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KoichiYasuoka
/
Xunzi-Qwen2-1.5B-upos

Token Classification
Transformers
PyTorch
Literary Chinese
qwen2
classical chinese
literary chinese
ancient chinese
pos
dependency-parsing
text-generation-inference
Inference Endpoints
Model card Files Community
KoichiYasuoka commited on
Commit
28048e3
•
1 Parent(s): 5a1ab7d

initial release

Browse files
Files changed (13) hide show
  1. README.md +31 -0
  2. config.json +374 -0
  3. maker.sh +105 -0
  4. mergeout.sh +3 -0
  5. merges.txt +0 -0
  6. oldmerges.txt +0 -0
  7. pytorch_model-00001-of-00002.bin +3 -0
  8. pytorch_model-00002-of-00002.bin +3 -0
  9. pytorch_model.bin.index.json +347 -0
  10. special_tokens_map.json +20 -0
  11. tokenizer_config.json +43 -0
  12. upos.py +41 -0
  13. vocab.json +0 -0
README.md ADDED
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+ ---
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+ language:
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+ - "lzh"
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+ tags:
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+ - "classical chinese"
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+ - "literary chinese"
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+ - "ancient chinese"
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+ - "token-classification"
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+ - "pos"
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+ - "dependency-parsing"
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+ datasets:
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+ - "universal_dependencies"
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+ license: "apache-2.0"
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+ pipeline_tag: "token-classification"
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+ widget:
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+ - text: "子曰學而時習之不亦説乎有朋自遠方來不亦樂乎人不知而不慍不亦君子乎"
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+ ---
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+
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+ # Xunzi-Qwen2-1.5B-upos
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+
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+ ## Model Description
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+
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+ This is a Qwen1.5 model pre-trained on Classical Chinese texts for POS-tagging, derived from [Xunzi-Qwen2-1.5B](https://www.modelscope.cn/models/Xunzillm4cc/Xunzi-Qwen2-1.5B). Every word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universal Part-Of-Speech) and [FEATS](https://universaldependencies.org/u/feat/).
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+
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+ ## How to Use
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+
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+ ```py
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+ from transformers import pipeline
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+ nlp=pipeline("upos","KoichiYasuoka/Xunzi-Qwen2-1.5B-upos",trust_remote_code=True,aggregation_strategy="simple")
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+ print(nlp("不入虎穴不得虎子"))
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+ ```
config.json ADDED
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+ {
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+ "architectures": [
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+ "Qwen2ForTokenClassification"
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+ ],
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+ "attention_dropout": 0.0,
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+ "bos_token_id": 151643,
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+ "custom_pipelines": {
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+ "upos": {
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+ "impl": "upos.BellmanFordTokenClassificationPipeline",
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+ "pt": "AutoModelForTokenClassification"
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+ }
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+ },
