model improved

setup.py

@@ -5,7 +5,7 @@ URL="https://github.com/KoichiYasuoka/SuPar-Kanbun"
 
 setuptools.setup(
   name="suparkanbun",
-  version="1.3.6",
+  version="1.3.8",
   description="Tokenizer POS-tagger and Dependency-parser for Classical Chinese",
   long_description=long_description,
   long_description_content_type="text/markdown",

suparkanbun/models/gloss.orig.txt

@@ -390,6 +390,8 @@
 像 像 v,動詞,行為,動作 image
 侨 僑 侨 v,動詞,行為,動作 sojourn
 僑 僑 侨 v,動詞,行為,動作 sojourn
+仆 僕 仆 n,代名詞,人称,止格 [1PRON]
+僕 僕 仆 n,代名詞,人称,止格 [1PRON]
 仆 僕 仆 n,名詞,人,役割 servant
 僕 僕 仆 n,名詞,人,役割 servant
 仆 僕 仆 v,動詞,描写,態度 intricate
@@ -613,6 +615,7 @@
 功 功 n,名詞,可搬,成果物 achievement
 加 加 v,動詞,行為,得失 add
 助 助 v,動詞,行為,交流 help
+劫 劫 n,名詞,時,* kalpa
 劫 劫 v,動詞,行為,交流 rob
 勃 勃 v,動詞,描写,形質 sudden
 勇 勇 n,名詞,描写,態度 bravery
@@ -1047,6 +1050,8 @@
 堯典 堯典 尧典 n,名詞,主体,書物 [book-name]
 尧典 堯典 尧典 n,名詞,主体,書物 [book-name]
 尭典 堯典 尧典 n,名詞,主体,書物 [book-name]
+報 報 报 n,名詞,可搬,成果物 retribution
+报 報 报 n,名詞,可搬,成果物 retribution
 報 報 报 v,動詞,行為,交流 report-back
 报 報 报 v,動詞,行為,交流 report-back
 场 場 场 n,名詞,固定物,建造物 meeting-place
@@ -2888,6 +2893,10 @@
 淇 淇 n,名詞,固定物,地名 [place-name]
 淑 淑 v,動詞,描写,態度 gentle
 淡 淡 v,動詞,描写,形質 thin
+净 淨 净 v,動詞,描写,形質 clean
+凈 淨 净 v,動詞,描写,形質 clean
+浄 淨 净 v,動詞,描写,形質 clean
+淨 淨 净 v,動詞,描写,形質 clean
 淫 淫 n,名詞,描写,態度 licentious
 淫 淫 v,動詞,描写,態度 excessive
 深 深 v,動詞,描写,量 deep
@@ -3124,6 +3133,8 @@
 爽 爽 v,動詞,行為,交流 lively
 尔 爾 尔 n,代名詞,人称,止格 [2PRON]
 爾 爾 尔 n,代名詞,人称,止格 [2PRON]
+尔 爾 尔 n,代名詞,指示,* this
+爾 爾 尔 n,代名詞,指示,* this
 尔 爾 尔 p,助詞,句末,* [final-particle]
 爾 爾 尔 p,助詞,句末,* [final-particle]
 尔 爾 尔 p,接尾辞,*,* [suffix]
@@ -3409,6 +3420,7 @@
 目 目 v,動詞,行為,動作 look-at
 直 直 v,副詞,範囲,限定 only
 直 直 v,動詞,描写,形質 straight
+相 相 n,名詞,不可譲,身体 appearlance
 相 相 n,名詞,人,役割 chief-minister
 相 相 v,副詞,範囲,共同 each-other
 相 相 v,動詞,行為,交流 assist

suparkanbun/models/guwenbert-base.danku/config.json

@@ -5,29 +5,29 @@
   ],
   "attention_probs_dropout_prob": 0.1,
   "bos_token_id": 0,
+  "classifier_dropout": null,
   "eos_token_id": 2,
   "finetuning_task": "ner",
-  "gradient_checkpointing": false,
   "hidden_act": "gelu",
   "hidden_dropout_prob": 0.1,
   "hidden_size": 768,
   "id2label": {
-    "0": "LABEL_0",
-    "1": "LABEL_1",
-    "2": "LABEL_2",
-    "3": "LABEL_3",
-    "4": "LABEL_4",
-    "5": "LABEL_5"
+    "0": "B",
+    "1": "E",
+    "2": "E2",
+    "3": "E3",
+    "4": "M",
+    "5": "S"
   },
   "initializer_range": 0.02,
   "intermediate_size": 3072,
   "label2id": {
-    "LABEL_0": 0,
-    "LABEL_1": 1,
-    "LABEL_2": 2,
-    "LABEL_3": 3,
-    "LABEL_4": 4,
-    "LABEL_5": 5
+    "B": 0,
+    "E": 1,
+    "E2": 2,
+    "E3": 3,
+    "M": 4,
+    "S": 5
   },
   "layer_norm_eps": 1e-05,
   "max_position_embeddings": 514,
@@ -38,7 +38,7 @@
   "position_embedding_type": "absolute",
   "tokenizer_class": "BertTokenizer",
   "torch_dtype": "float32",
-  "transformers_version": "4.9.2",
+  "transformers_version": "4.11.3",
   "type_vocab_size": 1,
   "use_cache": true,
   "vocab_size": 23292

suparkanbun/models/guwenbert-base.danku/filesize.txt

@@ -1 +1 @@
-pytorch_model.bin 413465365
+pytorch_model.bin 413465359

suparkanbun/models/guwenbert-base.danku/pytorch_model.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:39f108469923aff969a33f3c8d0d786d4104e87bc32692ba7d5ea951c388a8a7
-size 413465365
+oid sha256:2a0a39f0241d2b97409c36c4fe8a75c790f3f4af1db7900a3b795c2a47fb6299
+size 413465359

suparkanbun/models/guwenbert-base.danku/tokenizer_config.json

@@ -1 +1 @@
-{"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "special_tokens_map_file": null, "name_or_path": "ethanyt/guwenbert-base", "do_basic_tokenize": true, "never_split": null, "tokenizer_class": "BertTokenizer"}
+{"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "add_prefix_space": true, "special_tokens_map_file": null, "name_or_path": "ethanyt/guwenbert-base", "do_basic_tokenize": true, "never_split": null, "tokenizer_class": "BertTokenizer"}

