 -2
  25. vocab/baichuan2/bug.py +22 -0
  26. vocab/baichuan2/encode.py +15 -0
  27. vocab/baichuan2/special_token.py +15 -0
  28. vocab/baichuan7b_chat/__init__.py +5 -0
  29. vocab/build_zh_vocab.py +0 -32
  30. vocab/chatglm2_6b/__init__.py +6 -0
  31. vocab/chatglm3_6b/__init__.py +9 -0
  32. vocab/chatglm_6b/README.md +1 -1
  33. vocab/chatglm_6b/__init__.py +9 -5
  34. vocab/chatglm_6b/{tokenizer → chatglm_6b}/config.json +0 -0
  35. vocab/chatglm_6b/{tokenizer → chatglm_6b}/ice_text.model +0 -0
  36. vocab/chatglm_6b/{tokenizer → chatglm_6b}/tokenization_chatglm.py +0 -0
  37. vocab/chatglm_6b/{tokenizer → chatglm_6b}/tokenizer_config.json +0 -0
  38. vocab/chinese_llama2/demo.py +6 -0
  39. vocab/chinese_llama2/load_by_gpt_neox.py +19 -0
  40. vocab/chinese_llama2/tokenizer/special_tokens_map.json +24 -0
  41. vocab/chinese_llama2/tokenizer/tokenizer.model +3 -0
  42. vocab/chinese_llama2/tokenizer/tokenizer_config.json +35 -0
  43. vocab/gpt_35_turbo/README.md +41 -0
  44. vocab/gpt_35_turbo/__init__.py +21 -8
  45. vocab/gpt_35_turbo/decode_test.py +6 -0
  46. vocab/gpt_35_turbo/test_arena.py +10 -0
  47. vocab/gpt_35_turbo/{test2.py → test_tiktoken.py} +1 -0
  48. vocab/gpt_35_turbo/{aaa.py → tiktoken_decode_test.py} +1 -0
  49. vocab/gpt_35_turbo/vocab.jsonl +0 -311
  50. vocab/internlm_chat_7b/__init__.py +1 -0
.gitattributes CHANGED
@@ -35,3 +35,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
35
  *tfevents* filter=lfs diff=lfs merge=lfs -text
36
  vocab/belle_7b_2m/belle-7b-2m/tokenizer.json filter=lfs diff=lfs merge=lfs -text
37
  vocab/bloom/tokenizer/tokenizer.json filter=lfs diff=lfs merge=lfs -text
 
 
35
  *tfevents* filter=lfs diff=lfs merge=lfs -text
36
  vocab/belle_7b_2m/belle-7b-2m/tokenizer.json filter=lfs diff=lfs merge=lfs -text
37
  vocab/bloom/tokenizer/tokenizer.json filter=lfs diff=lfs merge=lfs -text
38
+ vocab/
README.md CHANGED
@@ -17,3 +17,12 @@ Check out the configuration reference at https://huggingface.co/docs/hub/spaces-
17
 
18
  ## ss
19
 
 
 
 
 
 
 
 
 
 
 
17
 
18
  ## ss
19
 
20
+
21
+
22
+
23
+ ## 统计
24
+
25
+
26
+
27
+
28
+ https://huggingface.co/spaces/yenniejun/tokenizers-languages
app.py CHANGED
@@ -18,6 +18,7 @@
18
  - 词典支持下载,借用image下载的标签,
19
  - baichuan的单字数量怎么两万多个?
20
  - qwen: ValueError: Unclosed image token
 
21
 
22
  plots
23
 
@@ -188,12 +189,5 @@ with gr.Blocks(css="css/style.css", title="Tokenizer Arena") as demo:
188
 
189
 
190
  if __name__ == "__main__":
191
- print("http://127.0.0.1:7860/?tokenizer1=llama&tokenizer2=chinese_llama2&text=fdsjlk") # llama chinese_llama2
192
- print(
193
- "http://127.0.0.1:7860/?tokenizer1=chinese_llama&tokenizer2=chinese_llama2&text=fdsjlk") # llama chinese_llama2
194
- print("http://127.0.0.1:7860/?tokenizer1=baichuan&tokenizer2=baichuan2&text=sss") # baichuan 1 VS 2
195
- print("http://127.0.0.1:7860/?tokenizer1=bert&tokenizer2=clue&text=sss") # bert VS clue
196
- print("http://127.0.0.1:7860/?tokenizer1=clue&tokenizer2=kplug&text=sss") # clue VS kplug
197
- print("http://127.0.0.1:7860/?tokenizer1=baichuan&tokenizer2=baichuan2&text=sss") #
198
  # demo.queue(max_size=20).launch()
199
  demo.launch()
 
18
  - 词典支持下载,借用image下载的标签,
19
  - baichuan的单字数量怎么两万多个?
20
  - qwen: ValueError: Unclosed image token
21
+ - 路径修改为全path meta-llama/Llama-2-13b-hf
22
 
23
  plots
24
 
 
189
 
190
 
191
  if __name__ == "__main__":
 
 
 
 
 
 
 
192
  # demo.queue(max_size=20).launch()
193
  demo.launch()
app_v1.py DELETED
@@ -1,196 +0,0 @@
1
- # coding=utf-8
2
- # author: xusong
3
- # time: 2022/8/23 16:06
4
-
5
- """
6
-
7
- plots
8
-
9
- table
10
-
11
- ## related demo
12
- - [](http://text-processing.com/demo/tokenize/)
13
- - [gpt-tokenizer](https://gpt-tokenizer.dev/)
14
- - [llama-tokenizer-js](https://belladoreai.github.io/llama-tokenizer-js/example-demo/build/)
15
- - [](https://huggingface.co/spaces/Xenova/the-tokenizer-playground)
16
-
17
- ## 可视化
18
-
19
- [ The, 2, QUICK, Brown, Foxes, jumped, over, the, lazy, dog's, bone ]
20
- """
21
-
22
- import json
23
- import pandas as pd
24
- import gradio as gr
25
-
26
- from vocab import all_tokenizers, load_tokener
27
-
28
- # 显示空格:https://blog.csdn.net/liuxiao723846/article/details/118994673
29
- # 隐藏legend:
30
- css = """
31
- .space-show {white-space: pre-wrap;}
32
- .cell-wrap {white-space: pre-wrap;}
33
- .category-legend {display: none !important}
34
- """
35
-
36
- example_text = """Replace this text in the input field to see how tokenization works
37
- 中文测试:华为智能音箱发布:华为Sound X。維基百科由非營利組織──維基媒體基金會負責維持
38
- 数字测试:(10086 + 98) = 100184"""
39
-
40
- # llama chatglm_6b gpt_nexo_20b baichuan baichuan_7b
41
- examples = [
42
- # ["空格测试: 2个空格 8个空格", "llama", "chatglm_6b"], # chatglm 有blank_n,
43
- ["标点测试:,。!?;", "baichuan_7b", "llama"],
44
- ["标点测试:🦙", "baichuan_7b", "llama"],
45
- ]
46
-
47
-
48
- def tokenize(text, tokenizer_type, color_num=5):
49
- print(text, tokenizer_type)
50
- pos_tokens = []
51
- tokenizer = load_tokener(tokenizer_type)
52
- encoding = tokenizer.encode(text)
53
-
54
- table = []
55
-
56
- for idx, token_id in enumerate(encoding):
57
- decode_text = tokenizer.decode([token_id]) # 特殊字符解码后会统一变成 �,对应 "\ufffd"
58
- pos_tokens.extend([(decode_text, str(idx % color_num))])
59
-
60
- # token "Byte": # 这是 utf-8编码吧?
61
- token = tokenizer.convert_ids_to_tokens([token_id])[0]
62
- if isinstance(token, bytes):
63
- try:
64
- token_str = token.decode("utf-8")
65
- except:
66
- token_str = token.decode("utf-8", errors="ignore")
67
- print("decode_error", token, token_str)
68
-
69
- token_bytes = token
70
- json_dumps = json.dumps(token_str)
71
- elif isinstance(token, str):
72
- token_str = token
73
- token_bytes = bytes(token_str, "utf-8")
74
- json_dumps = json.dumps(token_str)
75
- else:
76
- return
77
-
78
- table.append(
79
- {"TokenID": token_id,
80
- "Token": token_str, # utf-8解码后的字符串,为什么有些是 <0xE7>,表示什么?比如llama
81
- "Text": decode_text, #
82
- # "Bytes": token_bytes, # bytes类型在gradio前端页面被解码成字符串,比如 b'\xe4\xb8\xad' 仍然显示成 "中"。因此 str(token_bytes)
83
- "Bytes": str(token_bytes),
84
- # "Unicode": json_dumps # unicode, 如果是ascii码,就直接显示。如果不是ascii码,就显示unicode
85
- }
86
- )
87
-
88
- table_df = pd.DataFrame(table)
89
- print(table)
90
- # print(table_df)
91
-
92
- return pos_tokens, table_df
93
-
94
-
95
- def tokenize_pair(text, tokenizer_type_1, tokenizer_type_2):
96
- pos_tokens_1, table_df_1 = tokenize(text, tokenizer_type_1)
97
- pos_tokens_2, table_df_2 = tokenize(text, tokenizer_type_2)
98
- return pos_tokens_1, table_df_1, pos_tokens_2, table_df_2
99
-
100
-
101
- def test_coding():
102
- bytes1 = b'\xe4\xb8\xad'
103
- print(bytes1) # b'\xe4\xb8\xad'
104
-
105
-
106
- with gr.Blocks(css=css) as demo:
107
- gr.HTML("""<h1 align="center">The Tokenizer Arena</h1>""")
108
- # links: https://www.coderstool.com/utf8-encoding-decoding
109
- #
110
-
111
-
112
-
113
- gr.Markdown("## Input Text")
114
- user_input = gr.Textbox(
115
- value=example_text,
116
- label="Input Text",
117
- lines=5
118
- ) # placeholder="Enter sentence here..."
119
-
120
- # submitBtn = gr.Button("生成回复", variant="primary")
121
-
122
- gr.Markdown("## Tokenization")
123
-
124
- # with gr.Row():
125
-
126
-
127
-
128
- # TODO: 图 表 压缩率
129
- with gr.Row():
130
- with gr.Column():
131
- tokenizer_type_1 = gr.Dropdown(
132
- all_tokenizers,
133
- value="llama",
134
- label="Tokenizer 1",
135
- )
136
- token_counter_1 = None # 计数器
137
- output_text_1 = gr.Highlightedtext(
138
- label="Tokens 1",
139
- show_legend=True,
140
- elem_classes="space-show"
141
- )
142
-
143
- with gr.Column():
144
- tokenizer_type_2 = gr.Dropdown(
145
- all_tokenizers,
146
- value="baichuan_7b",
147
- label="Tokenizer 2"
148
- )
149
- token_counter_2 = None # 计数器
150
- output_text_2 = gr.Highlightedtext(
151
- label="Tokens 2",
152
- show_legend=True,
153
- elem_classes="space-show"
154
- )
155
-
156
- with gr.Row():
157
- output_table_1 = gr.Dataframe(
158
- headers=["TokenID", "Byte", "Text"],
159
- datatype=["str", "str", "str"],
160
- # elem_classes="space-show", # 给���个Dataframe加这个css不起作用,因此直接修改cell-wrap
161
- )
162
- output_table_2 = gr.Dataframe(
163
- headers=["TokenID", "Token", "Text"],
164
- datatype=["str", "str", "str"],
165
- )
166
-
167
- user_input.change(tokenize,
168
- [user_input, tokenizer_type_1],
169
- [output_text_1, output_table_1])
170
- tokenizer_type_1.change(tokenize, [user_input, tokenizer_type_1], [output_text_1, output_table_1])
171
-
172
- user_input.change(tokenize,
173
- [user_input, tokenizer_type_2],
174
- [output_text_2, output_table_2])
175
-
176
- tokenizer_type_2.change(tokenize, [user_input, tokenizer_type_2], [output_text_2, output_table_2])
177
-
178
- gr.Examples(
179
- examples,
180
- [user_input, tokenizer_type_1, tokenizer_type_2],
181
- [output_text_1, output_table_1, output_text_2, output_table_2],
182
- tokenize_pair,
183
- cache_examples=True,
184
- )
185
-
186
- # submitBtn.click(tokenize, [user_input, tokenizer_type], outputs,
187
- # show_progress=True)
188
-
189
- # examples=[
190
- # ["What a beautiful morning for a walk!"],
191
- # ["It was the best of times, it was the worst of times."],
192
- # ["多个空格 It ss was the best of times, it was the worst of times."],
193
- # ]
194
-
195
- if __name__ == "__main__":
196
- demo.launch()
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
config.py ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+
2
+
3
+ USE_REMOTE = False
examples.py CHANGED
@@ -17,6 +17,43 @@ examples = {
17
 
18
  }
19
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
20
 
21
  def example_fn(example_idx):
22
  return examples["en"][example_idx]
 
 
 
 
 
 
 
 
 
 
 
 
 
17
 
18
  }
19
 
20
+ more_examples = [
21
+ # bert VS clue
22
+ # bert系列
23
+ ("bert_base_cased", "bert_base_uncased", ""), # # clue VS kplug, bert VS clue
24
+
25
+ # llama系列 (基于sentencepiece)
26
+ ("baichuan", "baichuan2", "baichuan2支持多空格 ,多个换行\n\n\n,do not add dummy prefix as Baichuan1"),
27
+ ("llama", "baichuan2", "baichuan2支持多空格 ,多个换行\n\n"),
28
+ ("llama", "chinese_llama2", ""),
29
+ ("chinese_llama", "chinese_llama2", ""),
30
+
31
+ # glm系列 (基于sentencepiece)
32
+ ("glm", "chatglm1", ""),
33
+ ("chatglm1", "chatglm2", ""),
34
+
35
+ # gpt2系列
36
+ ("gpt2", "moss", ""),
37
+ ("", "", ""),
38
+
39
+ # openai系列 (tiktoken)
40
+ ("qwen", "gpt_35_turbo", ""),
41
+
42
+
43
+ ]
44
+
45
 
46
  def example_fn(example_idx):
47
  return examples["en"][example_idx]
48
+
49
+
50
+ def get_more_example():
51
+ import urllib.parse
52
+ url_prefix = "https://huggingface.co/spaces/eson/tokenizer-arena"
53
+ for tokenizer1, tokenizer2, text in more_examples:
54
+ full_url = f'{url_prefix}?tokenizer1={tokenizer1}&tokenizer2={tokenizer2}&text={urllib.parse.quote(text)}'
55
+ print(full_url)
56
+
57
+
58
+ if __name__ == "__main__":
59
+ get_more_example()
requirements.txt CHANGED
@@ -1,4 +1,4 @@
1
- transformers>=4.21.1
2
  sentencepiece
3
  tiktoken
4
  icetk
 