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+ "eos_token_id": 151643,
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+ "hidden_act": "silu",
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+ "hidden_size": 1536,
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+ "id2label": {
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+ "0": "ADP",
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+ "1": "ADP|Degree=Equ",
19
+ "2": "ADV",
20
+ "3": "ADV|AdvType=Cau",
21
+ "4": "ADV|AdvType=Deg|Degree=Cmp",
22
+ "5": "ADV|AdvType=Deg|Degree=Pos",
23
+ "6": "ADV|AdvType=Deg|Degree=Sup",
24
+ "7": "ADV|AdvType=Tim",
25
+ "8": "ADV|AdvType=Tim|Aspect=Perf",
26
+ "9": "ADV|AdvType=Tim|Tense=Fut",
27
+ "10": "ADV|AdvType=Tim|Tense=Past",
28
+ "11": "ADV|AdvType=Tim|Tense=Pres",
29
+ "12": "ADV|Degree=Equ|VerbForm=Conv",
30
+ "13": "ADV|Degree=Pos|VerbForm=Conv",
31
+ "14": "ADV|Polarity=Neg",
32
+ "15": "ADV|Polarity=Neg|VerbForm=Conv",
33
+ "16": "ADV|VerbForm=Conv",
34
+ "17": "AUX|Mood=Des",
35
+ "18": "AUX|Mood=Nec",
36
+ "19": "AUX|Mood=Pot",
37
+ "20": "AUX|VerbType=Cop",
38
+ "21": "AUX|Voice=Pass",
39
+ "22": "B-ADP",
40
+ "23": "B-ADP|Degree=Equ",
41
+ "24": "B-ADV",
42
+ "25": "B-ADV|AdvType=Cau",
43
+ "26": "B-ADV|AdvType=Deg|Degree=Cmp",
44
+ "27": "B-ADV|AdvType=Deg|Degree=Pos",
45
+ "28": "B-ADV|AdvType=Deg|Degree=Sup",
46
+ "29": "B-ADV|AdvType=Tim",
47
+ "30": "B-ADV|AdvType=Tim|Aspect=Perf",
48
+ "31": "B-ADV|AdvType=Tim|Tense=Fut",
49
+ "32": "B-ADV|AdvType=Tim|Tense=Past",
50
+ "33": "B-ADV|AdvType=Tim|Tense=Pres",
51
+ "34": "B-ADV|Degree=Equ|VerbForm=Conv",
52
+ "35": "B-ADV|Degree=Pos|VerbForm=Conv",
53
+ "36": "B-ADV|Polarity=Neg",
54
+ "37": "B-ADV|Polarity=Neg|VerbForm=Conv",
55
+ "38": "B-ADV|VerbForm=Conv",
56
+ "39": "B-AUX|Mood=Des",
57
+ "40": "B-AUX|Mood=Nec",
58
+ "41": "B-AUX|Mood=Pot",
59
+ "42": "B-AUX|VerbType=Cop",
60
+ "43": "B-AUX|Voice=Pass",
61
+ "44": "B-CCONJ",
62
+ "45": "B-INTJ",
63
+ "46": "B-NOUN",
64
+ "47": "B-NOUN|Case=Loc",
65
+ "48": "B-NOUN|Case=Tem",
66
+ "49": "B-NOUN|Degree=Pos",
67
+ "50": "B-NOUN|NounType=Clf",
68
+ "51": "B-NUM",
69
+ "52": "B-NUM|NumType=Ord",
70
+ "53": "B-PART",
71
+ "54": "B-PRON|Person=1|PronType=Prs",
72
+ "55": "B-PRON|Person=2|PronType=Prs",
73
+ "56": "B-PRON|Person=3|PronType=Prs",
74
+ "57": "B-PRON|PronType=Dem",
75
+ "58": "B-PRON|PronType=Int",
76
+ "59": "B-PRON|PronType=Prs",
77
+ "60": "B-PRON|PronType=Prs|Reflex=Yes",
78
+ "61": "B-PROPN",
79
+ "62": "B-PROPN|Case=Loc|NameType=Geo",
80
+ "63": "B-PROPN|Case=Loc|NameType=Nat",
81
+ "64": "B-PROPN|NameType=Giv",
82
+ "65": "B-PROPN|NameType=Prs",
83
+ "66": "B-PROPN|NameType=Sur",
84
+ "67": "B-PUNCT",
85
+ "68": "B-SCONJ",
86
+ "69": "B-SYM",
87
+ "70": "B-VERB",
88
+ "71": "B-VERB|Degree=Equ",
89
+ "72": "B-VERB|Degree=Equ|VerbForm=Part",
90
+ "73": "B-VERB|Degree=Pos",
91
+ "74": "B-VERB|Degree=Pos|VerbForm=Part",
92
+ "75": "B-VERB|Polarity=Neg",
93
+ "76": "B-VERB|Polarity=Neg|VerbForm=Part",
94
+ "77": "B-VERB|VerbForm=Part",
95
+ "78": "CCONJ",
96
+ "79": "I-ADP",
97
+ "80": "I-ADP|Degree=Equ",
98
+ "81": "I-ADV",
99
+ "82": "I-ADV|AdvType=Cau",
100
+ "83": "I-ADV|AdvType=Deg|Degree=Cmp",
101
+ "84": "I-ADV|AdvType=Deg|Degree=Pos",
102
+ "85": "I-ADV|AdvType=Deg|Degree=Sup",
103
+ "86": "I-ADV|AdvType=Tim",
104
+ "87": "I-ADV|AdvType=Tim|Aspect=Perf",
105
+ "88": "I-ADV|AdvType=Tim|Tense=Fut",
106
+ "89": "I-ADV|AdvType=Tim|Tense=Past",
107
+ "90": "I-ADV|AdvType=Tim|Tense=Pres",
108
+ "91": "I-ADV|Degree=Equ|VerbForm=Conv",
109
+ "92": "I-ADV|Degree=Pos|VerbForm=Conv",
110
+ "93": "I-ADV|Polarity=Neg",
111
+ "94": "I-ADV|Polarity=Neg|VerbForm=Conv",
112
+ "95": "I-ADV|VerbForm=Conv",
113
+ "96": "I-AUX|Mood=Des",
114
+ "97": "I-AUX|Mood=Nec",
115
+ "98": "I-AUX|Mood=Pot",
116
+ "99": "I-AUX|VerbType=Cop",
117
+ "100": "I-AUX|Voice=Pass",
118
+ "101": "I-CCONJ",
119
+ "102": "I-INTJ",
120
+ "103": "I-NOUN",
121
+ "104": "I-NOUN|Case=Loc",
122
+ "105": "I-NOUN|Case=Tem",
123
+ "106": "I-NOUN|Degree=Pos",
124
+ "107": "I-NOUN|NounType=Clf",
125
+ "108": "I-NUM",
126
+ "109": "I-NUM|NumType=Ord",
127
+ "110": "I-PART",
128
+ "111": "I-PRON|Person=1|PronType=Prs",
129
+ "112": "I-PRON|Person=2|PronType=Prs",
130
+ "113": "I-PRON|Person=3|PronType=Prs",
131
+ "114": "I-PRON|PronType=Dem",
132
+ "115": "I-PRON|PronType=Int",
133