suparkanbun/models/guwenbert-base.pos/config.json

@@ -5,275 +5,275 @@
   ],
   "attention_probs_dropout_prob": 0.1,
   "bos_token_id": 0,
+  "classifier_dropout": null,
   "eos_token_id": 2,
   "finetuning_task": "ner",
-  "gradient_checkpointing": false,
   "hidden_act": "gelu",
   "hidden_dropout_prob": 0.1,
   "hidden_size": 768,
   "id2label": {
-    "0": "n,代名詞,人称,他,PRON,Person=1|PronType=Prs",
-    "1": "n,代名詞,人称,他,PRON,Person=2|PronType=Prs",
-    "2": "n,代名詞,人称,他,PRON,Person=3|PronType=Prs",
-    "3": "n,代名詞,人称,他,PRON,PronType=Prs",
-    "4": "n,代名詞,人称,他,PRON,PronType=Prs|Reflex=Yes",
-    "5": "n,代名詞,人称,止格,PRON,Person=1|PronType=Prs",
-    "6": "n,代名詞,人称,止格,PRON,Person=2|PronType=Prs",
-    "7": "n,代名詞,人称,止格,PRON,Person=3|PronType=Prs",
-    "8": "n,代名詞,人称,止格,PRON,PronType=Prs",
-    "9": "n,代名詞,人称,起格,PRON,Person=1|PronType=Prs",
-    "10": "n,代名詞,人称,起格,PRON,Person=2|PronType=Prs",
-    "11": "n,代名詞,人称,起格,PRON,Person=3|PronType=Prs",
-    "12": "n,代名詞,人称,起格,PRON,PronType=Prs",
-    "13": "n,代名詞,指示,*,PRON,PronType=Dem",
-    "14": "n,代名詞,疑問,*,PRON,PronType=Int",
-    "15": "n,名詞,不可譲,属性,NOUN,_",
-    "16": "n,名詞,不可譲,疾病,NOUN,_",
-    "17": "n,名詞,不可譲,身体,NOUN,_",
-    "18": "n,名詞,主体,動物,NOUN,_",
-    "19": "n,名詞,主体,国名,PROPN,Case=Loc|NameType=Nat",
-    "20": "n,名詞,主体,書物,NOUN,_",
-    "21": "n,名詞,主体,機関,NOUN,_",
-    "22": "n,名詞,主体,集団,NOUN,_",
-    "23": "n,名詞,人,その他の人名,PROPN,NameType=Prs",
-    "24": "n,名詞,人,人,NOUN,_",
-    "25": "n,名詞,人,名,PROPN,NameType=Giv",
-    "26": "n,名詞,人,姓氏,PROPN,NameType=Sur",
-    "27": "n,名詞,人,役割,NOUN,_",
-    "28": "n,名詞,人,複合的人名,PROPN,NameType=Prs",
-    "29": "n,名詞,人,関係,NOUN,_",
-    "30": "n,名詞,制度,儀礼,NOUN,_",
-    "31": "n,名詞,制度,場,NOUN,Case=Loc",
-    "32": "n,名詞,可搬,乗り物,NOUN,_",
-    "33": "n,名詞,可搬,伝達,NOUN,_",
-    "34": "n,名詞,可搬,成果物,NOUN,_",
-    "35": "n,名詞,可搬,糧食,NOUN,_",
-    "36": "n,名詞,可搬,道具,NOUN,_",
-    "37": "n,名詞,固定物,地名,PROPN,Case=Loc|NameType=Geo",
-    "38": "n,名詞,固定物,地形,NOUN,Case=Loc",
-    "39": "n,名詞,固定物,建造物,NOUN,Case=Loc",
-    "40": "n,名詞,固定物,樹木,NOUN,_",
-    "41": "n,名詞,固定物,関係,NOUN,Case=Loc",
-    "42": "n,名詞,外観,人,NOUN,_",
-    "43": "n,名詞,天象,天文,NOUN,_",
-    "44": "n,名詞,天象,怪異,NOUN,_",
-    "45": "n,名詞,天象,気象,NOUN,_",
-    "46": "n,名詞,度量衡,*,NOUN,NounType=Clf",
-    "47": "n,名詞,思考,*,NOUN,_",
-    "48": "n,名詞,描写,形質,NOUN,_",
-    "49": "n,名詞,描写,態度,NOUN,_",
-    "50": "n,名詞,数量,*,NOUN,_",
-    "51": "n,名詞,時,*,NOUN,Case=Tem",
-    "52": "n,名詞,行為,*,NOUN,_",
-    "53": "n,数詞,干支,*,NUM,NumType=Ord",
-    "54": "n,数詞,数,*,NUM,_",
-    "55": "n,数詞,数字,*,NUM,_",
-    "56": "p,助詞,句末,*,PART,_",
-    "57": "p,助詞,句頭,*,PART,_",
-    "58": "p,助詞,接続,並列,CCONJ,_",
-    "59": "p,助詞,接続,体言化,PART,_",
-    "60": "p,助詞,接続,属格,SCONJ,_",
-    "61": "p,助詞,提示,*,PART,_",
-    "62": "p,感嘆詞,*,*,INTJ,_",
-    "63": "p,接尾辞,*,*,PART,_",
-    "64": "s,文字,*,*,SYM,_",
-    "65": "s,記号,一般,*,SYM,_",
-    "66": "s,記号,句点,*,PUNCT,_",
-    "67": "s,記号,読点,*,PUNCT,_",
-    "68": "v,前置詞,基盤,*,ADP,_",
-    "69": "v,前置詞,源泉,*,ADP,_",
-    "70": "v,前置詞,経由,*,ADP,_",
-    "71": "v,前置詞,関係,*,ADP,_",
-    "72": "v,副詞,判断,推定,ADV,_",
-    "73": "v,副詞,判断,確定,ADV,_",
-    "74": "v,副詞,判断,逆接,ADV,_",
-    "75": "v,副詞,否定,体言否定,ADV,Polarity=Neg",
-    "76": "v,副詞,否定,有界,ADV,Polarity=Neg",
-    "77": "v,副詞,否定,無界,ADV,Polarity=Neg",
-    "78": "v,副詞,否定,禁止,ADV,Polarity=Neg",
-    "79": "v,副詞,描写,*,ADV,_",
-    "80": "v,副詞,時相,変化,ADV,AdvType=Tim",
-    "81": "v,副詞,時相,完了,ADV,AdvType=Tim|Aspect=Perf",
-    "82": "v,副詞,時相,将来,ADV,AdvType=Tim|Tense=Fut",
-    "83": "v,副詞,時相,恒常,ADV,AdvType=Tim",
-    "84": "v,副詞,時相,現在,ADV,AdvType=Tim|Tense=Pres",
-    "85": "v,副詞,時相,終局,ADV,AdvType=Tim",
-    "86": "v,副詞,時相,継起,ADV,AdvType=Tim",
-    "87": "v,副詞,時相,緊接,ADV,AdvType=Tim",
-    "88": "v,副詞,時相,過去,ADV,AdvType=Tim|Tense=Past",
-    "89": "v,副詞,疑問,原因,ADV,AdvType=Cau",
-    "90": "v,副詞,疑問,反語,ADV,_",
-    "91": "v,副詞,疑問,所在,ADV,_",
-    "92": "v,副詞,程度,やや高度,ADV,AdvType=Deg|Degree=Cmp",
-    "93": "v,副詞,程度,極度,ADV,AdvType=Deg|Degree=Sup",
-    "94": "v,副詞,程度,軽度,ADV,AdvType=Deg|Degree=Pos",
-    "95": "v,副詞,範囲,共同,ADV,_",