1
+ transformers==4.33.1
2
  sentencepiece
3
  tiktoken
4
  icetk
util.py CHANGED
@@ -25,7 +25,7 @@ def tokenize(text, tokenizer_type, color_num=5):
25
  pos_tokens.extend([(decode_text, str(idx % color_num))])
26
 
27
  # token "Byte": # 这是 utf-8编码吧?
28
- token = tokenizer.convert_ids_to_tokens([token_id])[0]
29
  if isinstance(token, bytes):
30
  try:
31
  token_str = token.decode("utf-8")
 
25
  pos_tokens.extend([(decode_text, str(idx % color_num))])
26
 
27
  # token "Byte": # 这是 utf-8编码吧?
28
+ token = tokenizer.convert_ids_to_tokens([token_id], skip_special_tokens=False)[0]
29
  if isinstance(token, bytes):
30
  try:
31
  token_str = token.decode("utf-8")
utils/_vocab.zh.jsonl DELETED
@@ -1,1189 +0,0 @@
1
- {"id": 529, "token": "’", "type": "中文标点"}
2
- {"id": 753, "token": "’s", "type": "中文标点"}
3
- {"id": 863, "token": "”", "type": "中文标点"}
4
- {"id": 1054, "token": " “", "type": "中文标点"}
5
- {"id": 1389, "token": " –", "type": "中文标点"}
6
- {"id": 1431, "token": "’t", "type": "中文标点"}
7
- {"id": 1811, "token": "。", "type": "中文标点"}
8
- {"id": 1981, "token": "…", "type": "中文标点"}
9
- {"id": 2001, "token": " —", "type": "中文标点"}
10
- {"id": 2029, "token": ".”", "type": "中文标点"}
11
- {"id": 2118, "token": "“", "type": "中文标点"}
12
- {"id": 2345, "token": "—", "type": "中文标点"}
13
- {"id": 2476, "token": ",”", "type": "中文标点"}
14
- {"id": 2950, "token": ".”\n\n", "type": "中文标点"}
15
- {"id": 3207, "token": "’re", "type": "中文标点"}
16
- {"id": 3451, "token": " ‘", "type": "中文标点"}
17
- {"id": 3490, "token": "。\n\n", "type": "中文标点"}
18
- {"id": 3922, "token": ",", "type": "中文标点"}
19
- {"id": 4070, "token": "’ve", "type": "中文标点"}
20
- {"id": 4235, "token": "–", "type": "中文标点"}
21
- {"id": 4344, "token": "’m", "type": "中文标点"}
22
- {"id": 4696, "token": " …", "type": "中文标点"}
23
- {"id": 4805, "token": "’ll", "type": "中文标点"}
24
- {"id": 5232, "token": ":", "type": "中文标点"}
25
- {"id": 5486, "token": "、", "type": "中文标点"}
26
- {"id": 5551, "token": "…\n\n", "type": "中文标点"}
27
- {"id": 6447, "token": "!", "type": "中文标点"}
28
- {"id": 7070, "token": "’d", "type": "中文标点"}
29
- {"id": 7663, "token": "”\n\n", "type": "中文标点"}
30
- {"id": 7705, "token": ")", "type": "中文标点"}
31
- {"id": 8107, "token": "年", "type": "中文单字"}
32
- {"id": 8713, "token": "——", "type": "中文标点"}
33
- {"id": 9039, "token": "数", "type": "中文单字"}
34
- {"id": 9080, "token": "日", "type": "中文单字"}
35
- {"id": 9174, "token": "。\n", "type": "中文标点"}
36
- {"id": 9520, "token": "”,", "type": "中文标点"}
37
- {"id": 9554, "token": "的", "type": "中文单字"}
38
- {"id": 9787, "token": " ·", "type": "中文标点"}
39
- {"id": 9953, "token": "月", "type": "中文单字"}
40
- {"id": 10110, "token": "(", "type": "中文标点"}
41
- {"id": 10378, "token": "“I", "type": "中文标点"}
42
- {"id": 10416, "token": " […", "type": "中文标点"}
43
- {"id": 10646, "token": "」", "type": "中文标点"}
44
- {"id": 11144, "token": "【", "type": "中文标点"}
45
- {"id": 11199, "token": "】", "type": "中文标点"}
46
- {"id": 11453, "token": "”.", "type": "中文标点"}
47
- {"id": 11571, "token": "?", "type": "中文标点"}
48
- {"id": 11883, "token": "用", "type": "中文单字"}
49
- {"id": 12291, "token": " …\n\n", "type": "中文标点"}
50
- {"id": 12671, "token": "?”", "type": "中文标点"}
51
- {"id": 12996, "token": " […]\n\n", "type": "中文标点"}
52
- {"id": 13153, "token": "成", "type": "中文单字"}
53
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1189
- {"id": 100207, "token": "在线", "type": "中文多字"}
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
utils/fn_util.py ADDED
File without changes
utils/text_util.py CHANGED
@@ -1,6 +1,8 @@
1
 
2
 
3
 
 
 
4
  def is_chinese(uchar):
5
  """
6
  https://github.com/fxsjy/jieba/blob/master/jieba/__init__.py#L48
@@ -12,4 +14,20 @@ def is_chinese(uchar):
12
 
13
  def has_chinese(text):
14
  """ contains Chinese characters """
15
- return any(is_chinese(ch) for ch in text)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
 
2
 
3
 
4
+
5
+
6
  def is_chinese(uchar):
7
  """
8
  https://github.com/fxsjy/jieba/blob/master/jieba/__init__.py#L48
 