+ "116": "I-PRON|PronType=Prs",
134
+ "117": "I-PRON|PronType=Prs|Reflex=Yes",
135
+ "118": "I-PROPN",
136
+ "119": "I-PROPN|Case=Loc|NameType=Geo",
137
+ "120": "I-PROPN|Case=Loc|NameType=Nat",
138
+ "121": "I-PROPN|NameType=Giv",
139
+ "122": "I-PROPN|NameType=Prs",
140
+ "123": "I-PROPN|NameType=Sur",
141
+ "124": "I-PUNCT",
142
+ "125": "I-SCONJ",
143
+ "126": "I-SYM",
144
+ "127": "I-VERB",
145
+ "128": "I-VERB|Degree=Equ",
146
+ "129": "I-VERB|Degree=Equ|VerbForm=Part",
147
+ "130": "I-VERB|Degree=Pos",
148
+ "131": "I-VERB|Degree=Pos|VerbForm=Part",
149
+ "132": "I-VERB|Polarity=Neg",
150
+ "133": "I-VERB|Polarity=Neg|VerbForm=Part",
151
+ "134": "I-VERB|VerbForm=Part",
152
+ "135": "INTJ",
153
+ "136": "NOUN",
154
+ "137": "NOUN|Case=Loc",
155
+ "138": "NOUN|Case=Tem",
156
+ "139": "NOUN|Degree=Pos",
157
+ "140": "NOUN|NounType=Clf",
158
+ "141": "NUM",
159
+ "142": "NUM|NumType=Ord",
160
+ "143": "PART",
161
+ "144": "PRON|Person=1|PronType=Prs",
162
+ "145": "PRON|Person=2|PronType=Prs",
163
+ "146": "PRON|Person=3|PronType=Prs",
164
+ "147": "PRON|PronType=Dem",
165
+ "148": "PRON|PronType=Int",
166
+ "149": "PRON|PronType=Prs",
167
+ "150": "PRON|PronType=Prs|Reflex=Yes",
168
+ "151": "PROPN",
169
+ "152": "PROPN|Case=Loc|NameType=Geo",
170
+ "153": "PROPN|Case=Loc|NameType=Nat",
171
+ "154": "PROPN|NameType=Giv",
172
+ "155": "PROPN|NameType=Prs",
173
+ "156": "PROPN|NameType=Sur",
174
+ "157": "PUNCT",
175
+ "158": "SCONJ",
176
+ "159": "SYM",
177
+ "160": "VERB",
178
+ "161": "VERB|Degree=Equ",
179
+ "162": "VERB|Degree=Equ|VerbForm=Part",
180
+ "163": "VERB|Degree=Pos",
181
+ "164": "VERB|Degree=Pos|VerbForm=Part",
182
+ "165": "VERB|Polarity=Neg",
183
+ "166": "VERB|Polarity=Neg|VerbForm=Part",
184
+ "167": "VERB|VerbForm=Part"
185
+ },
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+ "initializer_range": 0.02,
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+ "intermediate_size": 8960,
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+ "label2id": {
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+ "ADP": 0,
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+ "ADP|Degree=Equ": 1,
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+ "ADV": 2,
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+ "ADV|AdvType=Cau": 3,
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+ "ADV|AdvType=Deg|Degree=Cmp": 4,
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+ "ADV|AdvType=Deg|Degree=Pos": 5,
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+ "ADV|AdvType=Deg|Degree=Sup": 6,
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+ "ADV|AdvType=Tim": 7,
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+ "ADV|AdvType=Tim|Aspect=Perf": 8,
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+ "ADV|AdvType=Tim|Tense=Fut": 9,
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+ "ADV|AdvType=Tim|Tense=Past": 10,
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+ "ADV|AdvType=Tim|Tense=Pres": 11,
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+ "ADV|Degree=Equ|VerbForm=Conv": 12,
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+ "ADV|Degree=Pos|VerbForm=Conv": 13,
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+ "ADV|Polarity=Neg": 14,
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+ "ADV|Polarity=Neg|VerbForm=Conv": 15,
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+ "ADV|VerbForm=Conv": 16,
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+ "AUX|Mood=Des": 17,
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+ "AUX|Mood=Nec": 18,
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+ "AUX|Mood=Pot": 19,
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+ "AUX|VerbType=Cop": 20,
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+ "AUX|Voice=Pass": 21,
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+ "B-ADP": 22,
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+ "B-ADP|Degree=Equ": 23,
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+ "B-ADV": 24,
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+ "B-ADV|AdvType=Cau": 25,
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+ "B-ADV|AdvType=Deg|Degree=Cmp": 26,
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+ "B-ADV|AdvType=Deg|Degree=Pos": 27,
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+ "B-ADV|AdvType=Deg|Degree=Sup": 28,