-    "96": "v,副詞,範囲,総��,ADV,_",
-    "97": "v,副詞,範囲,限定,ADV,_",
-    "98": "v,副詞,頻度,偶発,ADV,_",
-    "99": "v,副詞,頻度,重複,ADV,_",
-    "100": "v,副詞,頻度,頻繁,ADV,_",
-    "101": "v,助動詞,受動,*,AUX,Voice=Pass",
-    "102": "v,助動詞,可能,*,AUX,Mood=Pot",
-    "103": "v,助動詞,必要,*,AUX,Mood=Nec",
-    "104": "v,助動詞,願望,*,AUX,Mood=Des",
-    "105": "v,動詞,変化,制度,VERB,_",
-    "106": "v,動詞,変化,性質,VERB,_",
-    "107": "v,動詞,変化,生物,VERB,_",
-    "108": "v,動詞,存在,存在,VERB,Polarity=Neg",
-    "109": "v,動詞,存在,存在,VERB,VerbType=Cop",
-    "110": "v,動詞,存在,存在,VERB,_",
-    "111": "v,動詞,描写,境遇,VERB,Degree=Pos",
-    "112": "v,動詞,描写,形質,VERB,Degree=Pos",
-    "113": "v,動詞,描写,態度,VERB,Degree=Pos",
-    "114": "v,動詞,描写,量,VERB,Degree=Pos",
-    "115": "v,動詞,行為,交流,VERB,_",
-    "116": "v,動詞,行為,伝達,VERB,_",
-    "117": "v,動詞,行為,使役,VERB,_",
-    "118": "v,動詞,行為,儀礼,VERB,_",
-    "119": "v,動詞,行為,分類,VERB,Degree=Equ",
-    "120": "v,動詞,行為,動作,VERB,_",
-    "121": "v,動詞,行為,姿勢,VERB,_",
-    "122": "v,動詞,行為,役割,VERB,_",
-    "123": "v,動詞,行為,得失,VERB,_",
-    "124": "v,動詞,行為,態度,VERB,_",
-    "125": "v,動詞,行為,生産,VERB,_",
-    "126": "v,動詞,行為,移動,VERB,_",
-    "127": "v,動詞,行為,設置,VERB,_",
-    "128": "v,動詞,行為,飲食,VERB,_"
+    "0": "n,\u4ee3\u540d\u8a5e,\u4eba\u79f0,\u4ed6,PRON,Person=1|PronType=Prs",
+    "1": "n,\u4ee3\u540d\u8a5e,\u4eba\u79f0,\u4ed6,PRON,Person=2|PronType=Prs",
+    "2": "n,\u4ee3\u540d\u8a5e,\u4eba\u79f0,\u4ed6,PRON,Person=3|PronType=Prs",
+    "3": "n,\u4ee3\u540d\u8a5e,\u4eba\u79f0,\u4ed6,PRON,PronType=Prs",
+    "4": "n,\u4ee3\u540d\u8a5e,\u4eba\u79f0,\u4ed6,PRON,PronType=Prs|Reflex=Yes",
+    "5": "n,\u4ee3\u540d\u8a5e,\u4eba\u79f0,\u6b62\u683c,PRON,Person=1|PronType=Prs",
+    "6": "n,\u4ee3\u540d\u8a5e,\u4eba\u79f0,\u6b62\u683c,PRON,Person=2|PronType=Prs",
+    "7": "n,\u4ee3\u540d\u8a5e,\u4eba\u79f0,\u6b62\u683c,PRON,Person=3|PronType=Prs",
+    "8": "n,\u4ee3\u540d\u8a5e,\u4eba\u79f0,\u8d77\u683c,PRON,Person=1|PronType=Prs",
+    "9": "n,\u4ee3\u540d\u8a5e,\u4eba\u79f0,\u8d77\u683c,PRON,Person=2|PronType=Prs",
+    "10": "n,\u4ee3\u540d\u8a5e,\u4eba\u79f0,\u8d77\u683c,PRON,Person=3|PronType=Prs",
+    "11": "n,\u4ee3\u540d\u8a5e,\u4eba\u79f0,\u8d77\u683c,PRON,PronType=Prs",
+    "12": "n,\u4ee3\u540d\u8a5e,\u6307\u793a,*,PRON,PronType=Dem",
+    "13": "n,\u4ee3\u540d\u8a5e,\u7591\u554f,*,PRON,PronType=Int",
+    "14": "n,\u540d\u8a5e,\u4e0d\u53ef\u8b72,\u5c5e\u6027,NOUN,_",
+    "15": "n,\u540d\u8a5e,\u4e0d\u53ef\u8b72,\u75be\u75c5,NOUN,_",
+    "16": "n,\u540d\u8a5e,\u4e0d\u53ef\u8b72,\u8eab\u4f53,NOUN,_",
+    "17": "n,\u540d\u8a5e,\u4e3b\u4f53,\u52d5\u7269,NOUN,_",
+    "18": "n,\u540d\u8a5e,\u4e3b\u4f53,\u56fd\u540d,PROPN,Case=Loc|NameType=Nat",
+    "19": "n,\u540d\u8a5e,\u4e3b\u4f53,\u66f8\u7269,NOUN,_",
+    "20": "n,\u540d\u8a5e,\u4e3b\u4f53,\u6a5f\u95a2,NOUN,_",
+    "21": "n,\u540d\u8a5e,\u4e3b\u4f53,\u795e\u4ecf,NOUN,_",
+    "22": "n,\u540d\u8a5e,\u4e3b\u4f53,\u96c6\u56e3,NOUN,_",
+    "23": "n,\u540d\u8a5e,\u4eba,\u305d\u306e\u4ed6\u306e\u4eba\u540d,PROPN,NameType=Prs",
+    "24": "n,\u540d\u8a5e,\u4eba,\u4eba,NOUN,_",
+    "25": "n,\u540d\u8a5e,\u4eba,\u540d,PROPN,NameType=Giv",
+    "26": "n,\u540d\u8a5e,\u4eba,\u59d3\u6c0f,PROPN,NameType=Sur",
+    "27": "n,\u540d\u8a5e,\u4eba,\u5f79\u5272,NOUN,_",
+    "28": "n,\u540d\u8a5e,\u4eba,\u8907\u5408\u7684\u4eba\u540d,PROPN,NameType=Prs",
+    "29": "n,\u540d\u8a5e,\u4eba,\u95a2\u4fc2,NOUN,_",
+    "30": "n,\u540d\u8a5e,\u5236\u5ea6,\u5100\u793c,NOUN,_",
+    "31": "n,\u540d\u8a5e,\u5236\u5ea6,\u5834,NOUN,Case=Loc",
+    "32": "n,\u540d\u8a5e,\u53ef\u642c,\u4e57\u308a\u7269,NOUN,_",
+    "33": "n,\u540d\u8a5e,\u53ef\u642c,\u4f1d\u9054,NOUN,_",
+    "34": "n,\u540d\u8a5e,\u53ef\u642c,\u6210\u679c\u7269,NOUN,_",
+    "35": "n,\u540d\u8a5e,\u53ef\u642c,\u7ce7\u98df,NOUN,_",
+    "36": "n,\u540d\u8a5e,\u53ef\u642c,\u9053\u5177,NOUN,_",
+    "37": "n,\u540d\u8a5e,\u56fa\u5b9a\u7269,\u5730\u540d,PROPN,Case=Loc|NameType=Geo",
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suparkanbun/models/guwenbert-base.pos/tokenizer_config.json