14
 
15
  def has_chinese(text):
16
  """ contains Chinese characters """
17
+ return any(is_chinese(ch) for ch in text)
18
+
19
+
20
+ def get_zh_count(text):
21
+ return sum([is_chinese(uchar) for uchar in text])
22
+
23
+
24
+ def is_all_chinese(text):
25
+ return all(is_chinese(char) for char in text)
26
+
27
+
28
+ def get_digit_count(text):
29
+ digit_count = 0
30
+ for char in text:
31
+ if char in "0123456789":
32
+ digit_count += 1
33
+ return digit_count
utils/vocab.jd.txt.v2 DELETED
@@ -1,10268 +0,0 @@
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- [PAD]
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-
531
-
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-
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-
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-
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-
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-
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541
-
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-
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-
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-
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-
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-
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-
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-
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-
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952
-
953
-
954
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955
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956
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957
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958
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959
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960
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961
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962
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963
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964
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965
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966
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967
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968
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969
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970
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971
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972
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973
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974
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975
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976
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977
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978
-
979
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980
-
981
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982
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983
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984
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985
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986
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987
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988
-
989
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990
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991
-
992
-
993
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994
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995
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996
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997
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998
-
999
-
1000
-
1001
-
1002
-
1003
-
1004
-
1005
-
1006
-
1007
-
1008
-
1009
-
1010
-
1011
-
1012
-
1013
-
1014
-
1015
-
1016
-
1017
-
1018
-
1019
-
1020
-
1021
-
1022
-
1023
-
1024
-
1025
-
1026
-
1027
-
1028
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1029
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1030
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1031
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1032
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1033
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1034
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1035
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1036
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1037
-
1038
-
1039
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1040
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1041
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1042
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1043
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1044
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1045
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1046
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1047
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1048
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1049
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1050
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1051
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1052
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1053
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1054
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1055
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1056
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1057
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1058
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1059
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1060
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1061
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1062
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1063
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1064
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1065
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1066
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1067
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1068
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1069
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1070
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1071
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1072
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1073
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1074
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1075
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1076
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1077
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1078
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1079
-
1080
-
1081
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1082
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1083
-
1084
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1085
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1086
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1087
-
1088
-
1089
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1090
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1091
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1092
-
1093
-
1094
-
1095
-
1096
-
1097
-
1098
-
1099
-
1100
-
1101
-
1102
-
1103
-
1104
-
1105
-
1106
-
1107
-
1108
-
1109
-
1110
-
1111
-
1112
-
1113
-
1114
-
1115
-
1116
-
1117
-
1118
-
1119
-
1120
-
1121
-
1122
-
1123
-
1124
-
1125
-
1126
-
1127
-
1128
-
1129
-
1130
-
1131
-
1132
-
1133
-
1134
-
1135
-
1136
-
1137
-
1138
-
1139
-
1140
-
1141
-
1142
-
1143
-
1144
-
1145
-
1146
-
1147
-
1148
-
1149
-
1150
-
1151
-
1152
-
1153
-
1154
-
1155
-
1156
-
1157
-
1158
-
1159
-
1160
-
1161
-
1162
-
1163
-
1164
-
1165
-
1166
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1167
-
1168
-
1169
-
1170
-
1171
-
1172
-
1173
-
1174
-
1175
-
1176
-
1177
-
1178
-
1179
-
1180
-
1181
-
1182
-
1183
-
1184
-
1185
-
1186
-
1187
-
1188
-
1189
-
1190
-
1191
-
1192
-
1193
-
1194
-
1195
-
1196
-
1197
-
1198
-
1199
-
1200
-
1201
-
1202
-
1203
-
1204
-
1205
-
1206
-
1207
-
1208
-
1209
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1210
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1211
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1212
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1213
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1214
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1215
-
1216
-
1217
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1218
-
1219
-
1220
-
1221
-
1222
-
1223
-
1224
-
1225
-
1226
-
1227
-
1228
-
1229
-
1230
-
1231
-
1232
-
1233
-
1234
-
1235
-
1236
-
1237
-
1238
-
1239
-
1240
-
1241
-
1242
-
1243
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1244
-
1245
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1246
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1247
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1248
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1249
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1250
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1251
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1252
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1253
-
1254
-
1255
-
1256
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1257
-
1258
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1259
-
1260
-
1261
-
1262
-
1263
-
1264
-
1265
-
1266
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1267
-
1268
-
1269
-
1270
-
1271
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1272
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1273
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1274
-
1275
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1276
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1277
-
1278
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1279
-
1280
-
1281
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1282
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1283
-
1284
-
1285
-
1286
-
1287
-
1288
-
1289
-
1290
-
1291
-
1292
-
1293
-
1294
-
1295
-
1296
-
1297
-
1298
-
1299
-
1300
-
1301
-
1302
-
1303
-
1304
-
1305
-
1306
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1307
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1308
-
1309
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1310
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1311
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1312
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1313
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1314
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1315
-
1316
-
1317
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1318
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1319
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1320
-
1321
-
1322
-
1323
-
1324
-
1325
-
1326
-
1327
-
1328
-
1329
-
1330
-
1331
-
1332
-
1333
-
1334
-
1335
-
1336
-
1337
-
1338
-
1339
-
1340
-
1341
-
1342
-
1343
-
1344
-
1345
-
1346
-
1347
-
1348
-
1349
-
1350
-
1351
-
1352
-
1353
-
1354
-
1355
-
1356
-
1357
-
1358
-
1359
-
1360
-
1361
-
1362
-
1363
-
1364
-
1365
-
1366
-
1367
-
1368
-
1369
-
1370
-
1371
-
1372
-
1373
-
1374
-
1375
-
1376
-
1377
-
1378
-
1379
-
1380
-
1381
-
1382
-
1383
-
1384
-
1385
-
1386
-
1387
-
1388
-
1389
-
1390
-
1391
-
1392
-
1393
-
1394
-
1395
-
1396
-
1397
-
1398
-
1399
-
1400
-
1401
-
1402
-
1403
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1404
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1405
-
1406
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1407
-
1408
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1409
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1410
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1411
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1412
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1413
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1414
-
1415
-
1416
-
1417
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1418
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1419
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1420
-
1421
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1422
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1423
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1424
-
1425
-
1426
-
1427
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1428
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1429
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1430
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1431
-
1432
-
1433
-
1434
-
1435
-
1436
-
1437
-
1438
-
1439
-
1440
-
1441
-
1442
-
1443
-
1444
-
1445
-
1446
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1447
-
1448
-
1449
-
1450
-
1451
-
1452
-
1453
-
1454
-
1455
-
1456
-
1457
-
1458
-
1459
-
1460
-
1461
-
1462
-
1463
-
1464
-
1465
-
1466
-
1467
-
1468
-
1469
-
1470
-
1471
-
1472
-
1473
-
1474
-
1475
-
1476
-
1477
-
1478
-
1479
-
1480
-
1481
-
1482
-
1483
-
1484
-
1485
-
1486
-
1487
-
1488
-
1489
-
1490
-
1491
-
1492
-
1493
-
1494
-
1495
-
1496
-
1497
-
1498
-
1499
-
1500
-
1501
-
1502
-
1503
-
1504
-
1505
-
1506
-
1507
-
1508
-
1509
-
1510
-
1511
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1512
-
1513
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1514
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1515
-
1516
-
1517
-
1518
-
1519
-
1520
-
1521
-
1522
-
1523
-
1524
-
1525
-
1526
-
1527
-
1528
-
1529
-
1530
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1531
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1532
-
1533
-
1534
-
1535
-
1536
-
1537
-
1538
-
1539
-
1540
-
1541
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1542
-
1543
-
1544
-
1545
-
1546
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1547
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1548
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1549
-
1550
-
1551
-
1552
-
1553
-
1554
-
1555
-
1556
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1557
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1558
-
1559
-
1560
-
1561
-
1562
-
1563
-
1564
-
1565
-
1566
-
1567
-
1568
-
1569
-
1570
-
1571
-
1572
-
1573
-
1574
-
1575
-
1576
-
1577
-
1578
-
1579
-
1580
-
1581
-
1582
-
1583
-
1584
-
1585
-
1586
-
1587
-
1588
-
1589
-
1590
-
1591
-
1592
-
1593
-
1594
-
1595
-
1596
-
1597
-
1598
-
1599
-
1600
-
1601
-
1602
-
1603
-
1604
-
1605
-
1606
-
1607
-
1608
-
1609
-
1610
-
1611
-
1612
-
1613
-
1614
-
1615
-
1616
-
1617
-
1618
-
1619
-
1620
-
1621
-
1622
-
1623
-
1624
-
1625
-
1626
-
1627
-
1628
-
1629
-
1630
-
1631
-
1632
-
1633
-
1634
-
1635
-
1636
-
1637
-
1638
-
1639
-
1640
-
1641
-
1642
-
1643
-
1644
-
1645
-
1646
-
1647
-
1648
-
1649
-
1650
-
1651
-
1652
-
1653
-
1654
-
1655
-
1656
-
1657
-
1658
-
1659
-
1660
-
1661
-
1662
-
1663
-
1664
-
1665
-
1666
-
1667
-
1668
-
1669
-
1670
-
1671
-
1672
-
1673
-
1674
-
1675
-
1676
-
1677
-
1678
-
1679
-
1680
-
1681
-
1682
-
1683
-
1684
-
1685
-
1686
-
1687
-
1688
-
1689
-
1690
-
1691
-
1692
-
1693
-
1694
-
1695
-
1696
-
1697
-
1698
-
1699
-
1700
-
1701
-
1702
-
1703
-
1704
-
1705
-
1706
-
1707
-
1708
-
1709
-
1710
-
1711
-
1712
-
1713
-
1714
-
1715
-
1716
-
1717
-
1718
-
1719
-
1720
-
1721
-
1722
-
1723
-
1724
-
1725
-
1726
-
1727
-
1728
-
1729
-
1730
-
1731
-
1732
-
1733
-
1734
-
1735
-
1736
- 姿
1737
-
1738
-
1739
-
1740
-
1741
-
1742
-
1743
-
1744
-
1745
-
1746
-
1747
-
1748
-
1749
-
1750
-
1751
-
1752
-
1753
-
1754
-
1755
-
1756
-
1757
-
1758
-
1759
-
1760
-
1761
-
1762
-
1763
-
1764
-
1765
-
1766
-
1767
-
1768
-
1769
-
1770
-
1771
-
1772
- 婿
1773
-
1774
-
1775
-
1776
-
1777
-
1778
-
1779
-
1780
-
1781
-
1782
-
1783
-
1784
-
1785
-
1786
-
1787
-
1788
-
1789
-
1790
-
1791
-
1792
-
1793
-
1794
-
1795
-
1796
-
1797
-
1798
-
1799
-
1800
-
1801
- 嬿
1802
-
1803
-
1804
-
1805
-
1806
-
1807
-
1808
-
1809
-
1810
-
1811
-
1812
-
1813
-
1814
-
1815
-
1816
-
1817
-
1818
-
1819
-
1820
-
1821
-
1822
-
1823
-
1824
-
1825
-
1826
-
1827
-
1828
-
1829
-
1830
-
1831
-
1832
-
1833
-
1834
-
1835
-
1836
-
1837
-
1838
-
1839
-
1840
-
1841
-
1842
-
1843
-
1844
-
1845
-
1846
-
1847
-
1848
-
1849
-
1850
-
1851
-
1852
-
1853
-
1854
-
1855
-
1856
-
1857
-
1858
-
1859
-
1860
-
1861
-
1862
-
1863
-
1864
-
1865
-
1866
-
1867
-
1868
- 宿
1869
-
1870
-
1871
-
1872
-
1873
-
1874
-
1875
-
1876
-
1877
-
1878
-
1879
-
1880
-
1881
-
1882
-
1883
-
1884
-
1885
-
1886
-
1887
-
1888
-
1889
-
1890
-
1891
-
1892
-
1893
- 寿
1894
-
1895
-
1896
-
1897
-
1898
-
1899
-
1900
-
1901
-
1902
-
1903
-
1904
-
1905
-
1906
-
1907
-
1908
-
1909
-
1910
-
1911
-
1912
-
1913
-
1914
-
1915
-
1916
-
1917
-
1918
-
1919
-
1920
- 尿
1921
-
1922
-
1923
-
1924
-
1925
-
1926
-
1927
-
1928
-
1929
-
1930
-
1931
-
1932
-
1933
-
1934
-
1935
-
1936
-
1937
-
1938
-
1939
-
1940
-
1941
-
1942
-
1943
- 屿
1944
-
1945
-
1946
-
1947
-
1948
-
1949
-
1950
-
1951
-
1952
-
1953
-
1954
-
1955
-
1956
-
1957
-
1958
-
1959
-
1960
-
1961
-
1962
-
1963
-
1964
-
1965
- ��
1966
-
1967
-
1968
-
1969
-
1970
-
1971
-
1972
-
1973
-
1974
-
1975
-
1976
-
1977
-
1978
-
1979
-
1980
-
1981
-
1982
-
1983
-
1984
-
1985
-
1986
-
1987
-
1988
-
1989
-
1990
-
1991
-
1992
-
1993
-
1994
-
1995
-
1996
-
1997
-
1998
-
1999
-
2000
-
2001
-
2002
-
2003
-
2004
-
2005
-
2006
-
2007
-
2008
-
2009
-
2010
-
2011
-
2012
-
2013
-
2014
-
2015
-
2016
-
2017
-
2018
-
2019
-
2020
-
2021
-
2022
-
2023
-
2024
-
2025
-
2026
-
2027
- 巿
2028
-
2029
-
2030
-
2031
-
2032
-
2033
-
2034
-
2035
-
2036
-
2037
-
2038
-
2039
-
2040
-
2041
-
2042
-
2043
-
2044
-
2045
-
2046
-
2047
-
2048
-
2049
-
2050
-
2051
-
2052
-
2053
-
2054
-
2055
-
2056
-
2057
-
2058
-
2059
-
2060
-
2061
-
2062
-
2063
-
2064
-
2065
-
2066
-
2067
-
2068
-
2069
-
2070
- 广
2071
-
2072
-
2073
-
2074
-
2075
-
2076
-
2077
-
2078
-
2079
-
2080
-
2081
-
2082
-
2083
-
2084
-
2085
-
2086
-
2087
-
2088
-
2089
-
2090
-
2091
-
2092
-
2093
-
2094
-
2095
-
2096
-
2097
-
2098
-
2099
-
2100
-
2101
-
2102
-
2103
-
2104
-
2105
-
2106
-
2107
- 廿
2108
-
2109
-
2110
-
2111
-
2112
-
2113
-
2114
-
2115
-
2116
-
2117
-
2118
-
2119
-
2120
-
2121
-
2122
-
2123
-
2124
-
2125
-
2126
-
2127
-
2128
-
2129
-
2130
-
2131
-
2132
-
2133
-
2134
-
2135
-
2136
-
2137
-
2138
-
2139
-
2140
-
2141
-
2142
-
2143
-
2144
-
2145
-
2146
-
2147
-
2148
-
2149
-
2150
-
2151
-
2152
-
2153
-
2154
-
2155
-
2156
-
2157
-
2158
- 彿
2159
-
2160
-
2161
-
2162
-
2163
-
2164
-
2165
-
2166
-
2167
-
2168
-
2169
-
2170
-
2171
-
2172
-
2173
-
2174
-
2175
-
2176
-
2177
-
2178
-
2179
-
2180
-
2181
-
2182
-
2183
-
2184
-
2185
-
2186
-
2187
-
2188
-
2189
-
2190
-
2191
-
2192
-
2193
-
2194
-
2195
-
2196
-
2197
-
2198
-
2199
-
2200
-
2201
-
2202
-
2203
-
2204
-
2205
-
2206
-
2207
-
2208
-
2209
-
2210
- 忿
2211
- 怀
2212
-
2213
-
2214
-
2215
-
2216
-
2217
-
2218
-
2219
-
2220
-
2221
-
2222
-
2223
-
2224
-
2225
-
2226
-
2227
-
2228
-
2229
-
2230
-
2231
-
2232
-
2233
-
2234
-
2235
-
2236
-
2237
-
2238
-
2239
-
2240
-
2241
-
2242
-
2243
-
2244
-
2245
-
2246
-
2247
-
2248
-
2249
-
2250
-
2251
-
2252
-
2253
-
2254
-
2255
-
2256
-
2257
-
2258
-
2259
-
2260
-
2261
-
2262
-
2263
-
2264
-
2265
-
2266
-
2267
-
2268
-
2269
-
2270
-
2271
-
2272
-
2273
-
2274
-
2275
-
2276
-
2277
-
2278
-
2279
-
2280
-
2281
-
2282
-
2283
-
2284
-
2285
-
2286
-
2287
-
2288
-
2289
-
2290
-
2291
-
2292
-
2293
-
2294
-
2295
-
2296
-
2297
-
2298
-
2299
-
2300
-
2301
-
2302
-
2303
-
2304
-
2305
-
2306
-
2307
-
2308
-
2309
-
2310
-
2311
-
2312
-
2313
-
2314
-
2315
-
2316
-
2317
-
2318
-
2319
-
2320
-
2321
-
2322
-
2323
-
2324
-
2325
-
2326
-
2327
-
2328
-
2329
-
2330
-
2331
-
2332
-
2333
-
2334
-
2335
-
2336
-
2337
-
2338
-
2339
-
2340
-
2341
-
2342
-
2343
-
2344
-
2345
-
2346
-
2347
-
2348
-
2349
-
2350
-
2351
-
2352
-
2353
-
2354
-
2355
-
2356
-
2357
-
2358
-
2359
-
2360
-
2361
-
2362
-
2363
-
2364
-
2365
-
2366
-
2367
-
2368
-
2369
-
2370
-
2371
-
2372
-
2373
-
2374
-
2375
-
2376
-
2377
-
2378
-
2379
-
2380
-
2381
-
2382
-
2383
-
2384
-
2385
-
2386
-
2387
-
2388
-
2389
-
2390
-
2391
-
2392
-
2393
-
2394
-
2395
-
2396
-
2397
-
2398
-
2399
-
2400
-
2401
-
2402
-
2403
-
2404
-
2405
-
2406
-
2407
-
2408
-
2409
-
2410
-
2411
-
2412
-
2413
-
2414
-
2415
-
2416
-
2417
-
2418
-
2419
-
2420
-
2421
-
2422
-
2423
-
2424
-
2425
-
2426
-
2427
-
2428
-
2429
-
2430
-
2431
-
2432
-
2433
-
2434
-
2435
-
2436
-
2437
-
2438
-
2439
-
2440
-
2441
-
2442
-
2443
-
2444
-
2445
-
2446
-
2447
-
2448
-
2449
-
2450
-
2451
-
2452
-
2453
-
2454
-
2455
-
2456
-
2457
-
2458
-
2459
-
2460
-
2461
-
2462
-
2463
-
2464
-
2465
-
2466
-
2467
-
2468
-
2469
-
2470
-
2471
-
2472
-
2473
-
2474
-
2475
-
2476
-
2477
-
2478
-
2479
-
2480
-
2481
-
2482
-
2483
-
2484
-
2485
-
2486
-
2487
-
2488
-
2489
-
2490
-
2491
-
2492
-
2493
-
2494
-
2495
-
2496
-
2497
-
2498
-
2499
-
2500
-
2501
-
2502
-
2503
-
2504
-
2505
-
2506
-
2507
-
2508
-
2509
-
2510
-
2511
-
2512
-
2513
-
2514
-
2515
-
2516
-
2517
-
2518
-
2519
-
2520
-
2521
-
2522
-
2523
-
2524
-
2525
-
2526
-
2527
-
2528
-
2529
-
2530
-
2531
-
2532
-
2533
-
2534
-
2535
-
2536
-
2537
-
2538
-
2539
-
2540
-
2541
-
2542
-
2543
-
2544
-
2545
-
2546
-
2547
-
2548
-
2549
-
2550
-
2551
-
2552
-
2553
-
2554
-
2555
-
2556
-
2557
-
2558
-
2559
-
2560
-
2561
-
2562
-
2563
-
2564
-
2565
-
2566
-
2567
-
2568
-
2569
-
2570
-
2571
-
2572
-
2573
-
2574
-
2575
-
2576
-
2577
-
2578
-
2579
-
2580
-
2581
-
2582
-
2583
-
2584
-
2585
-
2586
-
2587
-
2588
-
2589
-
2590
-
2591
-
2592
-
2593
-
2594
-
2595
-
2596
-
2597
-
2598
-
2599
-
2600
-
2601
-
2602
-
2603
-
2604
-
2605
-
2606
-
2607
-
2608
-
2609
-
2610
-
2611
-
2612
-
2613
-
2614
-
2615
-
2616
-
2617
-
2618
-
2619
-
2620
-
2621
-
2622
-
2623
-
2624
-
2625
-
2626
-
2627
-
2628
-
2629
-
2630
-
2631
-
2632
-
2633
-
2634
-
2635
-
2636
-
2637
-
2638
-
2639
-
2640
-
2641
-
2642
-
2643
-
2644
-
2645
-
2646
-
2647
-
2648
-
2649
-
2650
-
2651
-
2652
-
2653
-
2654
-
2655
-
2656
-
2657
-
2658
-
2659
-
2660
-
2661
-
2662
-
2663
-
2664
-
2665
-
2666
-
2667
-
2668
-
2669
-
2670
-
2671
-
2672
-
2673
-
2674
-
2675
-
2676
-
2677
-
2678
-
2679
-
2680
-
2681
-
2682
-
2683
-
2684
-
2685
-
2686
-
2687
-
2688
-
2689
-
2690
-
2691
-
2692
-
2693
-
2694
-
2695
-
2696
-
2697
-
2698
-
2699
-
2700
-
2701
-
2702
-
2703
-
2704
-
2705
-
2706
-
2707
-
2708
-
2709
-
2710
-
2711
-
2712
-
2713
-
2714
-
2715
-
2716
-
2717
-
2718
-
2719
-
2720
-
2721
-
2722
-
2723
-
2724
-
2725
-
2726
-
2727
-
2728
-
2729
-
2730
-
2731
-
2732
-
2733
-
2734
-
2735
-
2736
-
2737
-
2738
-
2739
-
2740
-
2741
-
2742
-
2743
-
2744
-
2745
-
2746
-
2747
-
2748
-
2749
-
2750
-
2751
-
2752
-
2753
-
2754
-
2755
-
2756
-
2757
-
2758
-
2759
-
2760
-
2761
-
2762
-
2763
-
2764
-
2765
-
2766
-
2767
-
2768
-
2769
-
2770
-
2771
-
2772
-
2773
-
2774
-
2775
-
2776
-
2777
-
2778
-
2779
-
2780
-
2781
-
2782
-
2783
-
2784
-
2785
-
2786
-
2787
-
2788
-
2789
-
2790
-
2791
-
2792
-
2793
-
2794
-
2795
-
2796
-
2797
-
2798
-
2799
-
2800
-
2801
-
2802
-
2803
-
2804
-
2805
-
2806
-
2807
-
2808
-
2809
-
2810
-
2811
-
2812
-
2813
-
2814
-
2815
-
2816
-
2817
-
2818
-
2819
-
2820
-
2821
-
2822
-
2823
-
2824
-
2825
-
2826
-
2827
-
2828
-
2829
-
2830
-
2831
-
2832
-
2833
-
2834
-
2835
-
2836
-
2837
-
2838
-
2839
-
2840
-
2841
-
2842
-
2843
-
2844
-
2845
-
2846
-
2847
-
2848
-
2849
-
2850
-
2851
-
2852
-
2853
-
2854
-
2855
-
2856
-
2857
-
2858
-
2859
-
2860
-
2861
-
2862
-
2863
-
2864
-
2865
-
2866
-
2867
-
2868
-
2869
-
2870
-
2871
-
2872
-
2873
-
2874
-
2875
-
2876
-
2877
-
2878
-
2879
-
2880
-
2881
-
2882
-
2883
-
2884
-
2885
-
2886
-
2887
-
2888
-
2889
-
2890
-
2891
-
2892
-
2893
-
2894
-
2895
-
2896
-
2897
-
2898
-
2899
-
2900
-
2901
-
2902
-
2903
-
2904
-
2905
-
2906
-
2907
-
2908
-
2909
-
2910
-
2911
-
2912
-
2913
-
2914
-
2915
-
2916
-
2917
-
2918
-
2919
-
2920
-
2921
-
2922
-
2923
-
2924
-
2925
-
2926
-
2927
-
2928
-
2929
-
2930
-
2931
-
2932
-
2933
-
2934
-
2935
-
2936
-
2937
-
2938
-
2939
-
2940
-
2941
-
2942
-
2943
-
2944
-
2945
-
2946
-
2947
-
2948
-
2949
-
2950
-
2951
-
2952
-
2953
-
2954
-
2955
-
2956
-
2957
-
2958
-
2959
-
2960
-
2961
-
2962
-
2963
-
2964
-
2965
-
2966
-
2967
-
2968
-
2969
-
2970
-
2971
-
2972
-
2973
-
2974
-
2975
-
2976
-
2977
-
2978
-
2979
-
2980
-
2981
-
2982
-
2983
-
2984
-
2985
-
2986
-
2987
-
2988
-
2989
- ��
2990
-
2991
-
2992
-
2993
-
2994
-
2995
-
2996
-
2997
-
2998
- 椿
2999
-
3000
-
3001
-
3002
-
3003
-
3004
-
3005
-
3006
-
3007
-
3008
-
3009
-
3010
-
3011
-
3012
-
3013
-
3014
-
3015
-
3016
-
3017
-
3018
-
3019
-
3020
-
3021
-
3022
-
3023
-
3024
-
3025
-
3026
-
3027
-
3028
-
3029
-
3030
-
3031
-
3032
-
3033
-
3034
-
3035
-
3036
-
3037
-
3038
-
3039
-
3040
-
3041
-
3042
-
3043
- 槿
3044
-
3045
-
3046
-
3047
-
3048
-
3049
-
3050
-
3051
-
3052
-
3053
-
3054
-
3055
-
3056
-
3057
-
3058
-
3059
-
3060
-
3061
-
3062
-
3063
-
3064
-
3065
-
3066
-
3067
-
3068
-
3069
-
3070
-
3071
-
3072
-
3073
-
3074
-
3075
-
3076
-
3077
-
3078
-
3079
-
3080
-
3081
-
3082
-
3083
-
3084
-
3085
-
3086
-
3087
-
3088
-
3089
-
3090
-
3091
-
3092
-
3093
-
3094
-
3095
-
3096
-
3097
-
3098
-
3099
-
3100
-
3101
-
3102
-
3103
-
3104
-
3105
-
3106
-
3107
-
3108
-
3109
-
3110
-
3111
-
3112
-
3113
-
3114
-
3115
-
3116
-
3117
- 殿
3118
-
3119
-
3120
-
3121
-
3122
-
3123
-
3124
-
3125
-
3126
-
3127
-
3128
-
3129
-
3130
-
3131
-
3132
-
3133
-
3134
-
3135
-
3136
-
3137
-
3138
-
3139
-
3140
-
3141
-
3142
-
3143
-
3144
-
3145
-
3146
-
3147
-
3148
-
3149
-
3150
-
3151
-
3152
-
3153
-
3154
-
3155
-
3156
-
3157
-
3158
-
3159
-
3160
-
3161
-
3162
-
3163
-
3164
-
3165
-
3166
-
3167
-
3168
-
3169
-
3170
-
3171
-
3172
-
3173
-
3174
-
3175
-
3176
-
3177
-
3178
-
3179
-
3180
-
3181
-
3182
-
3183
-
3184
-
3185
-
3186
-
3187
-
3188
-
3189
-
3190
-
3191
-
3192
-
3193
-
3194
-
3195
-
3196
-
3197
-
3198
-
3199
-
3200
-
3201
-
3202
-
3203
-
3204
-
3205
-
3206
-
3207
-
3208
-
3209
-
3210
-
3211
-
3212
-
3213
-
3214
-
3215
-
3216
-
3217
-
3218
-
3219
-
3220
-
3221
- 沿
3222
-
3223
-
3224
-
3225
-
3226
-
3227
-
3228
-
3229
-
3230
-
3231
-
3232
-
3233
-
3234
-
3235
-
3236
-
3237
-
3238
-
3239
-
3240
-
3241
-
3242
-
3243
-
3244
-
3245
-
3246
-
3247
-
3248
-
3249
-
3250
-
3251
-
3252
-
3253
-
3254
-
3255
-
3256
-
3257
-
3258
-
3259
-
3260
-
3261
-
3262
-
3263
-
3264
-
3265
-
3266
-
3267
-
3268
-
3269
-
3270
-
3271
-
3272
-
3273
-
3274
-
3275
-
3276
-
3277
-
3278
-
3279
-
3280
-
3281
-
3282
-
3283
-
3284
-
3285
-
3286
-
3287
-
3288
-
3289
-
3290
-
3291
-
3292
-
3293
-
3294
-
3295
-
3296
-
3297
-
3298
-
3299
-
3300
-
3301
-
3302
-
3303
-
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6637
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6644
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6650
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6651
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6652
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6654
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6656
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6657
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6658
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6659
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6662
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6663
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6666
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6680
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6683
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6684
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6685
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6688
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6689
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6692
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6699
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6700
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6701
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6702
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6704
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6705
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6712
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6714
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6716
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6719
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6720
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36
 