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+ "B-ADV|AdvType=Tim": 29,
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+ "B-ADV|AdvType=Tim|Aspect=Perf": 30,
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+ "B-ADV|AdvType=Tim|Tense=Fut": 31,
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+ "B-ADV|AdvType=Tim|Tense=Past": 32,
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+ "B-ADV|AdvType=Tim|Tense=Pres": 33,
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+ "B-ADV|Degree=Equ|VerbForm=Conv": 34,
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+ "B-ADV|Degree=Pos|VerbForm=Conv": 35,
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+ "B-ADV|Polarity=Neg": 36,
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+ "B-ADV|Polarity=Neg|VerbForm=Conv": 37,
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+ "B-ADV|VerbForm=Conv": 38,
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+ "B-AUX|Mood=Des": 39,
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+ "B-AUX|Mood=Nec": 40,
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+ "B-AUX|Mood=Pot": 41,
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+ "B-AUX|VerbType=Cop": 42,
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+ "B-AUX|Voice=Pass": 43,
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+ "B-CCONJ": 44,
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+ "B-INTJ": 45,
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+ "B-NOUN": 46,
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+ "B-NOUN|Case=Loc": 47,
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+ "B-NOUN|Case=Tem": 48,
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+ "B-NOUN|Degree=Pos": 49,
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+ "B-NOUN|NounType=Clf": 50,
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+ "B-NUM": 51,
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+ "B-NUM|NumType=Ord": 52,
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+ "B-PART": 53,
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+ "B-PRON|Person=1|PronType=Prs": 54,
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+ "B-PRON|Person=2|PronType=Prs": 55,
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+ "B-PRON|Person=3|PronType=Prs": 56,
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+ "B-PRON|PronType=Dem": 57,
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+ "B-PRON|PronType=Int": 58,
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+ "B-PRON|PronType=Prs": 59,
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+ "B-PRON|PronType=Prs|Reflex=Yes": 60,
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+ "B-PROPN": 61,
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+ "B-PROPN|Case=Loc|NameType=Geo": 62,
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+ "B-PROPN|Case=Loc|NameType=Nat": 63,
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+ "B-PROPN|NameType=Giv": 64,
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+ "B-PROPN|NameType=Prs": 65,
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+ "B-PROPN|NameType=Sur": 66,
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+ "B-PUNCT": 67,
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+ "B-SCONJ": 68,
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+ "B-SYM": 69,
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+ "B-VERB": 70,
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+ "B-VERB|Degree=Equ": 71,
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+ "B-VERB|Degree=Equ|VerbForm=Part": 72,
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+ "B-VERB|Degree=Pos": 73,
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+ "B-VERB|Degree=Pos|VerbForm=Part": 74,
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+ "B-VERB|Polarity=Neg": 75,
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+ "B-VERB|Polarity=Neg|VerbForm=Part": 76,
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+ "B-VERB|VerbForm=Part": 77,
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+ "CCONJ": 78,
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+ "I-ADP": 79,
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+ "I-ADP|Degree=Equ": 80,
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+ "I-ADV": 81,