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suparkanbun/models/guwenbert-large.danku/config.json

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suparkanbun/models/guwenbert-large.danku/filesize.txt

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suparkanbun/models/guwenbert-large.pos/config.json

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-    "32": "n,名詞,可搬,乗り物,NOUN,_",
-    "33": "n,名詞,可搬,伝達,NOUN,_",
-    "34": "n,名詞,可搬,成果物,NOUN,_",
-    "35": "n,名詞,可搬,糧食,NOUN,_",
-    "36": "n,名詞,可搬,道具,NOUN,_",
-    "37": "n,名詞,固定物,地名,PROPN,Case=Loc|NameType=Geo",
-    "38": "n,名詞,固定物,地形,NOUN,Case=Loc",
-    "39": "n,名詞,固定物,建造物,NOUN,Case=Loc",
-    "40": "n,名詞,固定物,樹木,NOUN,_",
-    "41": "n,名詞,固定物,関係,NOUN,Case=Loc",
-    "42": "n,名詞,外観,人,NOUN,_",
-    "43": "n,名詞,天象,天文,NOUN,_",
-    "44": "n,名詞,天象,怪異,NOUN,_",
-    "45": "n,名詞,天象,気象,NOUN,_",
-    "46": "n,名詞,度量衡,*,NOUN,NounType=Clf",
-    "47": "n,名詞,思考,*,NOUN,_",
-    "48": "n,名詞,描写,形質,NOUN,_",
-    "49": "n,名詞,描写,態度,NOUN,_",
-    "50": "n,名詞,数量,*,NOUN,_",
-    "51": "n,名詞,時,*,NOUN,Case=Tem",
-    "52": "n,名詞,行為,*,NOUN,_",
-    "53": "n,数詞,干支,*,NUM,NumType=Ord",
-    "54": "n,数詞,数,*,NUM,_",
-    "55": "n,数詞,数字,*,NUM,_",
-    "56": "p,助詞,句末,*,PART,_",
-    "57": "p,助詞,句頭,*,PART,_",
-    "58": "p,助詞,接続,並列,CCONJ,_",
-    "59": "p,助詞,接続,体言化,PART,_",
-    "60": "p,助詞,接続,属格,SCONJ,_",
-    "61": "p,助詞,提示,*,PART,_",
-    "62": "p,感嘆詞,*,*,INTJ,_",
-    "63": "p,接尾辞,*,*,PART,_",
-    "64": "s,文字,*,*,SYM,_",
-    "65": "s,記号,一般,*,SYM,_",
-    "66": "s,記号,句点,*,PUNCT,_",
-    "67": "s,記号,読点,*,PUNCT,_",
-    "68": "v,前置詞,基盤,*,ADP,_",
-    "69": "v,前置詞,源泉,*,ADP,_",
-    "70": "v,前置詞,経由,*,ADP,_",
-    "71": "v,前置詞,関係,*,ADP,_",
-    "72": "v,副詞,判断,推定,ADV,_",
-    "73": "v,副詞,判断,確定,ADV,_",
-    "74": "v,副詞,判断,逆接,ADV,_",
-    "75": "v,副詞,否定,体言否定,ADV,Polarity=Neg",
-    "76": "v,副詞,否定,有界,ADV,Polarity=Neg",
-    "77": "v,副詞,否定,無界,ADV,Polarity=Neg",
-    "78": "v,副詞,否定,禁止,ADV,Polarity=Neg",
-    "79": "v,副詞,描写,*,ADV,_",
-    "80": "v,副詞,時相,変化,ADV,AdvType=Tim",
-    "81": "v,副詞,時相,完了,ADV,AdvType=Tim|Aspect=Perf",
-    "82": "v,副詞,時相,将来,ADV,AdvType=Tim|Tense=Fut",
-    "83": "v,副詞,時相,恒常,ADV,AdvType=Tim",
-    "84": "v,副詞,時相,現在,ADV,AdvType=Tim|Tense=Pres",
-    "85": "v,副詞,時相,終局,ADV,AdvType=Tim",
-    "86": "v,副詞,時相,継起,ADV,AdvType=Tim",
-    "87": "v,副詞,時相,緊接,ADV,AdvType=Tim",
-    "88": "v,副詞,時相,過去,ADV,AdvType=Tim|Tense=Past",
-    "89": "v,副詞,疑問,原因,ADV,AdvType=Cau",
-    "90": "v,副詞,疑問,反語,ADV,_",
-    "91": "v,副詞,疑問,所在,ADV,_",
-    "92": "v,副詞,程度,やや高度,ADV,AdvType=Deg|Degree=Cmp",
-    "93": "v,副詞,程度,極度,ADV,AdvType=Deg|Degree=Sup",
-    "94": "v,副詞,程度,軽度,ADV,AdvType=Deg|Degree=Pos",
-    "95": "v,副詞,範囲,共同,ADV,_",
-    "96": "v,副詞,範囲,総���,ADV,_",
-    "97": "v,副詞,範囲,限定,ADV,_",
-    "98": "v,副詞,頻度,偶発,ADV,_",
-    "99": "v,副詞,頻度,重複,ADV,_",
-    "100": "v,副詞,頻度,頻繁,ADV,_",
-    "101": "v,助動詞,受動,*,AUX,Voice=Pass",
-    "102": "v,助動詞,可能,*,AUX,Mood=Pot",
-    "103": "v,助動詞,必要,*,AUX,Mood=Nec",
-    "104": "v,助動詞,願望,*,AUX,Mood=Des",
-    "105": "v,動詞,変化,制度,VERB,_",
-    "106": "v,動詞,変化,性質,VERB,_",
-    "107": "v,動詞,変化,生物,VERB,_",
-    "108": "v,動詞,存在,存在,VERB,Polarity=Neg",
-    "109": "v,動詞,存在,存在,VERB,VerbType=Cop",
-    "110": "v,動詞,存在,存在,VERB,_",
-    "111": "v,動詞,描写,境遇,VERB,Degree=Pos",
-    "112": "v,動詞,描写,形質,VERB,Degree=Pos",
-    "113": "v,動詞,描写,態度,VERB,Degree=Pos",
-    "114": "v,動詞,描写,量,VERB,Degree=Pos",
-    "115": "v,動詞,行為,交流,VERB,_",
-    "116": "v,動詞,行為,伝達,VERB,_",
-    "117": "v,動詞,行為,使役,VERB,_",
-    "118": "v,動詞,行為,儀礼,VERB,_",
-    "119": "v,動詞,行為,分類,VERB,Degree=Equ",
-    "120": "v,動詞,行為,動作,VERB,_",
-    "121": "v,動詞,行為,姿勢,VERB,_",
-    "122": "v,動詞,行為,役割,VERB,_",
-    "123": "v,動詞,行為,得失,VERB,_",
-    "124": "v,動詞,行為,態度,VERB,_",
-    "125": "v,動詞,行為,生産,VERB,_",
-    "126": "v,動詞,行為,移動,VERB,_",
-    "127": "v,動詞,行為,設置,VERB,_",
-    "128": "v,動詞,行為,飲食,VERB,_"
+    "0": "n,\u4ee3\u540d\u8a5e,\u4eba\u79f0,\u4ed6,PRON,Person=1|PronType=Prs",
+    "1": "n,\u4ee3\u540d\u8a5e,\u4eba\u79f0,\u4ed6,PRON,Person=2|PronType=Prs",
+    "2": "n,\u4ee3\u540d\u8a5e,\u4eba\u79f0,\u4ed6,PRON,Person=3|PronType=Prs",
+    "3": "n,\u4ee3\u540d\u8a5e,\u4eba\u79f0,\u4ed6,PRON,PronType=Prs",
+    "4": "n,\u4ee3\u540d\u8a5e,\u4eba\u79f0,\u4ed6,PRON,PronType=Prs|Reflex=Yes",
+    "5": "n,\u4ee3\u540d\u8a5e,\u4eba\u79f0,\u6b62\u683c,PRON,Person=1|PronType=Prs",
+    "6": "n,\u4ee3\u540d\u8a5e,\u4eba\u79f0,\u6b62\u683c,PRON,Person=2|PronType=Prs",
+    "7": "n,\u4ee3\u540d\u8a5e,\u4eba\u79f0,\u6b62\u683c,PRON,Person=3|PronType=Prs",
+    "8": "n,\u4ee3\u540d\u8a5e,\u4eba\u79f0,\u8d77\u683c,PRON,Person=1|PronType=Prs",
+    "9": "n,\u4ee3\u540d\u8a5e,\u4eba\u79f0,\u8d77\u683c,PRON,Person=2|PronType=Prs",