37
- def has_zh_char(text):
 
 
 
38
  return any(ch in zh_punc for ch in text)
39
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
40
  cache = {}
41
 
 
42
  def iter_vocab(tokenizer, name="", from_cache=True):
43
  if from_cache and name in cache:
44
  return cache[name]
45
 
46
  f_out = open(name + "_vocab.zh.jsonl", "w", encoding="utf-8")
47
  zh_token_count = {"total": 0, "中文单字": 0, "中文多字": 0}
 
 
 
 
 
48
  all_single_zh_tokens = set()
49
  zh_symbol_count = 0
50
- for idx in range(tokenizer.vocab_size):
51
- decode_str = tokenizer.decode([idx])
52
- if has_chinese(decode_str):
53
- # bert词典有 ##开头的
54
- # byteBPE词典有带空格的
55
- decode_str = decode_str.strip().replace("#", "") # TODO, 按类型
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
56
  zh_token_count["total"] += 1
57
- if len(decode_str) > 1:
58
  zh_token_count["中文多字"] += 1
59
- f_out.write(json.dumps({"id": idx, "token": decode_str, "type": "中文多字"},
60
- ensure_ascii=False) + "\n")
61
  else:
62
- all_single_zh_tokens.add(decode_str)
63
  zh_token_count["中文单字"] += 1
64
- f_out.write(json.dumps({"id": idx, "token": decode_str, "type": "中文单字"},
65
- ensure_ascii=False) + "\n")
66
-
67
- elif has_zh_char(decode_str):
68
- zh_symbol_count += 1
69
- f_out.write(json.dumps({"id": idx, "token": decode_str, "type": "中文标点"},
70
- ensure_ascii=False) + "\n")
71
-
72
  #
73
 
74
  dist_length, mean_length = get_coding_length(tokenizer, zh_tokens, filter=lambda k: not is_chinese(k))
@@ -94,5 +140,9 @@ if __name__ == "__main__":
94
  # test_coding_length(zh_punc)
95
  # test_coding_length(zh_iterator())
96
 
97
- from vocab.gpt_35_turbo import tokenizer
98
- iter_vocab(tokenizer)
 
 
 
 
 
1
  """
2
+ TODO: 繁体、简体、语种、
3
  """
4
  import os
5
  import json
6
  from collections import Counter
7
+ from utils.text_util import is_chinese, get_zh_count, get_digit_count
8
  from zhon.hanzi import punctuation as zh_punc
9
 
10
  CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
11
 
12
+ zh_tokens = [line.strip() for line in open(os.path.join(CURRENT_DIR, "vocab.jd.txt.v2"), "r", encoding="utf-8") if
13
+ is_chinese(line.strip())]
14
+
15
 
16
  def zh_iterator():
17
  for idx in range(ord(u'\u4e00'), ord(u'\u9fa5')):
 
19
 
20
 
21
  def get_coding_length(tokenizer, vocab, filter=None):
22
+ """
23
+ 计算编码长度。(有些中文汉字被解码成多个token)
24
+ """
25
  all_length = []
26
  for word in vocab:
27
  if len(word) > 1:
 
39
  return dist_length, mean_length
40
 
41
 
42
+ def has_zh_punc(text):
43
+ """
44
+ 是否包含中文标点
45
+ """
46
  return any(ch in zh_punc for ch in text)
47
 
48
+
49
+
50
+ def get_space_count(text):
51
+ space_count = 0
52
+ for char in text:
53
+ if len(char.strip()) == 0:
54
+ space_count += 1
55
+ return space_count
56
+
57
+
58
+ def remove_special_char():
59
+ """
60
+ :return:
61
+ """
62
+ # bert词典有 ##开头的
63
+ # byteBPE词典有带空格的
64
+ # decode_str = decode_str.strip().replace("#", "") # TODO, 按类型
65
+ pass
66
+
67
+
68
  cache = {}
69
 
70
+
71
  def iter_vocab(tokenizer, name="", from_cache=True):
72
  if from_cache and name in cache:
73
  return cache[name]
74
 
75
  f_out = open(name + "_vocab.zh.jsonl", "w", encoding="utf-8")
76
  zh_token_count = {"total": 0, "中文单字": 0, "中文多字": 0}
77
+
78
+ # zh_token_count = {"total": 0, "包含1个中文单字": 0, "中文多字": 0}
79
+
80
+ # symbol_count = 0
81
+
82
  all_single_zh_tokens = set()
83
  zh_symbol_count = 0
84
+ for token_id in range(tokenizer.vocab_size):
85
+ decode_str = tokenizer.decode([token_id], skip_special_tokens=False)
86
+ token = tokenizer.convert_ids_to_tokens([token_id], skip_special_tokens=False)[0]
87
+ # tokenizer.convert_tokens_to_string(tokens)
88
+
89
+ if token is None: # 有些词典有空的id(不连续)
90
+ continue
91
+ if isinstance(token, bytes):
92
+ token = token.decode("utf-8", errors="ignore")
93
+
94
+ digit_count = get_digit_count(token)
95
+ zh_count = get_zh_count(decode_str)
96
+ space_count = get_space_count(decode_str)
97
+
98
+ f_out.write(json.dumps(
99
+ {"id": token_id,
100
+ "token": token,
101
+ "token_decode": decode_str,
102
+ "token_len": len(token),
103
+ "zh_count": zh_count,
104
+ "space_count": space_count,
105
+ "digit_count": digit_count,
106
+ "zh_symbol_count": zh_symbol_count,
107
+ },
108
+ ensure_ascii=False) + "\n"
109
+ )
110
+
111
+ if zh_count >= 1:
112
  zh_token_count["total"] += 1
113
+ if zh_count > 1:
114
  zh_token_count["中文多字"] += 1
 
 
115
  else:
 
116
  zh_token_count["中文单字"] += 1
117
+ all_single_zh_tokens.add(decode_str.strip().replace("#", ""))
 
 
 
 
 
 
 
118
  #
119
 
120
  dist_length, mean_length = get_coding_length(tokenizer, zh_tokens, filter=lambda k: not is_chinese(k))
 
140
  # test_coding_length(zh_punc)
141
  # test_coding_length(zh_iterator())
142
 
143
+ from vocab.chatglm2_6b import tokenizer; name = "chatglm2_6b"
144
+ # from vocab.chatglm_6b import tokenizer; name="chatglm_6b"
145
+ # from vocab.baichuan2 import tokenizer; name="baichuan2"
146
+ # from vocab.gpt_4 import tokenizer; name="gpt4"
147
+
148
+ print(iter_vocab(tokenizer, name=name))
vocab/README.md CHANGED
@@ -1,4 +1,7 @@
1
 
 
 
 
2
  ## 词典构建
3
 
4
  bert词典
 
1
 
2
+ 对于OpenAI的模型而言,英文的Token效率是中文的8-12倍,
3
+ 之前三百字中文以上时Turbo 3.5 16k就会出现逻辑颠倒问题,提示词换成英文后该问题没有出现过。
4
+
5
  ## 词典构建
6
 
7
  bert词典
vocab/__init__.py CHANGED
@@ -4,8 +4,10 @@ from enum import Enum, auto
4
  """Interface:
5
  tokenizer.encode
6
  tokenizer.decode
 
7
  tokenizer.convert_ids_to_tokens
8
 
 
9
  tokenizer.parent = ""
10
  tokenizer.vocab_size
11
  tokenizer.get_vocab() # gpt-neox-20b, llama
@@ -21,8 +23,9 @@ tokenizer.implementation = TokenizerImpl.SentencePiece.name # https://github.c
21
  - sentencepiece:
22
  - 特征:.sp_model 是SentencePieceProcessor类型,sp_model.id_to_piece,有tokenizer.json tokenizer.model,词典字符有 ▁,
23
  - 示例:llama,baichuan
 
 
24
  - tiktoken
25
- - icetk
26
  - hf_tokenizer
27
  - 特征:
28
  - .model 是 tokenizer.models.BPE 类型
@@ -32,7 +35,7 @@ tokenizer.implementation = TokenizerImpl.SentencePiece.name # https://github.c
32
  - 示例:gpt_neox_20b, moss, bloom
33
  - tiktoken
34
  - 特征:空格就是空格,
35
- - 示例:gpt3.5 gpt4
36
  tokenizer.comments = "split all numbers into individual digits, " \
37
  "and fallback to bytes to decompose unknown UTF-8 characters"
38
 