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+ "I-ADV|AdvType=Cau": 82,
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+ "I-ADV|AdvType=Deg|Degree=Cmp": 83,
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+ "I-ADV|AdvType=Deg|Degree=Pos": 84,
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+ "I-ADV|AdvType=Deg|Degree=Sup": 85,
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+ "I-ADV|AdvType=Tim": 86,
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+ "I-ADV|AdvType=Tim|Aspect=Perf": 87,
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+ "I-ADV|AdvType=Tim|Tense=Fut": 88,
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+ "I-ADV|AdvType=Tim|Tense=Past": 89,
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+ "I-ADV|AdvType=Tim|Tense=Pres": 90,
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+ "I-ADV|Degree=Equ|VerbForm=Conv": 91,
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+ "I-ADV|Degree=Pos|VerbForm=Conv": 92,
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+ "I-ADV|Polarity=Neg": 93,
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+ "I-ADV|Polarity=Neg|VerbForm=Conv": 94,
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+ "I-ADV|VerbForm=Conv": 95,
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+ "I-AUX|Mood=Des": 96,
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+ "I-AUX|Mood=Nec": 97,
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+ "I-AUX|Mood=Pot": 98,
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+ "I-AUX|VerbType=Cop": 99,
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+ "I-AUX|Voice=Pass": 100,
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+ "I-CCONJ": 101,
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+ "I-INTJ": 102,
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+ "I-NOUN": 103,
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+ "I-NOUN|Case=Loc": 104,
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+ "I-NOUN|Case=Tem": 105,
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+ "I-NOUN|Degree=Pos": 106,
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+ "I-NOUN|NounType=Clf": 107,
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+ "I-NUM": 108,
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+ "I-NUM|NumType=Ord": 109,
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+ "I-PART": 110,
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+ "I-PRON|Person=1|PronType=Prs": 111,
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+ "I-PRON|Person=2|PronType=Prs": 112,
302
+ "I-PRON|Person=3|PronType=Prs": 113,
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+ "I-PRON|PronType=Dem": 114,
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+ "I-PRON|PronType=Int": 115,
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+ "I-PRON|PronType=Prs": 116,
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+ "I-PRON|PronType=Prs|Reflex=Yes": 117,
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+ "I-PROPN": 118,
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+ "I-PROPN|Case=Loc|NameType=Geo": 119,
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+ "I-PROPN|Case=Loc|NameType=Nat": 120,
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+ "I-PROPN|NameType=Giv": 121,
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+ "I-PROPN|NameType=Prs": 122,
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+ "I-PROPN|NameType=Sur": 123,
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+ "I-PUNCT": 124,
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+ "I-SCONJ": 125,
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+ "I-SYM": 126,
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+ "I-VERB": 127,
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+ "I-VERB|Degree=Equ": 128,
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+ "I-VERB|Degree=Equ|VerbForm=Part": 129,
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+ "I-VERB|Degree=Pos": 130,
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+ "I-VERB|Degree=Pos|VerbForm=Part": 131,
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+ "I-VERB|Polarity=Neg": 132,
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+ "I-VERB|Polarity=Neg|VerbForm=Part": 133,
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+ "I-VERB|VerbForm=Part": 134,