+    "10": "n,\u4ee3\u540d\u8a5e,\u4eba\u79f0,\u8d77\u683c,PRON,Person=3|PronType=Prs",
+    "11": "n,\u4ee3\u540d\u8a5e,\u4eba\u79f0,\u8d77\u683c,PRON,PronType=Prs",
+    "12": "n,\u4ee3\u540d\u8a5e,\u6307\u793a,*,PRON,PronType=Dem",
+    "13": "n,\u4ee3\u540d\u8a5e,\u7591\u554f,*,PRON,PronType=Int",
+    "14": "n,\u540d\u8a5e,\u4e0d\u53ef\u8b72,\u5c5e\u6027,NOUN,_",
+    "15": "n,\u540d\u8a5e,\u4e0d\u53ef\u8b72,\u75be\u75c5,NOUN,_",
+    "16": "n,\u540d\u8a5e,\u4e0d\u53ef\u8b72,\u8eab\u4f53,NOUN,_",
+    "17": "n,\u540d\u8a5e,\u4e3b\u4f53,\u52d5\u7269,NOUN,_",
+    "18": "n,\u540d\u8a5e,\u4e3b\u4f53,\u56fd\u540d,PROPN,Case=Loc|NameType=Nat",
+    "19": "n,\u540d\u8a5e,\u4e3b\u4f53,\u66f8\u7269,NOUN,_",
+    "20": "n,\u540d\u8a5e,\u4e3b\u4f53,\u6a5f\u95a2,NOUN,_",
+    "21": "n,\u540d\u8a5e,\u4e3b\u4f53,\u795e\u4ecf,NOUN,_",
+    "22": "n,\u540d\u8a5e,\u4e3b\u4f53,\u96c6\u56e3,NOUN,_",
+    "23": "n,\u540d\u8a5e,\u4eba,\u305d\u306e\u4ed6\u306e\u4eba\u540d,PROPN,NameType=Prs",
+    "24": "n,\u540d\u8a5e,\u4eba,\u4eba,NOUN,_",
+    "25": "n,\u540d\u8a5e,\u4eba,\u540d,PROPN,NameType=Giv",
+    "26": "n,\u540d\u8a5e,\u4eba,\u59d3\u6c0f,PROPN,NameType=Sur",
+    "27": "n,\u540d\u8a5e,\u4eba,\u5f79\u5272,NOUN,_",
+    "28": "n,\u540d\u8a5e,\u4eba,\u8907\u5408\u7684\u4eba\u540d,PROPN,NameType=Prs",
+    "29": "n,\u540d\u8a5e,\u4eba,\u95a2\u4fc2,NOUN,_",
+    "30": "n,\u540d\u8a5e,\u5236\u5ea6,\u5100\u793c,NOUN,_",
+    "31": "n,\u540d\u8a5e,\u5236\u5ea6,\u5834,NOUN,Case=Loc",
+    "32": "n,\u540d\u8a5e,\u53ef\u642c,\u4e57\u308a\u7269,NOUN,_",
+    "33": "n,\u540d\u8a5e,\u53ef\u642c,\u4f1d\u9054,NOUN,_",
+    "34": "n,\u540d\u8a5e,\u53ef\u642c,\u6210\u679c\u7269,NOUN,_",
+    "35": "n,\u540d\u8a5e,\u53ef\u642c,\u7ce7\u98df,NOUN,_",
+    "36": "n,\u540d\u8a5e,\u53ef\u642c,\u9053\u5177,NOUN,_",
+    "37": "n,\u540d\u8a5e,\u56fa\u5b9a\u7269,\u5730\u540d,PROPN,Case=Loc|NameType=Geo",
+    "38": "n,\u540d\u8a5e,\u56fa\u5b9a\u7269,\u5730\u5f62,NOUN,Case=Loc",
+    "39": "n,\u540d\u8a5e,\u56fa\u5b9a\u7269,\u5efa\u9020\u7269,NOUN,Case=Loc",
+    "40": "n,\u540d\u8a5e,\u56fa\u5b9a\u7269,\u6a39\u6728,NOUN,_",
+    "41": "n,\u540d\u8a5e,\u56fa\u5b9a\u7269,\u95a2\u4fc2,NOUN,Case=Loc",
+    "42": "n,\u540d\u8a5e,\u5916\u89b3,\u4eba,NOUN,_",
+    "43": "n,\u540d\u8a5e,\u5929\u8c61,\u5929\u6587,NOUN,_",
+    "44": "n,\u540d\u8a5e,\u5929\u8c61,\u602a\u7570,NOUN,_",
+    "45": "n,\u540d\u8a5e,\u5929\u8c61,\u6c17\u8c61,NOUN,_",
+    "46": "n,\u540d\u8a5e,\u5ea6\u91cf\u8861,*,NOUN,NounType=Clf",
+    "47": "n,\u540d\u8a5e,\u601d\u8003,*,NOUN,_",
+    "48": "n,\u540d\u8a5e,\u63cf\u5199,\u5f62\u8cea,NOUN,_",
+    "49": "n,\u540d\u8a5e,\u63cf\u5199,\u614b\u5ea6,NOUN,_",
+    "50": "n,\u540d\u8a5e,\u6570\u91cf,*,NOUN,_",
+    "51": "n,\u540d\u8a5e,\u6642,*,NOUN,Case=Tem",
+    "52": "n,\u540d\u8a5e,\u884c\u70ba,*,NOUN,_",
+    "53": "n,\u6570\u8a5e,\u5e72\u652f,*,NUM,NumType=Ord",
+    "54": "n,\u6570\u8a5e,\u6570,*,NUM,_",
+    "55": "n,\u6570\u8a5e,\u6570\u5b57,*,NUM,_",
+    "56": "p,\u52a9\u8a5e,\u53e5\u672b,*,PART,_",
+    "57": "p,\u52a9\u8a5e,\u53e5\u982d,*,PART,_",
+    "58": "p,\u52a9\u8a5e,\u63a5\u7d9a,\u4e26\u5217,CCONJ,_",
+    "59": "p,\u52a9\u8a5e,\u63a5\u7d9a,\u4f53\u8a00\u5316,PART,_",
+    "60": "p,\u52a9\u8a5e,\u63a5\u7d9a,\u5c5e\u683c,SCONJ,_",
+    "61": "p,\u52a9\u8a5e,\u63d0\u793a,*,PART,_",
+    "62": "p,\u611f\u5606\u8a5e,*,*,INTJ,_",
+    "63": "p,\u63a5\u5c3e\u8f9e,*,*,PART,_",
+    "64": "s,\u6587\u5b57,*,*,SYM,_",
+    "65": "s,\u8a18\u53f7,\u4e00\u822c,*,SYM,_",
+    "66": "s,\u8a18\u53f7,\u53e5\u70b9,*,PUNCT,_",
+    "67": "s,\u8a18\u53f7,\u8aad\u70b9,*,PUNCT,_",
+    "68": "v,\u524d\u7f6e\u8a5e,\u57fa\u76e4,*,ADP,_",
+    "69": "v,\u524d\u7f6e\u8a5e,\u6e90\u6cc9,*,ADP,_",
+    "70": "v,\u524d\u7f6e\u8a5e,\u7d4c\u7531,*,ADP,_",
+    "71": "v,\u524d\u7f6e\u8a5e,\u95a2\u4fc2,*,ADP,_",
+    "72": "v,\u526f\u8a5e,\u5224\u65ad,\u63a8\u5b9a,ADV,_",
+    "73": "v,\u526f\u8a5e,\u5224\u65ad,\u78ba\u5b9a,ADV,_",
+    "74": "v,\u526f\u8a5e,\u5224\u65ad,\u9006\u63a5,ADV,_",
+    "75": "v,\u526f\u8a5e,\u5426\u5b9a,\u4f53\u8a00\u5426\u5b9a,ADV,Polarity=Neg",
+    "76": "v,\u526f\u8a5e,\u5426\u5b9a,\u6709\u754c,ADV,Polarity=Neg",
+    "77": "v,\u526f\u8a5e,\u5426\u5b9a,\u7121\u754c,ADV,Polarity=Neg",
+    "78": "v,\u526f\u8a5e,\u5426\u5b9a,\u7981\u6b62,ADV,Polarity=Neg",
+    "79": "v,\u526f\u8a5e,\u63cf\u5199,*,ADV,_",
+    "80": "v,\u526f\u8a5e,\u6642\u76f8,\u5909\u5316,ADV,AdvType=Tim",
+    "81": "v,\u526f\u8a5e,\u6642\u76f8,\u5b8c\u4e86,ADV,AdvType=Tim|Aspect=Perf",
+    "82": "v,\u526f\u8a5e,\u6642\u76f8,\u5c06\u6765,ADV,AdvType=Tim|Tense=Fut",
+    "83": "v,\u526f\u8a5e,\u6642\u76f8,\u6052\u5e38,ADV,AdvType=Tim",
+    "84": "v,\u526f\u8a5e,\u6642\u76f8,\u73fe\u5728,ADV,AdvType=Tim|Tense=Pres",