@@ -50,14 +53,19 @@ uniq_tokenizers = [
50
  ]
51
 
52
  all_tokenizers = [
53
- "gpt_35_turbo",
54
- "gpt_4",
55
  "gpt2",
56
  "gpt2_chinese",
 
 
 
 
57
  "bert_base_cased",
58
  "bert_base_uncased",
59
  "bert_base_chinese",
 
60
  "kplug",
 
 
61
  "moss",
62
  #
63
  # ######
@@ -76,9 +84,11 @@ all_tokenizers = [
76
  "glm_chinese",
77
  "chatglm_6b",
78
  "chatglm2_6b",
 
79
  #
80
  # #### llama alpaca系列
81
  "llama", # '中文单字': 700, '中文多字': 0
 
82
  "chinese_llama", #
83
  "chinese_llama2", #
84
  # "chinese_alpaca_lora_7b", # 中文Alpaca模型在上述中文LLaMA模型的基础上进一步使用了指令数据进行精调。
@@ -90,6 +100,15 @@ all_tokenizers = [
90
  "internlm_chat_7b",
91
  "falcon_180b",
92
  # "goat",
 
 
 
 
 
 
 
 
 
93
  ]
94
 
95
  class TokenizerType(Enum):
 
4
  """Interface:
5
  tokenizer.encode
6
  tokenizer.decode
7
+ tokenizer.convert_tokens_to_string # gpt4 没有这个方法
8
  tokenizer.convert_ids_to_tokens
9
 
10
+
11
  tokenizer.parent = ""
12
  tokenizer.vocab_size
13
  tokenizer.get_vocab() # gpt-neox-20b, llama
 
23
  - sentencepiece:
24
  - 特征:.sp_model 是SentencePieceProcessor类型,sp_model.id_to_piece,有tokenizer.json tokenizer.model,词典字符有 ▁,
25
  - 示例:llama,baichuan
26
+ - icetk: sentencepiece的分支,支持image_tokenizer
27
+ - glm, chatglm1, chatglm2
28
  - tiktoken
 
29
  - hf_tokenizer
30
  - 特征:
31
  - .model 是 tokenizer.models.BPE 类型
 
35
  - 示例:gpt_neox_20b, moss, bloom
36
  - tiktoken
37
  - 特征:空格就是空格,
38
+ - 示例:gpt3.5 gpt4, qwen,
39
  tokenizer.comments = "split all numbers into individual digits, " \
40
  "and fallback to bytes to decompose unknown UTF-8 characters"
41
 
 
53
  ]
54
 
55
  all_tokenizers = [
 
 
56
  "gpt2",
57
  "gpt2_chinese",
58
+ "gpt_35_turbo",
59
+ "gpt_4",
60
+
61
+ # bert 系列
62
  "bert_base_cased",
63
  "bert_base_uncased",
64
  "bert_base_chinese",
65
+ "roberta_chinese_clue",
66
  "kplug",
67
+
68
+ # gpt2 系列
69
  "moss",
70
  #
71
  # ######
 
84
  "glm_chinese",
85
  "chatglm_6b",
86
  "chatglm2_6b",
87
+ "chatglm3_6b",
88
  #
89
  # #### llama alpaca系列
90
  "llama", # '中文单字': 700, '中文多字': 0
91
+ "llama2",
92
  "chinese_llama", #
93
  "chinese_llama2", #
94
  # "chinese_alpaca_lora_7b", # 中文Alpaca模型在上述中文LLaMA模型的基础上进一步使用了指令数据进行精调。
 
100
  "internlm_chat_7b",
101
  "falcon_180b",
102
  # "goat",
103
+
104
+ # tiktoken 系列
105
+ "qwen_7b_chat",
106
+ "qwen_72b_chat",
107
+
108
+ # 未分类
109
+
110
+ "mistral",
111
+
112
  ]
113
 
114
  class TokenizerType(Enum):
vocab/baichuan/Baichuan-7B/tokenization_baichuan.py CHANGED
@@ -71,6 +71,11 @@ class BaiChuanTokenizer(PreTrainedTokenizer):
71
  eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
72
  unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
73
  pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
 
 
 
 
 
74
  super().__init__(
75
  bos_token=bos_token,
76
  eos_token=eos_token,
@@ -82,11 +87,6 @@ class BaiChuanTokenizer(PreTrainedTokenizer):
82
  clean_up_tokenization_spaces=clean_up_tokenization_spaces,
83
  **kwargs,
84
  )
85
- self.vocab_file = vocab_file
86
- self.add_bos_token = add_bos_token
87
- self.add_eos_token = add_eos_token
88
- self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
89
- self.sp_model.Load(vocab_file)
90
 
91
  def __getstate__(self):
92
  state = self.__dict__.copy()
 
71
  eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
72
  unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
73
  pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
74
+ self.vocab_file = vocab_file
75
+ self.add_bos_token = add_bos_token
76
+ self.add_eos_token = add_eos_token
77
+ self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
78
+ self.sp_model.Load(vocab_file)
79
  super().__init__(
80
  bos_token=bos_token,
81
  eos_token=eos_token,
 
87
  clean_up_tokenization_spaces=clean_up_tokenization_spaces,
88
  **kwargs,
89
  )
 
 
 
 
 
90
 
91
  def __getstate__(self):
92
  state = self.__dict__.copy()
vocab/baichuan/__init__.py CHANGED
@@ -1,7 +1,15 @@
 
 
1
  from transformers import AutoTokenizer
2
  from vocab import TokenizerType
3
 
4
- tokenizer = AutoTokenizer.from_pretrained("baichuan-inc/Baichuan-7B", trust_remote_code=True)
 
 
 
 
 
 
5
 
6
 
7
  # byte-bpe sentencepiece
 
1
+ import os
2
+ import config
3
  from transformers import AutoTokenizer
4
  from vocab import TokenizerType
5
 
6
+
7
+ if config.USE_REMOTE:
8
+ tokenizer = AutoTokenizer.from_pretrained("baichuan-inc/Baichuan-7B", trust_remote_code=True)
9
+ else:
10
+ CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
11
+ TOKENIZER_DIR = os.path.join(CURRENT_DIR, "Baichuan-7B")
12
+ tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_DIR, trust_remote_code=True)
13
 
14
 
15
  # byte-bpe sentencepiece
vocab/baichuan/demo.py CHANGED
@@ -1,3 +1,6 @@
1
 
2
- from vocab.baichuan_7b import tokenizer
 
 
 
3
 
 
1
 
2
+ from vocab.baichuan import tokenizer
3
+
4
+ id1 = tokenizer.encode("<pad>")
5
+ token1 = tokenizer.decode(125696)
6
 
vocab/baichuan/error.md ADDED
@@ -0,0 +1,8 @@
 
 
 
 
 
 
 
 
 
1
+
2
+
3
+ ## AttributeError: 'BaichuanTokenizer' object has no attribute 'sp_model'
4
+
5
+ https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat/discussions/18
6
+
7
+
8
+ transfomers 4.34 doesn't work for me either. Degrading to 4.33.1 works in my case
vocab/baichuan2/Baichuan2-7B-Chat/special_tokens_map.json ADDED
@@ -0,0 +1,30 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "bos_token": {
3
+ "content": "<s>",
4
+ "lstrip": false,
5
+ "normalized": true,
6
+ "rstrip": false,
7
+ "single_word": false
8
+ },
9
+ "eos_token": {
10
+ "content": "</s>",
11
+ "lstrip": false,
12
+ "normalized": true,
13
+ "rstrip": false,
14
+ "single_word": false
15
+ },
16
+ "pad_token": {
17
+ "content": "<unk>",
18
+ "lstrip": false,
19
+ "normalized": true,
20
+ "rstrip": false,
21
+ "single_word": false
22
+ },
23
+ "unk_token": {
24
+ "content": "<unk>",
25
+ "lstrip": false,
26
+ "normalized": true,
27
+ "rstrip": false,
28
+ "single_word": false
29
+ }
30
+ }
vocab/baichuan2/Baichuan2-7B-Chat/tokenization_baichuan.py ADDED
@@ -0,0 +1,251 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright 2023 Baichuan Inc. All Rights Reserved.
2
+
3
+ # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
4
+ #
5
+ # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
6
+ # and OPT implementations in this library. It has been modified from its
7
+ # original forms to accommodate minor architectural differences compared
8
+ # to GPT-NeoX and OPT used by the Meta AI team that trained the model.
9
+ #
10
+ # Licensed under the Apache License, Version 2.0 (the "License");
11
+ # you may not use this file except in compliance with the License.
12
+ # You may obtain a copy of the License at
13
+ #
14
+ # http://www.apache.org/licenses/LICENSE-2.0
15
+ #
16
+ # Unless required by applicable law or agreed to in writing, software
17
+ # distributed under the License is distributed on an "AS IS" BASIS,
18
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
19
+ # See the License for the specific language governing permissions and
20
+ # limitations under the License.
21
+
22
+ import os
23
+ from shutil import copyfile
24
+ from typing import Any, Dict, List, Optional, Tuple
25
+
26
+ import sentencepiece as spm
27
+
28
+ from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
29
+ from transformers.utils import logging
30
+
31
+
32
+ logger = logging.get_logger(__name__)
33
+
34
+ VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"}
35
+
36
+ PRETRAINED_VOCAB_FILES_MAP = {
37
+ "vocab_file": {},
38
+ "tokenizer_file": {},
39
+ }
40
+ PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {}
41
+
42
+
43
+ class BaichuanTokenizer(PreTrainedTokenizer):
44
+ """
45
+ Construct a Baichuan tokenizer. Based on byte-level Byte-Pair-Encoding.
46
+
47
+ Args:
48
+ vocab_file (`str`):
49
+ Path to the vocabulary file.
50
+ """
51
+
52
+ vocab_files_names = VOCAB_FILES_NAMES
53
+ pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
54
+ max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
55
+ model_input_names = ["input_ids", "attention_mask"]
56
+
57
+ def __init__(
58
+ self,
59
+ vocab_file,
60
+ unk_token="<unk>",
61
+ bos_token="<s>",
62
+ eos_token="</s>",
63
+ pad_token=None,
64
+ sp_model_kwargs: Optional[Dict[str, Any]] = None,
65
+ add_bos_token=True,
66
+ add_eos_token=False,
67
+ clean_up_tokenization_spaces=False,
68
+ **kwargs,
69
+ ):
70
+ self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
71
+ bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
72
+ eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
73
+ unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
74
+ pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
75
+ self.vocab_file = vocab_file
76
+ self.add_bos_token = add_bos_token
77
+ self.add_eos_token = add_eos_token
78
+ self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
79
+ self.sp_model.Load(vocab_file)
80
+ super().__init__(
81
+ bos_token=bos_token,
82
+ eos_token=eos_token,
83
+ unk_token=unk_token,
84
+ pad_token=pad_token,
85
+ add_bos_token=add_bos_token,
86
+ add_eos_token=add_eos_token,
87
+ sp_model_kwargs=self.sp_model_kwargs,
88
+ clean_up_tokenization_spaces=clean_up_tokenization_spaces,
89
+ **kwargs,
90
+ )
91
+
92
+ def __getstate__(self):
93
+ state = self.__dict__.copy()
94
+ state["sp_model"] = None
95
+ return state
96
+
97
+ def __setstate__(self, d):
98
+ self.__dict__ = d
99
+ self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
100
+ self.sp_model.Load(self.vocab_file)
101
+
102
+ @property
103
+ def vocab_size(self):
104
+ """Returns vocab size"""
105
+ return self.sp_model.get_piece_size()
106
+
107
+ def get_vocab(self):
108
+ """Returns vocab as a dict"""
109
+ vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
110
+ vocab.update(self.added_tokens_encoder)
111
+ return vocab
112
+
113
+ def _tokenize(self, text):
114
+ """Returns a tokenized string."""
115
+ return self.sp_model.encode(text, out_type=str)
116
+
117
+ def _convert_token_to_id(self, token):
118
+ """Converts a token (str) in an id using the vocab."""
119
+ return self.sp_model.piece_to_id(token)
120
+
121
+ def _convert_id_to_token(self, index):
122
+ """Converts an index (integer) in a token (str) using the vocab."""
123
+ token = self.sp_model.IdToPiece(index)
124
+ return token
125
+
126
+ def convert_tokens_to_string(self, tokens):
127
+ """Converts a sequence of tokens (string) in a single string."""
128
+ current_sub_tokens = []
129
+ out_string = ""
130
+ prev_is_special = False
131
+ for i, token in enumerate(tokens):
132
+ # make sure that special tokens are not decoded using sentencepiece model
133
+ if token in self.all_special_tokens:
134
+ if not prev_is_special and i != 0:
135
+ out_string += " "
136
+ out_string += self.sp_model.decode(current_sub_tokens) + token
137
+ prev_is_special = True
138
+ current_sub_tokens = []
139
+ else:
140
+ current_sub_tokens.append(token)
141
+ prev_is_special = False
142
+ out_string += self.sp_model.decode(current_sub_tokens)
143
+ return out_string
144
+
145
+ def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
146
+ """
147
+ Save the vocabulary and special tokens file to a directory.
148
+
149
+ Args:
150
+ save_directory (`str`):
151
+ The directory in which to save the vocabulary.
152
+
153
+ Returns:
154
+ `Tuple(str)`: Paths to the files saved.
155
+ """
156
+ if not os.path.isdir(save_directory):
157
+ logger.error(f"Vocabulary path ({save_directory}) should be a directory")
158
+ return
159
+ out_vocab_file = os.path.join(
160
+ save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
161
+ )
162
+
163
+ if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
164
+ copyfile(self.vocab_file, out_vocab_file)
165
+ elif not os.path.isfile(self.vocab_file):
166
+ with open(out_vocab_file, "wb") as fi:
167
+ content_spiece_model = self.sp_model.serialized_model_proto()
168
+ fi.write(content_spiece_model)
169
+
170
+ return (out_vocab_file,)
171
+
172
+ def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
173
+ bos_token_id = [self.bos_token_id] if self.add_bos_token else []
174
+ eos_token_id = [self.eos_token_id] if self.add_eos_token else []
175
+
176
+ output = bos_token_id + token_ids_0 + eos_token_id
177
+
178
+ if token_ids_1 is not None:
179
+ output = output + bos_token_id + token_ids_1 + eos_token_id
180
+
181
+ return output
182
+
183
+ def get_special_tokens_mask(
184
+ self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
185
+ ) -> List[int]:
186
+ """
187
+ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
188
+ special tokens using the tokenizer `prepare_for_model` method.
189
+
190
+ Args:
191
+ token_ids_0 (`List[int]`):
192
+ List of IDs.
193
+ token_ids_1 (`List[int]`, *optional*):
194
+ Optional second list of IDs for sequence pairs.
195
+ already_has_special_tokens (`bool`, *optional*, defaults to `False`):
196
+ Whether or not the token list is already formatted with special tokens for the model.
197
+
198
+ Returns:
199
+ `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
200
+ """
201
+ if already_has_special_tokens:
202
+ return super().get_special_tokens_mask(
203
+ token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
204
+ )
205
+
206
+ bos_token_id = [1] if self.add_bos_token else []
207
+ eos_token_id = [1] if self.add_eos_token else []
208
+
209
+ if token_ids_1 is None:
210
+ return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id
211
+ return (
212
+ bos_token_id
213
+ + ([0] * len(token_ids_0))
214
+ + eos_token_id
215
+ + bos_token_id
216
+ + ([0] * len(token_ids_1))
217
+ + eos_token_id
218
+ )
219
+
220
+ def create_token_type_ids_from_sequences(
221
+ self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
222
+ ) -> List[int]:
223
+ """
224
+ Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
225
+ sequence pair mask has the following format:
226
+
227
+ ```
228
+ 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
229
+ | first sequence | second sequence |
230
+ ```
231
+
232
+ if token_ids_1 is None, only returns the first portion of the mask (0s).
233
+
234
+ Args:
235
+ token_ids_0 (`List[int]`):
236
+ List of ids.
237
+ token_ids_1 (`List[int]`, *optional*):
238
+ Optional second list of IDs for sequence pairs.
239
+
240
+ Returns:
241
+ `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
242
+ """
243
+ bos_token_id = [self.bos_token_id] if self.add_bos_token else []
244
+ eos_token_id = [self.eos_token_id] if self.add_eos_token else []
245
+
246
+ output = [0] * len(bos_token_id + token_ids_0 + eos_token_id)
247
+
248
+ if token_ids_1 is not None:
249
+ output += [1] * len(bos_token_id + token_ids_1 + eos_token_id)
250
+
251
+ return output
vocab/baichuan2/Baichuan2-7B-Chat/tokenizer.model ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:79452955be6b419a65984273a9f08af86042e1c2a75ee3ba989cbf620a133cc2
3
+ size 2001107
vocab/baichuan2/Baichuan2-7B-Chat/tokenizer_config.json ADDED
@@ -0,0 +1,36 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "auto_map": {
3
+ "AutoTokenizer": ["tokenization_baichuan.BaichuanTokenizer", null]
4
+ },
5
+ "add_bos_token": false,
6
+ "add_eos_token": false,
7
+ "use_fast": false,
8
+ "clean_up_tokenization_spaces": false,
9
+ "eos_token": {
10
+ "__type": "AddedToken",
11
+ "content": "</s>",
12
+ "lstrip": false,
13
+ "normalized": true,
14
+ "rstrip": false,
15
+ "single_word": true
16
+ },
17
+ "model_max_length": 4096,
18
+ "sp_model_kwargs": {},
19
+ "tokenizer_class": "BaichuanTokenizer",
20
+ "pad_token": {
21
+ "__type": "AddedToken",
22
+ "content": "<unk>",
23
+ "lstrip": false,
24
+ "normalized": true,
25
+ "rstrip": false,
26
+ "single_word": true
27
+ },
28
+ "unk_token": {
29
+ "__type": "AddedToken",
30
+ "content": "<unk>",
31
+ "lstrip": false,
32
+ "normalized": true,
33
+ "rstrip": false,
34
+ "single_word": true
35
+ }
36
+ }
vocab/baichuan2/__init__.py CHANGED
@@ -1,10 +1,21 @@
 