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+ "INTJ": 135,
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+ "NOUN": 136,
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+ "NOUN|Case=Loc": 137,
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+ "NOUN|Case=Tem": 138,
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+ "NOUN|Degree=Pos": 139,
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+ "NOUN|NounType=Clf": 140,
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+ "NUM": 141,
331
+ "NUM|NumType=Ord": 142,
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+ "PART": 143,
333
+ "PRON|Person=1|PronType=Prs": 144,
334
+ "PRON|Person=2|PronType=Prs": 145,
335
+ "PRON|Person=3|PronType=Prs": 146,
336
+ "PRON|PronType=Dem": 147,
337
+ "PRON|PronType=Int": 148,
338
+ "PRON|PronType=Prs": 149,
339
+ "PRON|PronType=Prs|Reflex=Yes": 150,
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+ "PROPN": 151,
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+ "PROPN|Case=Loc|NameType=Geo": 152,
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+ "PROPN|Case=Loc|NameType=Nat": 153,
343
+ "PROPN|NameType=Giv": 154,
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+ "PROPN|NameType=Prs": 155,
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+ "PROPN|NameType=Sur": 156,
346
+ "PUNCT": 157,
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+ "SCONJ": 158,
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+ "SYM": 159,
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+ "VERB": 160,
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+ "VERB|Degree=Equ": 161,
351
+ "VERB|Degree=Equ|VerbForm=Part": 162,
352
+ "VERB|Degree=Pos": 163,
353
+ "VERB|Degree=Pos|VerbForm=Part": 164,
354
+ "VERB|Polarity=Neg": 165,
355
+ "VERB|Polarity=Neg|VerbForm=Part": 166,
356
+ "VERB|VerbForm=Part": 167
357
+ },
358
+ "max_position_embeddings": 131072,
359
+ "max_window_layers": 28,
360
+ "model_type": "qwen2",
361
+ "num_attention_heads": 12,
362
+ "num_hidden_layers": 28,
363
+ "num_key_value_heads": 2,
364
+ "rms_norm_eps": 1e-06,
365
+ "rope_theta": 1000000.0,
366
+ "sliding_window": 131072,
367
+ "tie_word_embeddings": true,
368
+ "tokenizer_class": "Qwen2Tokenizer",
369
+ "torch_dtype": "float32",
370
+ "transformers_version": "4.42.4",
371
+ "use_cache": false,
372
+ "use_sliding_window": false,
373
+ "vocab_size": 151936
374
+ }
maker.sh ADDED
@@ -0,0 +1,105 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #! /bin/sh
2
+ S=Xunzi-Qwen2-1.5B
3
+ U=UD_Classical_Chinese-Kyoto
4
+ test -d $U || git clone --depth=1 https://github.com/UniversalDependencies/$U
5
+ for F in train dev test
6
+ do cp $U/*-$F.conllu $F.conllu
7
+ done
8
+ test -d $S || git clone --depth=1 https://www.modelscope.cn/Xunzillm4cc/$S.git
9
+
10
+ TMP=./maker$$.py
11
+ ( echo '#! /usr/bin/env deepspeed'
12
+ echo 'src="'$S'"'
13
+ echo 'tgt="KoichiYasuoka/'$S'-upos"'
14
+ ) > $TMP
15
+ cat << 'EOF' >> $TMP
16
+ from transformers import AutoTokenizer,Qwen2ForTokenClassification,AutoConfig,DataCollatorForTokenClassification,TrainingArguments,Trainer
17
+
18
+ class UPOSFileDataset(object):
19
+ def __init__(self,conllu,tokenizer):
20
+ self.conllu=open(conllu,"r",encoding="utf-8")
21
+ self.tokenizer=tokenizer
22
+ self.seeks=[0]
23
+ self.multiword={}
24
+ label=set(["SYM"])
25
+ s=self.conllu.readline()
26
+ while s!="":
27
+ if s=="\n":
28
+ self.seeks.append(self.conllu.tell())
29
+ else:
30
+ w=s.split("\t")
31
+ if len(w)==10:
32
+ if w[0].isdecimal():
33
+ label.add(w[3] if w[5]=="_" else w[3]+"|"+w[5])
34
+ elif w[0].find("-")>0:
35
+ t=w[0].split("-")
36
+ f,j,k=w[1],[],[]
37
+ for i in range(int(t[0]),int(t[1])+1):
38
+ w=self.conllu.readline().split("\t")
39
+ j.append(w[3] if w[5]=="_" else w[3]+"|"+w[5])
40
+ k.append(w[1])
41
+ p="+".join(j)
42
+ label.add(p)
43
+ if p in self.multiword:
44
+ self.multiword[p][f]=list(k)
45
+ else:
46
+ self.multiword[p]={f:list(k)}
47
+ s=self.conllu.readline()
48
+ lid={}
49
+ for i,l in enumerate(sorted(label)):
50
+ lid[l],lid["B-"+l],lid["I-"+l]=i*3,i*3+1,i*3+2
51
+ self.label2id=lid
52
+ def __call__(*args):
53
+ lid={l:i for i,l in enumerate(sorted(set(sum([list(t.label2id) for t in args],[]))))}
54
+ for t in args:
55
+ t.label2id=lid
56
+ return lid
57
+ def __del__(self):
58
+ self.conllu.close()