+    "85": "v,\u526f\u8a5e,\u6642\u76f8,\u7d42\u5c40,ADV,AdvType=Tim",
+    "86": "v,\u526f\u8a5e,\u6642\u76f8,\u7d99\u8d77,ADV,AdvType=Tim",
+    "87": "v,\u526f\u8a5e,\u6642\u76f8,\u7dca\u63a5,ADV,AdvType=Tim",
+    "88": "v,\u526f\u8a5e,\u6642\u76f8,\u904e\u53bb,ADV,AdvType=Tim|Tense=Past",
+    "89": "v,\u526f\u8a5e,\u7591\u554f,\u539f\u56e0,ADV,AdvType=Cau",
+    "90": "v,\u526f\u8a5e,\u7591\u554f,\u53cd\u8a9e,ADV,_",
+    "91": "v,\u526f\u8a5e,\u7591\u554f,\u6240\u5728,ADV,_",
+    "92": "v,\u526f\u8a5e,\u7a0b\u5ea6,\u3084\u3084\u9ad8\u5ea6,ADV,AdvType=Deg|Degree=Cmp",
+    "93": "v,\u526f\u8a5e,\u7a0b\u5ea6,\u6975\u5ea6,ADV,AdvType=Deg|Degree=Sup",
+    "94": "v,\u526f\u8a5e,\u7a0b\u5ea6,\u8efd\u5ea6,ADV,AdvType=Deg|Degree=Pos",
+    "95": "v,\u526f\u8a5e,\u7bc4\u56f2,\u5171\u540c,ADV,_",
+    "96": "v,\u526f\u8a5e,\u7bc4\u56f2,\u7dcf\u62ec,ADV,_",
+    "97": "v,\u526f\u8a5e,\u7bc4\u56f2,\u9650\u5b9a,ADV,_",
+    "98": "v,\u526f\u8a5e,\u983b\u5ea6,\u5076\u767a,ADV,_",
+    "99": "v,\u526f\u8a5e,\u983b\u5ea6,\u91cd\u8907,ADV,_",
+    "100": "v,\u526f\u8a5e,\u983b\u5ea6,\u983b\u7e41,ADV,_",
+    "101": "v,\u52a9\u52d5\u8a5e,\u53d7\u52d5,*,AUX,Voice=Pass",
+    "102": "v,\u52a9\u52d5\u8a5e,\u53ef\u80fd,*,AUX,Mood=Pot",
+    "103": "v,\u52a9\u52d5\u8a5e,\u5fc5\u8981,*,AUX,Mood=Nec",
+    "104": "v,\u52a9\u52d5\u8a5e,\u9858\u671b,*,AUX,Mood=Des",
+    "105": "v,\u52d5\u8a5e,\u5909\u5316,\u5236\u5ea6,VERB,_",
+    "106": "v,\u52d5\u8a5e,\u5909\u5316,\u6027\u8cea,VERB,_",
+    "107": "v,\u52d5\u8a5e,\u5909\u5316,\u751f\u7269,VERB,_",
+    "108": "v,\u52d5\u8a5e,\u5b58\u5728,\u5b58\u5728,VERB,Polarity=Neg",
+    "109": "v,\u52d5\u8a5e,\u5b58\u5728,\u5b58\u5728,VERB,VerbType=Cop",
+    "110": "v,\u52d5\u8a5e,\u5b58\u5728,\u5b58\u5728,VERB,_",
+    "111": "v,\u52d5\u8a5e,\u63cf\u5199,\u5883\u9047,VERB,Degree=Pos",
+    "112": "v,\u52d5\u8a5e,\u63cf\u5199,\u5f62\u8cea,VERB,Degree=Pos",
+    "113": "v,\u52d5\u8a5e,\u63cf\u5199,\u614b\u5ea6,VERB,Degree=Pos",
+    "114": "v,\u52d5\u8a5e,\u63cf\u5199,\u91cf,VERB,Degree=Pos",
+    "115": "v,\u52d5\u8a5e,\u884c\u70ba,\u4ea4\u6d41,VERB,_",
+    "116": "v,\u52d5\u8a5e,\u884c\u70ba,\u4f1d\u9054,VERB,_",
+    "117": "v,\u52d5\u8a5e,\u884c\u70ba,\u4f7f\u5f79,VERB,_",
+    "118": "v,\u52d5\u8a5e,\u884c\u70ba,\u5100\u793c,VERB,_",
+    "119": "v,\u52d5\u8a5e,\u884c\u70ba,\u5206\u985e,VERB,Degree=Equ",
+    "120": "v,\u52d5\u8a5e,\u884c\u70ba,\u52d5\u4f5c,VERB,_",
+    "121": "v,\u52d5\u8a5e,\u884c\u70ba,\u59ff\u52e2,VERB,_",
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+    "v,\u526f\u8a5e,\u983b\u5ea6,\u983b\u7e41,ADV,_": 100,
+    "v,\u52a9\u52d5\u8a5e,\u53d7\u52d5,*,AUX,Voice=Pass": 101,
+    "v,\u52a9\u52d5\u8a5e,\u53ef\u80fd,*,AUX,Mood=Pot": 102,
+    "v,\u52a9\u52d5\u8a5e,\u5fc5\u8981,*,AUX,Mood=Nec": 103,
+    "v,\u52a9\u52d5\u8a5e,\u9858\u671b,*,AUX,Mood=Des": 104,
+    "v,\u52d5\u8a5e,\u5909\u5316,\u5236\u5ea6,VERB,_": 105,
+    "v,\u52d5\u8a5e,\u5909\u5316,\u6027\u8cea,VERB,_": 106,
+    "v,\u52d5\u8a5e,\u5909\u5316,\u751f\u7269,VERB,_": 107,
+    "v,\u52d5\u8a5e,\u5b58\u5728,\u5b58\u5728,VERB,Polarity=Neg": 108,
+    "v,\u52d5\u8a5e,\u5b58\u5728,\u5b58\u5728,VERB,VerbType=Cop": 109,
+    "v,\u52d5\u8a5e,\u5b58\u5728,\u5b58\u5728,VERB,_": 110,
+    "v,\u52d5\u8a5e,\u63cf\u5199,\u5883\u9047,VERB,Degree=Pos": 111,
+    "v,\u52d5\u8a5e,\u63cf\u5199,\u5f62\u8cea,VERB,Degree=Pos": 112,
+    "v,\u52d5\u8a5e,\u63cf\u5199,\u614b\u5ea6,VERB,Degree=Pos": 113,
+    "v,\u52d5\u8a5e,\u63cf\u5199,\u91cf,VERB,Degree=Pos": 114,
+    "v,\u52d5\u8a5e,\u884c\u70ba,\u4ea4\u6d41,VERB,_": 115,
+    "v,\u52d5\u8a5e,\u884c\u70ba,\u4f1d\u9054,VERB,_": 116,
+    "v,\u52d5\u8a5e,\u884c\u70ba,\u4f7f\u5f79,VERB,_": 117,
+    "v,\u52d5\u8a5e,\u884c\u70ba,\u5100\u793c,VERB,_": 118,
+    "v,\u52d5\u8a5e,\u884c\u70ba,\u5206\u985e,VERB,Degree=Equ": 119,
+    "v,\u52d5\u8a5e,\u884c\u70ba,\u52d5\u4f5c,VERB,_": 120,
+    "v,\u52d5\u8a5e,\u884c\u70ba,\u59ff\u52e2,VERB,_": 121,
+    "v,\u52d5\u8a5e,\u884c\u70ba,\u5f79\u5272,VERB,_": 122,
+    "v,\u52d5\u8a5e,\u884c\u70ba,\u5f97\u5931,VERB,_": 123,
+    "v,\u52d5\u8a5e,\u884c\u70ba,\u614b\u5ea6,VERB,_": 124,
+    "v,\u52d5\u8a5e,\u884c\u70ba,\u751f\u7523,VERB,_": 125,
+    "v,\u52d5\u8a5e,\u884c\u70ba,\u79fb\u52d5,VERB,_": 126,
+    "v,\u52d5\u8a5e,\u884c\u70ba,\u8a2d\u7f6e,VERB,_": 127,
+    "v,\u52d5\u8a5e,\u884c\u70ba,\u98f2\u98df,VERB,_": 128
   },
   "layer_norm_eps": 1e-05,
   "max_position_embeddings": 514,
@@ -284,7 +284,7 @@
   "position_embedding_type": "absolute",
   "tokenizer_class": "BertTokenizer",
   "torch_dtype": "float32",
-  "transformers_version": "4.9.2",
+  "transformers_version": "4.11.3",
   "type_vocab_size": 1,
   "use_cache": true,
   "vocab_size": 23292