 
1
  from transformers import AutoTokenizer
2
  from vocab import TokenizerType
3
 
4
- tokenizer = AutoTokenizer.from_pretrained("baichuan-inc/Baichuan2-7B-Chat", trust_remote_code=True)
 
 
 
 
 
 
 
 
 
5
 
6
 
7
  # byte-bpe sentencepiece
8
  tokenizer.type = TokenizerType.ByteBPE
9
 
10
- tokenizer.comments = "expand the vocqbulary size from 64000 in Baichuan1 to 125696"
 
1
+ import os
2
+ import config
3
  from transformers import AutoTokenizer
4
  from vocab import TokenizerType
5
 
6
+
7
+
8
+
9
+
10
+ if config.USE_REMOTE:
11
+ tokenizer = AutoTokenizer.from_pretrained("baichuan-inc/Baichuan2-7B-Chat", trust_remote_code=True)
12
+ else:
13
+ CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
14
+ TOKENIZER_DIR = os.path.join(CURRENT_DIR, "Baichuan2-7B-Chat")
15
+ tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_DIR, trust_remote_code=True)
16
 
17
 
18
  # byte-bpe sentencepiece
19
  tokenizer.type = TokenizerType.ByteBPE
20
 
21
+ tokenizer.comments = "expand the vocabulary size from 64000 in Baichuan1 to 125696"
vocab/baichuan2/bug.py ADDED
@@ -0,0 +1,22 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ """
3
+ 这个 bug 已解决, https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat/commit/e03d54f05b9d42740c43a191c5d2914fcfb4c6e5
4
+ """
5
+
6
+ import os
7
+ from transformers import AutoTokenizer
8
+ from vocab import TokenizerType
9
+
10
+
11
+ CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
12
+ TOKENIZER_DIR = os.path.join(CURRENT_DIR, "Baichuan2-7B-Chat")
13
+ tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_DIR, trust_remote_code=True)
14
+ # tokenizer = AutoTokenizer.from_pretrained("baichuan-inc/Baichuan2-7B-Chat", trust_remote_code=True)
15
+
16
+ token_ids = tokenizer.encode("<pad>")
17
+ # token1 = tokenizer.decode(125696) # out of range
18
+
19
+ token_ids = tokenizer.encode("中")
20
+ filtered_tokens = tokenizer.convert_ids_to_tokens(token_ids)
21
+ decoded_text = tokenizer.convert_tokens_to_string(filtered_tokens)
22
+ print(decoded_text)
vocab/baichuan2/encode.py ADDED
@@ -0,0 +1,15 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ import os
3
+ os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python"
4
+ from sentencepiece import sentencepiece_model_pb2 as sp_pb2_model
5
+
6
+ # sp = sp_pb2_model.ModelProto()
7
+ # # sp.ParseFromString(open("zh_corpus.unigram.model", "rb").read())
8
+ # sp.ParseFromString(open("zh_corpus.bpe.user_defined_symbols.model", "rb").read())
9
+
10
+ import sentencepiece as spm
11
+ sp = spm.SentencePieceProcessor(model_file="Baichuan2-7B-Chat/tokenizer.model")
12
+ for text in ["汉堡王", "汉", "堡", "sfds<|USER|>ss</s><Rhino>", "<reserved_87254>", "<reserved_928>"]:
13
+ result_str = sp.encode(text, out_type=str)
14
+ result_int = sp.encode(text, out_type=int)
15
+ print(result_str, result_int)
vocab/baichuan2/special_token.py ADDED
@@ -0,0 +1,15 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ import os
3
+ os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python"
4
+ from sentencepiece import sentencepiece_model_pb2 as sp_pb2_model
5
+ import sentencepiece as spm
6
+
7
+ baichuan_spm = sp_pb2_model.ModelProto()
8
+ baichuan_spm.ParseFromString(open("Baichuan2-7B-Chat/tokenizer.model", "rb").read())
9
+
10
+
11
+ vocab_size = len(baichuan_spm.pieces)
12
+ for i in range(vocab_size):
13
+ piece = baichuan_spm.pieces[i]
14
+ if "reser" in piece.piece:
15
+ print(i, str(piece).strip().replace("\n", ", "))
vocab/baichuan7b_chat/__init__.py ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ """
2
+ "vocab_size": 125696
3
+ """
4
+
5
+
vocab/build_zh_vocab.py DELETED
@@ -1,32 +0,0 @@
1
- import json
2
- from collections import defaultdict
3
-
4
- all_zh_words = defaultdict(int)
5
- for model_name in [
6
- "gpt2",
7
- "gpt2_chinese",
8
- "chinese_llama_lora_7b",
9
- "bert_chinese",
10
- "moss",
11
- "bloom",
12
- "bloomz_6b4_zh",
13
- "gpt_nexo_20b",
14
- "gpt_neox_chinese_v1",
15
- "glm_chinese",
16
- "chatglm"
17
- ]:
18
- zh_word_set = set()
19
- for line in open(model_name + "_vocab.zh.jsonl", "r", encoding="utf-8"):
20
- item = json.loads(line)
21
- token = item["token"]
22
- if item["type"] in ["中文单字", "中文多字"]:
23
- zh_word_set.add(token.strip())
24
-
25
- for word in zh_word_set:
26
- all_zh_words[word] += 1
27
-
28
- sorted_keywords = sorted(all_zh_words.items(), key=lambda kv: kv[1], reverse=True)
29
-
30
- with open("vocab.freq.zh.txt", "w", encoding="utf-8") as f_out:
31
- for word, count in sorted_keywords:
32
- f_out.write("%s\t%d\n" % (word, count))
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
vocab/chatglm2_6b/__init__.py CHANGED
@@ -1,2 +1,8 @@
 
 
1
  from transformers import AutoTokenizer
 
 
 
 
2
  tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True)
 
1
+
2
+ import os
3
  from transformers import AutoTokenizer
4
+
5
+ os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python"
6
+
7
+
8
  tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True)
vocab/chatglm3_6b/__init__.py ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+
2
+
3
+
4
+ import os
5
+ from transformers import AutoTokenizer
6
+
7
+ os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python"
8
+
9
+ tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm3-6b", trust_remote_code=True)
vocab/chatglm_6b/README.md CHANGED
@@ -7,7 +7,7 @@
7
  - ice_text.model
8
  - 二进制文件
9
  - num_image_tokens = 20000
10
- 词典大小 150528
11
 
12
 
13
  ```
 
7
  - ice_text.model
8
  - 二进制文件
9
  - num_image_tokens = 20000
10
+ 文本词典大小=150528-20000
11
 
12
 
13
  ```
vocab/chatglm_6b/__init__.py CHANGED
@@ -3,15 +3,19 @@
3
  """
4
 
5
  import os
 
6
  from transformers import AutoTokenizer
7
 
8
  os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python"
9
 
10
- CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
11
- TOKENIZER_DIR = os.path.join(CURRENT_DIR, "tokenizer")
12
 
13
- tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_DIR, trust_remote_code=True)
 
 
 
 
 
14
 
 
 
15
 
16
- # vocab_size = len(tokenizer.get_vocab())
17
- # vocab_size = tokenizer.vocab_size
 
3
  """
4
 
5
  import os
6
+ import config
7
  from transformers import AutoTokenizer
8
 
9
  os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python"
10
 
 
 
11
 
12
+ if config.USE_REMOTE:
13
+ pass
14
+ else:
15
+ CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
16
+ TOKENIZER_DIR = os.path.join(CURRENT_DIR, "chatglm_6b")
17
+ tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_DIR, trust_remote_code=True)
18
 
19
+ # https://huggingface.co/THUDM/chatglm-6b/blob/main/tokenization_chatglm.py#L153
20
+ tokenizer.comments = f"num_image_tokens: {tokenizer.sp_tokenizer.num_image_tokens}; num_image_tokens: {tokenizer.sp_tokenizer.num_text_tokens} "
21
 
 
 