59
+ __len__=lambda self:len(self.seeks)-1
60
+ def __getitem__(self,i):
61
+ self.conllu.seek(self.seeks[i])
62
+ form,upos=[],[]
63
+ while self.conllu.tell()<self.seeks[i+1]:
64
+ w=self.conllu.readline().split("\t")
65
+ if len(w)==10:
66
+ form.append(w[1])
67
+ if w[0].isdecimal():
68
+ upos.append(w[3] if w[5]=="_" else w[3]+"|"+w[5])
69
+ elif w[0].find("-")>0:
70
+ t=w[0].split("-")
71
+ u=[]
72
+ for j in range(int(t[0]),int(t[1])+1):
73
+ k=self.conllu.readline().split("\t")
74
+ u.append(k[3] if k[5]=="_" else k[3]+"|"+k[5])
75
+ upos.append("+".join(u))
76
+ v=self.tokenizer(form,add_special_tokens=False)
77
+ i,u=[],[]
78
+ for j,(x,y) in enumerate(zip(v["input_ids"],upos)):
79
+ if x!=[]:
80
+ i+=x
81
+ u+=[y] if len(x)==1 else ["B-"+y]+["I-"+y]*(len(x)-1)
82
+ if len(i)<self.tokenizer.model_max_length-3:
83
+ ids=i
84
+ upos=u
85
+ else:
86
+ ids=i[0:self.tokenizer.model_max_length-2]
87
+ upos=u[0:self.tokenizer.model_max_length-2]
88
+ return {"input_ids":ids,"labels":[self.label2id[t] for t in upos]}
89
+
90
+ tkz=AutoTokenizer.from_pretrained(src)
91
+ trainDS=UPOSFileDataset("train.conllu",tkz)
92
+ devDS=UPOSFileDataset("dev.conllu",tkz)
93
+ testDS=UPOSFileDataset("test.conllu",tkz)
94
+ lid=trainDS(devDS,testDS)
95
+ cfg=AutoConfig.from_pretrained(src,num_labels=len(lid),label2id=lid,id2label={i:l for l,i in lid.items()},ignore_mismatched_sizes=True)
96
+ dsp={"fp16":{"enabled":"auto"},"optimizer":{"type":"AdamW"},"scheduler":{"type":"WarmupLR","params":{}},"train_batch_size":"auto","train_micro_batch_size_per_gpu":"auto","zero_optimization":{"stage":3,"offload_optimizer":{"device":"cpu","pin_memory":True},"offload_param":{"device":"cpu","pin_memory":True},"overlap_comm":True,"contiguous_gradients":True,"reduce_bucket_size":"auto","stage3_prefetch_bucket_size":"auto","stage3_param_persistence_threshold":"auto","stage3_gather_16bit_weights_on_model_save":True}}
97
+ arg=TrainingArguments(num_train_epochs=3,per_device_train_batch_size=32,deepspeed=dsp,output_dir=tgt,overwrite_output_dir=True,save_total_limit=2,learning_rate=5e-05,warmup_ratio=0.1,save_safetensors=False)
98
+ trn=Trainer(args=arg,data_collator=DataCollatorForTokenClassification(tkz),model=Qwen2ForTokenClassification.from_pretrained(src,config=cfg,ignore_mismatched_sizes=True),train_dataset=trainDS)
99
+ trn.train()
100
+ trn.save_model(tgt)
101
+ tkz.save_pretrained(tgt)
102
+ EOF
103
+ chmod 755 $TMP
104
+ $TMP
105
+ exit
mergeout.sh ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ #! /usr/bin/egrep -vf
2
+ ^ã[^ĢģĤĥĦħĨĩĪīĬĭĮįİı ][^ ]
3
+ ^[ãäåæçèé][^ ][^ ]
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+ "model.layers.8.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
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+ "model.layers.8.self_attn.k_proj.bias": "pytorch_model-00001-of-00002.bin",
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+ "model.layers.8.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
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+ "model.layers.8.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
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+ "model.layers.8.self_attn.q_proj.bias": "pytorch_model-00001-of-00002.bin",
328
+ "model.layers.8.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
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+ "model.layers.8.self_attn.v_proj.bias": "pytorch_model-00001-of-00002.bin",
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+ "model.layers.8.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
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+ "model.layers.9.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
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+ "model.layers.9.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
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+ "model.layers.9.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
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+ "model.layers.9.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
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+ "model.layers.9.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
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+ "model.layers.9.self_attn.k_proj.bias": "pytorch_model-00001-of-00002.bin",