suparkanbun/models/guwenbert-large.pos/filesize.txt

@@ -1,2 +1,2 @@
-pytorch_model.bin 1307456849
-guwenbert-large.supar 1358476727
+pytorch_model.bin 1307456848
+guwenbert-large.supar 1358552571

suparkanbun/models/guwenbert-large.pos/guwenbert-large.supar

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:0f0040a8bf03e6e15bb0854ea911d5e4f0533d5f057fd0cb511e41a89872ee29
-size 1358476727
+oid sha256:482a135799b79f43a266a9e16df04852840be433bd23058ce7756a0c7e79cd1a
+size 1358552571

suparkanbun/models/guwenbert-large.pos/pytorch_model.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:bc56f044840d62fd4f332d76d81275f04ecd66509f1699eadc5ce9353a0b2385
-size 1307456849
+oid sha256:bc0d5760a0cc8b9832e4555be3292af45d964938321c71ca684c4942f569d1a1
+size 1307456848

suparkanbun/models/guwenbert-large.pos/tokenizer_config.json

@@ -1 +1 @@
-{"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "special_tokens_map_file": null, "name_or_path": "ethanyt/guwenbert-large", "do_basic_tokenize": true, "never_split": null, "tokenizer_class": "BertTokenizer"}
+{"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "add_prefix_space": true, "special_tokens_map_file": null, "name_or_path": "ethanyt/guwenbert-large", "do_basic_tokenize": true, "never_split": null, "tokenizer_class": "BertTokenizer"}

suparkanbun/models/lzh_kyoto.conllu

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:86c46887798cd5d93f500ef99674897876501177366ad2b3e4ad861f3a362beb
-size 24744523
+oid sha256:96b0cb456a53f665127e5abc54e5addf87d6d2f78ae2b60f86bffca3f1e1e361
+size 27347302

suparkanbun/models/mkmodel.sh

@@ -1,6 +1,6 @@
 #! /bin/sh
-# pip3 install transformers seqeval datasets supar
-test -f run_ner.py || curl -LO https://raw.githubusercontent.com/huggingface/transformers/v4.0.1/examples/token-classification/run_ner.py
+# pip3 install 'transformers>=4.10.0' seqeval datasets supar==1.1.3
+test -f run_ner.py || curl -LO https://raw.githubusercontent.com/huggingface/transformers/v`pip3 list | sed -n 's/^transformers *\([^ ]*\) *$/\1/p'`/examples/pytorch/token-classification/run_ner.py
 