vocab/chatglm_6b/{tokenizer → chatglm_6b}/config.json RENAMED
File without changes
vocab/chatglm_6b/{tokenizer → chatglm_6b}/ice_text.model RENAMED
File without changes
vocab/chatglm_6b/{tokenizer → chatglm_6b}/tokenization_chatglm.py RENAMED
File without changes
vocab/chatglm_6b/{tokenizer → chatglm_6b}/tokenizer_config.json RENAMED
File without changes
vocab/chinese_llama2/demo.py ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+
2
+
3
+ from vocab.chinese_llama2 import tokenizer
4
+
5
+ encoding = tokenizer.encode("<s>开始</s>站位符<pad>试试<unk>")
6
+ print(encoding)
vocab/chinese_llama2/load_by_gpt_neox.py ADDED
@@ -0,0 +1,19 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ # HF_tokenizer
3
+ # from tokenizers import Tokenizer
4
+ # tokenizer = Tokenizer.from_file("tokenizer/tokenizer.json")
5
+
6
+
7
+ import sentencepiece as spm
8
+
9
+ text = "nice job 华为手机"
10
+ text = "<s>世界上最高的山是哪座山?</s><pad>" # 29871, 41334, 30528, 30210, 30329, 41894, 31780, 30329, 30882,
11
+ tokenizer = spm.SentencePieceProcessor(model_file="tokenizer/tokenizer.model")
12
+ tokens = tokenizer.encode(text) # [7575, 4982, 29871, 31266, 30573, 30880, 31429]
13
+ print(tokens)
14
+
15
+ from transformers import LlamaTokenizer
16
+ tokenizer = LlamaTokenizer.from_pretrained("tokenizer")
17
+ tokens = tokenizer.encode(text) # [1, 7575, 4982, 29871, 31266, 30573, 30880, 31429]
18
+ print(tokens)
19
+
vocab/chinese_llama2/tokenizer/special_tokens_map.json ADDED
@@ -0,0 +1,24 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "bos_token": {
3
+ "content": "<s>",
4
+ "lstrip": false,
5
+ "normalized": true,
6
+ "rstrip": false,
7
+ "single_word": false
8
+ },
9
+ "eos_token": {
10
+ "content": "</s>",
11
+ "lstrip": false,
12
+ "normalized": true,
13
+ "rstrip": false,
14
+ "single_word": false
15
+ },
16
+ "pad_token": "<pad>",
17
+ "unk_token": {
18
+ "content": "<unk>",
19
+ "lstrip": false,
20
+ "normalized": true,
21
+ "rstrip": false,
22
+ "single_word": false
23
+ }
24
+ }
vocab/chinese_llama2/tokenizer/tokenizer.model ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:a3b8844863b200dfcca971db228e96ce388290dfcf72c15d7a9d2f604bac787c
3
+ size 844403
vocab/chinese_llama2/tokenizer/tokenizer_config.json ADDED
@@ -0,0 +1,35 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "add_bos_token": true,
3
+ "add_eos_token": false,
4
+ "bos_token": {
5
+ "__type": "AddedToken",
6
+ "content": "<s>",
7
+ "lstrip": false,
8
+ "normalized": true,
9
+ "rstrip": false,
10
+ "single_word": false
11
+ },
12
+ "clean_up_tokenization_spaces": false,
13
+ "eos_token": {
14
+ "__type": "AddedToken",
15
+ "content": "</s>",
16
+ "lstrip": false,
17
+ "normalized": true,
18
+ "rstrip": false,
19
+ "single_word": false
20
+ },
21
+ "legacy": true,
22
+ "model_max_length": 1000000000000000019884624838656,
23
+ "pad_token": null,
24
+ "sp_model_kwargs": {},
25
+ "tokenizer_class": "LlamaTokenizer",
26
+ "unk_token": {
27
+ "__type": "AddedToken",
28
+ "content": "<unk>",
29
+ "lstrip": false,
30
+ "normalized": true,
31
+ "rstrip": false,
32
+ "single_word": false
33
+ },
34
+ "use_fast": false
35
+ }
vocab/gpt_35_turbo/README.md ADDED
@@ -0,0 +1,41 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+
3
+ ## token
4
+
5
+
6
+
7
+ space
8
+ ```yml
9
+ # multi-space
10
+ {"id": 881, "token": "\r\n\r\n", "token_decode": "\r\n\r\n", "token_len": 4, "zh_count": 0, "space_count": 4, "digit_count": 0, "zh_symbol_count": 0}
11
+ # space + en
12
+ {"id": 862, "token": "\treturn", "token_decode": "\treturn", "token_len": 7, "zh_count": 0, "space_count": 1, "digit_count": 0, "zh_symbol_count": 0}
13
+ # sapce + zh
14
+ {"id": 40195, "token": " 下", "token_decode": " 下", "token_len": 2, "zh_count": 1, "space_count": 1, "digit_count": 0, "zh_symbol_count": 0}
15
+ ```
16
+
17
+
18
+ special_token
19
+ ```
20
+ {"id": 100257, "token": "<|endoftext|>", "token_decode": "<|endoftext|>", "token_len": 13, "zh_count": 0, "space_count": 0, "digit_count": 0, "zh_symbol_count": 0}
21
+ {"id": 100258, "token": "<|fim_prefix|>", "token_decode": "<|fim_prefix|>", "token_len": 14, "zh_count": 0, "space_count": 0, "digit_count": 0, "zh_symbol_count": 0}
22
+ {"id": 100259, "token": "<|fim_middle|>", "token_decode": "<|fim_middle|>", "token_len": 14, "zh_count": 0, "space_count": 0, "digit_count": 0, "zh_symbol_count": 0}
23
+ {"id": 100260, "token": "<|fim_suffix|>", "token_decode": "<|fim_suffix|>", "token_len": 14, "zh_count": 0, "space_count": 0, "digit_count": 0, "zh_symbol_count": 0}
24
+ {"id": 100276, "token": "<|endofprompt|>", "token_decode": "<|endofprompt|>", "token_len": 15, "zh_count": 0, "space_count": 0, "digit_count": 0, "zh_symbol_count": 0}
25
+ ```
26
+
27
+ ## 词典文件
28
+
29
+
30
+ ```
31
+ IQ== 0
32
+ Ig== 1
33
+ Iw== 2
34
+ JA== 3
35
+ JQ== 4
36
+ Jg== 5
37
+ Jw== 6
38
+ KA== 7
39
+ ```
40
+
41
+ 这是啥玩意?
vocab/gpt_35_turbo/__init__.py CHANGED
@@ -11,16 +11,27 @@ tokenizer.comments = "tiktoken is a fast BPE tokeniser for use with OpenAI's mod
11
  tokenizer.reversible = True # It's reversible and lossless, so you can convert tokens back into the original text
12
 
13
 
14
- def decode(self, tokens, errors="replace"):
15
- # def decode(self, tokens: list[int], errors: str = "replace") -> str:
 
 
 
16
  try:
17
  decode_str = self._core_bpe.decode_bytes(tokens).decode("utf-8", errors=errors)
18
  except:
19
  decode_str = "null"
20
  return decode_str
21
 
22
- def convert_ids_to_tokens(self, tokens):
23
- return tokenizer.decode_tokens_bytes(tokens)
 
 
 
 
 
 
 
 
24
 
25
  def get_vocab(self, token_type="str"):
26
  """Returns vocab as a dict
@@ -31,13 +42,15 @@ def get_vocab(self, token_type="str"):
31
  key_error_list = []
32
  unicode_decode_error_list = []
33
  for i in range(self.vocab_size):
 
 
34
  try:
35
  token_byte = self.convert_ids_to_tokens([i])[0]
36
- token_str = token_byte.decode("utf-8")
 
 
37
  vocab[token_byte] = i
38
- except KeyError: # 16 KeyError, 100256 100261-100275
39
- key_error_list.append(i)
40
- # vocab[f"[KeyError]-{i}"] = i
41
  except UnicodeDecodeError: # 773 UnicodeDecodeError
42
  unicode_decode_error_list.append((i, str(token_byte)))
43
  vocab[token_byte] = i
 
11
  tokenizer.reversible = True # It's reversible and lossless, so you can convert tokens back into the original text
12
 
13
 
14
+ def decode(self, tokens, errors="replace", skip_special_tokens=False):
15
+ """
16
+ 默认的decode,可能会报错,详见 decode_test.py
17
+ skip_special_tokens 是为了兼容 hf_tokenizer
18
+ """
19
  try:
20
  decode_str = self._core_bpe.decode_bytes(tokens).decode("utf-8", errors=errors)
21
  except:
22
  decode_str = "null"
23
  return decode_str
24
 
25
+ def convert_ids_to_tokens(self, tokens, skip_special_tokens=False):
26
+ """
27
+ 为什么没有这个方法?
28
+ """
29
+ try:
30
+ return tokenizer.decode_tokens_bytes(tokens)
31
+ except:
32
+ # 什么要返回None?见zh_util.py
33
+ # 16个空闲id, 100256 100261-100275
34
+ return [None for token in tokens]
35
 
36
  def get_vocab(self, token_type="str"):
37
  """Returns vocab as a dict
 
42
  key_error_list = []
43
  unicode_decode_error_list = []
44
  for i in range(self.vocab_size):
45
+ if i == 100256:
46
+ print(i)
47
  try:
48
  token_byte = self.convert_ids_to_tokens([i])[0]
49
+ if token_byte is None:
50
+ continue
51
+ # token_str = token_byte.decode("utf-8")
52
  vocab[token_byte] = i
53
+
 
 
54
  except UnicodeDecodeError: # 773 UnicodeDecodeError
55
  unicode_decode_error_list.append((i, str(token_byte)))
56
  vocab[token_byte] = i
vocab/gpt_35_turbo/decode_test.py ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+
2
+ from vocab.gpt_35_turbo import tokenizer
3
+
4
+ print(tokenizer.decode([100256]))
5
+
6
+ print(tokenizer.convert_ids_to_tokens([100256]))
vocab/gpt_35_turbo/test_arena.py ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ from vocab.gpt_35_turbo import tokenizer
2
+
3
+
4
+ print(tokenizer.get_vocab())
5
+
6
+
7
+ text = "中"
8
+ token_ids = tokenizer.encode(text)
9
+ decode_str = tokenizer.convert_tokens_to_string(token_ids)
10
+ print(decode_str)
vocab/gpt_35_turbo/{test2.py → test_tiktoken.py} RENAMED
@@ -26,6 +26,7 @@ tokenizer.decode_tokens_bytes([10])
26
  tokenizer.decode_single_token_bytes(10)
27
  tokenizer.decode_bytes([10])
28
 
 
29
  f_out = open("vocab.jsonl", "w")
30
  # 100255
31
  for i in range(tokenizer.n_vocab):
 
26
  tokenizer.decode_single_token_bytes(10)
27
  tokenizer.decode_bytes([10])
28
 
29
+
30
  f_out = open("vocab.jsonl", "w")
31
  # 100255
32
  for i in range(tokenizer.n_vocab):
vocab/gpt_35_turbo/{aaa.py → tiktoken_decode_test.py} RENAMED
@@ -19,6 +19,7 @@ tokenizer = tiktoken.encoding_for_model('gpt-3.5-turbo')
19
 
20
  tokens = [100263, 99834]
21
 
 
22
  tokenizer.decode(tokens)
23
 
24
  tokenizer._core_bpe.decode_bytes(tokens).decode("utf-8", errors="replace")
 