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+ "model.layers.9.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
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+ "model.layers.9.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
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+ "model.layers.9.self_attn.q_proj.bias": "pytorch_model-00001-of-00002.bin",
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+ "model.layers.9.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
341
+ "model.layers.9.self_attn.v_proj.bias": "pytorch_model-00001-of-00002.bin",
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+ "model.layers.9.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
343
+ "model.norm.weight": "pytorch_model-00002-of-00002.bin",
344
+ "score.bias": "pytorch_model-00002-of-00002.bin",
345
+ "score.weight": "pytorch_model-00002-of-00002.bin"
346
+ }
347
+ }
special_tokens_map.json ADDED
@@ -0,0 +1,20 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "additional_special_tokens": [
3
+ "<|im_start|>",
4
+ "<|im_end|>"
5
+ ],
6
+ "eos_token": {
7
+ "content": "<|endoftext|>",
8
+ "lstrip": false,
9
+ "normalized": false,
10
+ "rstrip": false,
11
+ "single_word": false
12
+ },
13
+ "pad_token": {
14
+ "content": "<|endoftext|>",
15
+ "lstrip": false,
16
+ "normalized": false,
17
+ "rstrip": false,
18
+ "single_word": false
19
+ }
20
+ }
tokenizer_config.json ADDED
@@ -0,0 +1,43 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "add_prefix_space": false,
3
+ "added_tokens_decoder": {
4
+ "151643": {
5
+ "content": "<|endoftext|>",
6
+ "lstrip": false,
7
+ "normalized": false,
8
+ "rstrip": false,
9
+ "single_word": false,
10
+ "special": true
11
+ },
12
+ "151644": {
13
+ "content": "<|im_start|>",
14
+ "lstrip": false,
15
+ "normalized": false,
16
+ "rstrip": false,
17
+ "single_word": false,
18
+ "special": true
19
+ },
20
+ "151645": {
21
+ "content": "<|im_end|>",
22
+ "lstrip": false,
23
+ "normalized": false,
24
+ "rstrip": false,
25
+ "single_word": false,
26
+ "special": true
27
+ }
28
+ },
29
+ "additional_special_tokens": [
30
+ "<|im_start|>",
31
+ "<|im_end|>"
32
+ ],
33
+ "bos_token": null,
34
+ "clean_up_tokenization_spaces": false,
35
+ "eos_token": "<|endoftext|>",
36
+ "errors": "replace",
37
+ "model_max_length": 32768,
38
+ "pad_token": "<|endoftext|>",
39
+ "padding_side": "right",
40
+ "split_special_tokens": false,
41
+ "tokenizer_class": "Qwen2Tokenizer",
42
+ "unk_token": null
43
+ }
upos.py ADDED
@@ -0,0 +1,41 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from transformers import TokenClassificationPipeline
2
+
3
+ class BellmanFordTokenClassificationPipeline(TokenClassificationPipeline):
4
+ def __init__(self,**kwargs):
5
+ import numpy
6
+ super().__init__(**kwargs)
7
+ x=self.model.config.label2id
8
+ y=[k for k in x if not k.startswith("I-")]
9
+ self.transition=numpy.full((len(x),len(x)),numpy.nan)
10
+ for k,v in x.items():
11
+ for j in ["I-"+k[2:]] if k.startswith("B-") else [k]+y if k.startswith("I-") else y:
12
+ self.transition[v,x[j]]=0
13
+ def check_model_type(self,supported_models):
14
+ pass
15
+ def postprocess(self,model_outputs,**kwargs):
16
+ import numpy
17
+ if "logits" not in model_outputs:
18
+ return self.postprocess(model_outputs[0],**kwargs)
19
+ m=model_outputs["logits"][0].numpy()
20
+ e=numpy.exp(m-numpy.max(m,axis=-1,keepdims=True))
21
+ z=e/e.sum(axis=-1,keepdims=True)
22
+ for i in range(m.shape[0]-1,0,-1):
23
+ m[i-1]+=numpy.nanmax(m[i]+self.transition,axis=1)
24
+ k=[numpy.nanargmax(m[0])]
25
+ for i in range(1,m.shape[0]):
26
+ k.append(numpy.nanargmax(m[i]+self.transition[k[-1]]))
27
+ w=[{"entity":self.model.config.id2label[j],"start":s,"end":e,"score":z[i,j]} for i,((s,e),j) in enumerate(zip(model_outputs["offset_mapping"][0].tolist(),k)) if s<e]
28
+ if "aggregation_strategy" in kwargs and kwargs["aggregation_strategy"]!="none":
29
+ for i,t in reversed(list(enumerate(w))):
30
+ p=t.pop("entity")
31
+ if p.startswith("I-"):
32
+ w[i-1]["score"]=min(w[i-1]["score"],t["score"])
33
+ w[i-1]["end"]=w.pop(i)["end"]
34
+ elif p.startswith("B-"):
35
+ t["entity_group"]=p[2:]
36
+ else:
37
+ t["entity_group"]=p
38
+ for t in w:
39
+ t["text"]=model_outputs["sentence"][t["start"]:t["end"]]
40
+ return w
41
+
vocab.json ADDED
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