19
 
20
  tokens = [100263, 99834]
21
 
22
+ tokenizer.decode(tokens)
23
  tokenizer.decode(tokens)
24
 
25
  tokenizer._core_bpe.decode_bytes(tokens).decode("utf-8", errors="replace")
vocab/gpt_35_turbo/vocab.jsonl CHANGED
@@ -99964,314 +99964,3 @@
99964
  {"id": 99963, "token": "\" Geg\""}
99965
  {"id": 99964, "token": "\"\\tdto\""}
99966
  {"id": 99965, "token": "\".defaultValue\""}
99967
- {"id": 99966, "token": "\" Kami\""}
99968
- {"id": 99967, "token": "\" ASE\""}
99969
- {"id": 99968, "token": "\"optimized\""}
99970
- {"id": 99969, "token": "\" \\ud3ec\""}
99971
- {"id": 99970, "token": "\" originates\""}
99972
- {"id": 99971, "token": "\"errMsg\""}
99973
- {"id": 99972, "token": "\" espa\\u00e7o\""}
99974
- {"id": 99973, "token": "\"(SYS\""}
99975
- {"id": 99974, "token": "\" McB\""}
99976
- {"id": 99975, "token": "\"dance\""}
99977
- {"id": 99976, "token": "\"_detected\""}
99978
- {"id": 99977, "token": "\" fr\\u00fc\""}
99979
- {"id": 99978, "token": "\"\\t\\t \\t\\t\""}
99980
- {"id": 99979, "token": "\"<Date\""}
99981
- {"id": 99980, "token": "\"(comb\""}
99982
- {"id": 99981, "token": "\" Decide\""}
99983
- {"id": 99982, "token": "\"\\\\Field\""}
99984
- {"id": 99983, "token": "\" Proposed\""}
99985
- {"id": 99984, "token": "\"Rib\""}
99986
- {"id": 99985, "token": "\" dislikes\""}
99987
- {"id": 99986, "token": "\" Wien\""}
99988
- {"id": 99987, "token": "\"\\tDocument\""}
99989
- {"id": 99988, "token": "\" traf\""}
99990
- {"id": 99989, "token": "\" storia\""}
99991
- {"id": 99990, "token": "\" Tells\""}
99992
- {"id": 99991, "token": "\"')==\""}
99993
- {"id": 99992, "token": "\"Cri\""}
99994
- {"id": 99993, "token": "\"(VALUE\""}
99995
- {"id": 99994, "token": "\" Burnett\""}
99996
- {"id": 99995, "token": "\",void\""}
99997
- {"id": 99996, "token": "\" danh\""}
99998
- {"id": 99997, "token": "\" ccp\""}
99999
- {"id": 99998, "token": "\"Blockchain\""}
100000
- {"id": 99999, "token": "\":\\\"-\\\"`\\n\""}
100001
- {"id": 100000, "token": "\"IClient\""}
100002
- {"id": 100001, "token": "\"ISODE\""}
100003
- {"id": 100002, "token": "\"Issuer\""}
100004
- {"id": 100003, "token": "\")}\\r\\n\""}
100005
- {"id": 100004, "token": "\",but\""}
100006
- {"id": 100005, "token": "\" Uph\""}
100007
- {"id": 100006, "token": "\"(Sub\""}
100008
- {"id": 100007, "token": "\" t\\u00e9l\\u00e9phone\""}
100009
- {"id": 100008, "token": "\" onDataChange\""}
100010
- {"id": 100009, "token": "\" marshaller\""}
100011
- {"id": 100010, "token": "\"-analytics\""}
100012
- {"id": 100011, "token": "\",content\""}
100013
- {"id": 100012, "token": "\" debacle\""}
100014
- {"id": 100013, "token": "\"_ValueChanged\""}
100015
- {"id": 100014, "token": "\" fauna\""}
100016
- {"id": 100015, "token": "\" #=>\""}
100017
- {"id": 100016, "token": "\" foyer\""}
100018
- {"id": 100017, "token": "\"'utilisation\""}
100019
- {"id": 100018, "token": "\" M\\u00fcller\""}
100020
- {"id": 100019, "token": "\" Fetish\""}
100021
- {"id": 100020, "token": "\" defaultManager\""}
100022
- {"id": 100021, "token": "\" backtrack\""}
100023
- {"id": 100022, "token": "\"Bah\""}
100024
- {"id": 100023, "token": "\"Explicit\""}
100025
- {"id": 100024, "token": "\"_ASCII\""}
100026
- {"id": 100025, "token": "\" mActivity\""}
100027
- {"id": 100026, "token": "\"(Msg\""}
100028
- {"id": 100027, "token": "\" \\uac8c\""}
100029
- {"id": 100028, "token": "\" TERMS\""}
100030
- {"id": 100029, "token": "\" Angie\""}
100031
- {"id": 100030, "token": "\"HSV\""}
100032
- {"id": 100031, "token": "\" Mosque\""}
100033
- {"id": 100032, "token": "\".Names\""}
100034
- {"id": 100033, "token": "\"\\ud2bc\""}
100035
- {"id": 100034, "token": "\"reste\""}
100036
- {"id": 100035, "token": "\"_parms\""}
100037
- {"id": 100036, "token": "\" gaping\""}
100038
- {"id": 100037, "token": "\" cropping\""}
100039
- {"id": 100038, "token": "\"DataFrame\""}
100040
- {"id": 100039, "token": "\" responsiveness\""}
100041
- {"id": 100040, "token": "\"_undo\""}
100042
- {"id": 100041, "token": "\"_tran\""}
100043
- {"id": 100042, "token": "\".terminate\""}
100044
- {"id": 100043, "token": "\" italiane\""}
100045
- {"id": 100044, "token": "\" walkthrough\""}
100046
- {"id": 100045, "token": "\" attractiveness\""}
100047
- {"id": 100046, "token": "\"\\u0434\\u0435\""}
100048
- {"id": 100047, "token": "\"_STS\""}
100049
- {"id": 100048, "token": "\"_learn\""}
100050
- {"id": 100049, "token": "\" chocolates\""}
100051
- {"id": 100050, "token": "\"ierarchical\""}
100052
- {"id": 100051, "token": "\"-thinking\""}
100053
- {"id": 100052, "token": "\" )))\""}
100054
- {"id": 100053, "token": "\"ishments\""}
100055
- {"id": 100054, "token": "\".Logf\""}
100056
- {"id": 100055, "token": "\" TMZ\""}
100057
- {"id": 100056, "token": "\" Canary\""}
100058
- {"id": 100057, "token": "\"foil\""}
100059
- {"id": 100058, "token": "\" Vaccine\""}
100060
- {"id": 100059, "token": "\".vx\""}
100061
- {"id": 100060, "token": "\" Surround\""}
100062
- {"id": 100061, "token": "\"Intermediate\""}
100063
- {"id": 100062, "token": "\" iov\""}
100064
- {"id": 100063, "token": "\"vais\""}
100065
- {"id": 100064, "token": "\"';\\\";\\n\""}
100066
- {"id": 100065, "token": "\"\\uff5e\\n\\n\""}
100067
- {"id": 100066, "token": "\"\\u9001\\u6599\""}
100068
- {"id": 100067, "token": "\"\\u2026it\""}
100069
- {"id": 100068, "token": "\"Seats\""}
100070
- {"id": 100069, "token": "\"Clar\""}
100071
- {"id": 100070, "token": "\"Wars\""}
100072
- {"id": 100071, "token": "\" Hutchinson\""}
100073
- {"id": 100072, "token": "\" Hasan\""}
100074
- {"id": 100073, "token": "\"!')\\n\\n\""}
100075
- {"id": 100074, "token": "\" Richie\""}
100076
- {"id": 100075, "token": "\"cheiden\""}
100077
- {"id": 100076, "token": "\"($('\""}
100078
- {"id": 100077, "token": "\"York\""}
100079
- {"id": 100078, "token": "\" lids\""}
100080
- {"id": 100079, "token": "\" alphanumeric\""}
100081
- {"id": 100080, "token": "\" Glock\""}
100082
- {"id": 100081, "token": "\".shapes\""}
100083
- {"id": 100082, "token": "\" sparking\""}
100084
- {"id": 100083, "token": "\"_epsilon\""}
100085
- {"id": 100084, "token": "\"uplicated\""}
100086
- {"id": 100085, "token": "\".dirty\""}
100087
- {"id": 100086, "token": "\"])==\""}
100088
- {"id": 100087, "token": "\" \\uc704\\uce58\""}
100089
- {"id": 100088, "token": "\" scn\""}
100090
- {"id": 100089, "token": "\" /****************************************************************\""}
100091
- {"id": 100090, "token": "\"_PREVIEW\""}
100092
- {"id": 100091, "token": "\"_HC\""}
100093
- {"id": 100092, "token": "\"ielding\""}
100094
- {"id": 100093, "token": "\"fgets\""}
100095
- {"id": 100094, "token": "\" Addison\""}
100096
- {"id": 100095, "token": "\" productService\""}
100097
- {"id": 100096, "token": "\"-figure\""}
100098
- {"id": 100097, "token": "\"(retval\""}
100099
- {"id": 100098, "token": "\"zano\""}
100100
- {"id": 100099, "token": "\" autob\""}
100101
- {"id": 100100, "token": "\"\\tsd\""}
100102
- {"id": 100101, "token": "\"_numer\""}
100103
- {"id": 100102, "token": "\" SetLastError\""}
100104
- {"id": 100103, "token": "\" Fior\""}
100105
- {"id": 100104, "token": "\"ificance\""}
100106
- {"id": 100105, "token": "\"Untitled\""}
100107
- {"id": 100106, "token": "\" infield\""}
100108
- {"id": 100107, "token": "\" {}));\\n\""}
100109
- {"id": 100108, "token": "\" spac\""}
100110
- {"id": 100109, "token": "\" rookies\""}
100111
- {"id": 100110, "token": "\"(describing\""}
100112
- {"id": 100111, "token": "\"ngen\""}
100113
- {"id": 100112, "token": "\"\\u0bbf\\ufffd\""}
100114
- {"id": 100113, "token": "\".rdf\""}
100115
- {"id": 100114, "token": "\".Mutex\""}
100116
- {"id": 100115, "token": "\" kneeling\""}
100117
- {"id": 100116, "token": "\" QE\""}
100118
- {"id": 100117, "token": "\"setMax\""}
100119
- {"id": 100118, "token": "\"ReadStream\""}
100120
- {"id": 100119, "token": "\" ventas\""}
100121
- {"id": 100120, "token": "\"sut\""}
100122
- {"id": 100121, "token": "\"cmpeq\""}
100123
- {"id": 100122, "token": "\".WriteAllText\""}
100124
- {"id": 100123, "token": "\" Experienced\""}
100125
- {"id": 100124, "token": "\"$__\""}
100126
- {"id": 100125, "token": "\" kaum\""}
100127
- {"id": 100126, "token": "\" LIS\""}
100128
- {"id": 100127, "token": "\" documentos\""}
100129
- {"id": 100128, "token": "\"_HEALTH\""}
100130
- {"id": 100129, "token": "\"icontains\""}
100131
- {"id": 100130, "token": "\" artisans\""}
100132
- {"id": 100131, "token": "\"OWNER\""}
100133
- {"id": 100132, "token": "\" blinked\""}
100134
- {"id": 100133, "token": "\"getDisplay\""}
100135
- {"id": 100134, "token": "\" toen\""}
100136
- {"id": 100135, "token": "\" rowNum\""}
100137
- {"id": 100136, "token": "\" avril\""}
100138
- {"id": 100137, "token": "\" invis\""}
100139
- {"id": 100138, "token": "\" Kear\""}
100140
- {"id": 100139, "token": "\"toBeInTheDocument\""}
100141
- {"id": 100140, "token": "\"apur\""}
100142
- {"id": 100141, "token": "\" racked\""}
100143
- {"id": 100142, "token": "\" McMaster\""}
100144
- {"id": 100143, "token": "\"_ATTRIB\""}
100145
- {"id": 100144, "token": "\"Haz\""}
100146
- {"id": 100145, "token": "\" factura\""}
100147
- {"id": 100146, "token": "\"/ts\""}
100148
- {"id": 100147, "token": "\" \\u0440\\u0430\\u0437\\u043c\\u0435\\u0440\""}
100149
- {"id": 100148, "token": "\" zf\""}
100150
- {"id": 100149, "token": "\" shortfall\""}
100151
- {"id": 100150, "token": "\".fasta\""}
100152
- {"id": 100151, "token": "\" CONSTANT\""}
100153
- {"id": 100152, "token": "\".managed\""}
100154
- {"id": 100153, "token": "\"gems\""}
100155
- {"id": 100154, "token": "\"SharedPointer\""}
100156
- {"id": 100155, "token": "\" blurry\""}
100157
- {"id": 100156, "token": "\"brightness\""}
100158
- {"id": 100157, "token": "\"(components\""}
100159
- {"id": 100158, "token": "\" ...\\\"\\n\\n\""}
100160
- {"id": 100159, "token": "\"SELL\""}
100161
- {"id": 100160, "token": "\" Illustrator\""}
100162
- {"id": 100161, "token": "\".getChannel\""}
100163
- {"id": 100162, "token": "\" trouv\\u00e9\""}
100164
- {"id": 100163, "token": "\"ysters\""}
100165
- {"id": 100164, "token": "\" vois\""}
100166
- {"id": 100165, "token": "\" Linden\""}
100167
- {"id": 100166, "token": "\" emojis\""}
100168
- {"id": 100167, "token": "\" brawl\""}
100169
- {"id": 100168, "token": "\" MSR\""}
100170
- {"id": 100169, "token": "\" Elo\""}
100171
- {"id": 100170, "token": "\" Croatian\""}
100172
- {"id": 100171, "token": "\"PopupMenu\""}
100173
- {"id": 100172, "token": "\"Lewis\""}
100174
- {"id": 100173, "token": "\".JWT\""}
100175
- {"id": 100174, "token": "\" astonished\""}
100176
- {"id": 100175, "token": "\"Bush\""}
100177
- {"id": 100176, "token": "\"(itemId\""}
100178
- {"id": 100177, "token": "\" detachment\""}
100179
- {"id": 100178, "token": "\" Encore\""}
100180
- {"id": 100179, "token": "\"\\u5c14\""}
100181
- {"id": 100180, "token": "\" rekl\""}
100182
- {"id": 100181, "token": "\" cram\""}
100183
- {"id": 100182, "token": "\")$/\""}
100184
- {"id": 100183, "token": "\".getHost\""}
100185
- {"id": 100184, "token": "\"_recommend\""}
100186
- {"id": 100185, "token": "\"-HT\""}
100187
- {"id": 100186, "token": "\"_calibration\""}
100188
- {"id": 100187, "token": "\"Authenticate\""}
100189
- {"id": 100188, "token": "\".firebaseapp\""}
100190
- {"id": 100189, "token": "\"UNIX\""}
100191
- {"id": 100190, "token": "\"\\tCamera\""}
100192
- {"id": 100191, "token": "\" HEAP\""}
100193
- {"id": 100192, "token": "\"Ideal\""}
100194
- {"id": 100193, "token": "\".office\""}
100195
- {"id": 100194, "token": "\" goofy\""}
100196
- {"id": 100195, "token": "\"(Symbol\""}
100197
- {"id": 100196, "token": "\" jouer\""}
100198
- {"id": 100197, "token": "\"_partitions\""}
100199
- {"id": 100198, "token": "\" rapidement\""}
100200
- {"id": 100199, "token": "\" GNUNET\""}
100201
- {"id": 100200, "token": "\"idUser\""}
100202
- {"id": 100201, "token": "\" supervise\""}
100203
- {"id": 100202, "token": "\"(Contact\""}
100204
- {"id": 100203, "token": "\"AWN\""}
100205
- {"id": 100204, "token": "\"\\u3058\""}
100206
- {"id": 100205, "token": "\" naam\""}
100207
- {"id": 100206, "token": "\" aust\""}
100208
- {"id": 100207, "token": "\"\\u5728\\u7ebf\""}
100209
- {"id": 100208, "token": "\"_softmax\""}
100210
- {"id": 100209, "token": "\"AllowAnonymous\""}
100211
- {"id": 100210, "token": "\"ammable\""}
100212
- {"id": 100211, "token": "\"ROUTE\""}
100213
- {"id": 100212, "token": "\"*D\""}
100214
- {"id": 100213, "token": "\" aden\""}
100215
- {"id": 100214, "token": "\" Cristina\""}
100216
- {"id": 100215, "token": "\" Cristiano\""}
100217
- {"id": 100216, "token": "\" bloodstream\""}
100218
- {"id": 100217, "token": "\"subclass\""}
100219
- {"id": 100218, "token": "\"_persona\""}
100220
- {"id": 100219, "token": "\"CHILD\""}
100221
- {"id": 100220, "token": "\"-know\""}
100222
- {"id": 100221, "token": "\" navigationOptions\""}
100223
- {"id": 100222, "token": "\" Zukunft\""}
100224
- {"id": 100223, "token": "\" Pixar\""}
100225
- {"id": 100224, "token": "\"Tyler\""}
100226
- {"id": 100225, "token": "\" underworld\""}
100227
- {"id": 100226, "token": "\" sincerity\""}
100228
- {"id": 100227, "token": "\" dispenser\""}
100229
- {"id": 100228, "token": "\" kter\""}
100230
- {"id": 100229, "token": "\"idders\""}
100231
- {"id": 100230, "token": "\".addNode\""}
100232
- {"id": 100231, "token": "\"-checked\""}
100233
- {"id": 100232, "token": "\" keyst\""}
100234
- {"id": 100233, "token": "\" WTO\""}
100235
- {"id": 100234, "token": "\".signals\""}
100236
- {"id": 100235, "token": "\" adventurer\""}
100237
- {"id": 100236, "token": "\" Pang\""}
100238
- {"id": 100237, "token": "\"\\\\R\""}
100239
- {"id": 100238, "token": "\"=pos\""}
100240
- {"id": 100239, "token": "\" dispensaries\""}
100241
- {"id": 100240, "token": "\" Closet\""}
100242
- {"id": 100241, "token": "\"(\\\"{\\\\\\\"\""}
100243
- {"id": 100242, "token": "\"ideon\""}
100244
- {"id": 100243, "token": "\" n\\u00e9cessaire\""}
100245
- {"id": 100244, "token": "\"()\\\"\\n\""}
100246
- {"id": 100245, "token": "\"_RECEIVED\""}
100247
- {"id": 100246, "token": "\" r\\u00e9sultats\""}
100248
- {"id": 100247, "token": "\" moden\""}
100249
- {"id": 100248, "token": "\" Icelandic\""}
100250
- {"id": 100249, "token": "\";d\""}
100251
- {"id": 100250, "token": "\".allowed\""}
100252
- {"id": 100251, "token": "\"(newUser\""}
100253
- {"id": 100252, "token": "\" merciless\""}
100254
- {"id": 100253, "token": "\".WaitFor\""}
100255
- {"id": 100254, "token": "\" daycare\""}
100256
- {"id": 100255, "token": "\" Conveyor\""}
100257
- {"id": 100256, "token": "null"}
100258
- {"id": 100257, "token": "\"<|endoftext|>\""}
100259
- {"id": 100258, "token": "\"<|fim_prefix|>\""}
100260
- {"id": 100259, "token": "\"<|fim_middle|>\""}
100261
- {"id": 100260, "token": "\"<|fim_suffix|>\""}
100262
- {"id": 100261, "token": "null"}
100263
- {"id": 100262, "token": "null"}
100264
- {"id": 100263, "token": "null"}
100265
- {"id": 100264, "token": "null"}
100266
- {"id": 100265, "token": "null"}
100267
- {"id": 100266, "token": "null"}
100268
- {"id": 100267, "token": "null"}
100269
- {"id": 100268, "token": "null"}
100270
- {"id": 100269, "token": "null"}
100271
- {"id": 100270, "token": "null"}
100272
- {"id": 100271, "token": "null"}
100273
- {"id": 100272, "token": "null"}
100274
- {"id": 100273, "token": "null"}
100275
- {"id": 100274, "token": "null"}
100276
- {"id": 100275, "token": "null"}
100277
- {"id": 100276, "token": "\"<|endofprompt|>\""}
 
99964
  {"id": 99963, "token": "\" Geg\""}
99965
  {"id": 99964, "token": "\"\\tdto\""}
99966
  {"id": 99965, "token": "\".defaultValue\""}
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
vocab/internlm_chat_7b/__init__.py CHANGED
@@ -3,4 +3,5 @@ https://huggingface.co/internlm/internlm-chat-7b
3
  """
4
 
5
  from transformers import AutoTokenizer
 
6
  tokenizer = AutoTokenizer.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True)
 
3
  """
4
 
5
  from transformers import AutoTokenizer
6
+
7
  tokenizer = AutoTokenizer.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True)