 python3 -c '
 from suparkanbun.simplify import simplify
@@ -73,13 +73,13 @@ while True:
     printf("%s\n",$0)>"validPOS.json";
 }'
 sed 's/^.*"tags":\[//' trainPOS.json | tr '"' '\012' | sort -u | egrep '^[nvps],' > labelPOS.txt
-if [ ! -d guwenbert-base.pos ]
+if [ ! -f guwenbert-base.pos/pytorch_model.bin ]
 then mkdir -p guwenbert-base.pos
-     python3 run_ner.py --model_name_or_path ethanyt/guwenbert-base --train_file trainPOS.json --validation_file validPOS.json --output_dir guwenbert-base.pos --do_train --do_eval
+     python3 run_ner.py --model_name_or_path ethanyt/guwenbert-base --train_file trainPOS.json --validation_file validPOS.json --output_dir guwenbert-base.pos --do_train --do_eval --overwrite_output_dir
 fi
-if [ ! -d guwenbert-large.pos ]
+if [ ! -f guwenbert-large.pos/pytorch_model.bin ]
 then mkdir -p guwenbert-large.pos
-     python3 run_ner.py --model_name_or_path ethanyt/guwenbert-large --train_file trainPOS.json --validation_file validPOS.json --output_dir guwenbert-large.pos --do_train --do_eval
+     python3 run_ner.py --model_name_or_path ethanyt/guwenbert-large --train_file trainPOS.json --validation_file validPOS.json --output_dir guwenbert-large.pos --do_train --do_eval --overwrite_output_dir --per_device_train_batch_size=4 --per_device_eval_batch_size=4
 fi
 
 nawk '
@@ -139,13 +139,13 @@ while True:
     printf("%s\n",$0)>"validDanku.json";
 }'
 sed 's/^.*"tags":\[//' trainDanku.json | tr '"' '\012' | sort -u | egrep '^[A-Z]' > labelDanku.txt
-if [ ! -d guwenbert-base.danku ]
+if [ ! -f guwenbert-base.danku/pytorch_model.bin ]
 then mkdir -p guwenbert-base.danku
-     python3 run_ner.py --model_name_or_path ethanyt/guwenbert-base --train_file trainDanku.json --validation_file validDanku.json --output_dir guwenbert-base.danku --do_train --do_eval
+     python3 run_ner.py --model_name_or_path ethanyt/guwenbert-base --train_file trainDanku.json --validation_file validDanku.json --output_dir guwenbert-base.danku --do_train --do_eval --overwrite_output_dir
 fi
-if [ ! -d guwenbert-large.danku ]
+if [ ! -f guwenbert-large.danku/pytorch_model.bin ]
 then mkdir -p guwenbert-large.danku
-     python3 run_ner.py --model_name_or_path ethanyt/guwenbert-large --train_file trainDanku.json --validation_file validDanku.json --output_dir guwenbert-large.danku --do_train --do_eval
+     python3 run_ner.py --model_name_or_path ethanyt/guwenbert-large --train_file trainDanku.json --validation_file validDanku.json --output_dir guwenbert-large.danku --do_train --do_eval --overwrite_output_dir --per_device_train_batch_size=4 --per_device_eval_batch_size=4
 fi
 
 python3 -c '
@@ -213,13 +213,13 @@ while True:
   else
     printf("%s\n",$0)>>"validPOS.json";
 }'
-if [ ! -d roberta-classical-chinese-base-char.pos ]
+if [ ! -f roberta-classical-chinese-base-char.pos/pytorch_model.bin ]
 then mkdir -p roberta-classical-chinese-base-char.pos
-     python3 run_ner.py --model_name_or_path KoichiYasuoka/roberta-classical-chinese-base-char --train_file trainPOS.json --validation_file validPOS.json --output_dir roberta-classical-chinese-base-char.pos --do_train --do_eval
+     python3 run_ner.py --model_name_or_path KoichiYasuoka/roberta-classical-chinese-base-char --train_file trainPOS.json --validation_file validPOS.json --output_dir roberta-classical-chinese-base-char.pos --do_train --do_eval --overwrite_output_dir
 fi
-if [ ! -d roberta-classical-chinese-large-char.pos ]
+if [ ! -f roberta-classical-chinese-large-char.pos/pytorch_model.bin ]
 then mkdir -p roberta-classical-chinese-large-char.pos
-     python3 run_ner.py --model_name_or_path KoichiYasuoka/roberta-classical-chinese-large-char --train_file trainPOS.json --validation_file validPOS.json --output_dir roberta-classical-chinese-large-char.pos --do_train --do_eval
+     python3 run_ner.py --model_name_or_path KoichiYasuoka/roberta-classical-chinese-large-char --train_file trainPOS.json --validation_file validPOS.json --output_dir roberta-classical-chinese-large-char.pos --do_train --do_eval --overwrite_output_dir --per_device_train_batch_size=4 --per_device_eval_batch_size=4
 fi
 
 nawk '
@@ -278,13 +278,13 @@ while True:
   else
     printf("%s\n",$0)>>"validDanku.json";
 }'
-if [ ! -d roberta-classical-chinese-base-char.danku ]
+if [ ! -f roberta-classical-chinese-base-char.danku/pytorch_model.bin ]
 then mkdir -p roberta-classical-chinese-base-char.danku
-     python3 run_ner.py --model_name_or_path KoichiYasuoka/roberta-classical-chinese-base-char --train_file trainDanku.json --validation_file validDanku.json --output_dir roberta-classical-chinese-base-char.danku --do_train --do_eval
+     python3 run_ner.py --model_name_or_path KoichiYasuoka/roberta-classical-chinese-base-char --train_file trainDanku.json --validation_file validDanku.json --output_dir roberta-classical-chinese-base-char.danku --do_train --do_eval --overwrite_output_dir
 fi
-if [ ! -d roberta-classical-chinese-large-char.danku ]
+if [ ! -f roberta-classical-chinese-large-char.danku/pytorch_model.bin ]
 then mkdir -p roberta-classical-chinese-large-char.danku
-     python3 run_ner.py --model_name_or_path KoichiYasuoka/roberta-classical-chinese-large-char --train_file trainDanku.json --validation_file validDanku.json --output_dir roberta-classical-chinese-large-char.danku --do_train --do_eval
+     python3 run_ner.py --model_name_or_path KoichiYasuoka/roberta-classical-chinese-large-char --train_file trainDanku.json --validation_file validDanku.json --output_dir roberta-classical-chinese-large-char.danku --do_train --do_eval --overwrite_output_dir --per_device_train_batch_size=4 --per_device_eval_batch_size=4
 fi
 
 nawk '
@@ -294,13 +294,13 @@ nawk '
   else
     printf("%s\n",$0)>"validPOS.json";
 }' traditionalPOS.json
-if [ ! -d sikubert.pos ]
+if [ ! -f sikubert.pos/pytorch_model.bin ]
 then mkdir -p sikubert.pos
-     python3 run_ner.py --model_name_or_path SIKU-BERT/sikubert --train_file trainPOS.json --validation_file validPOS.json --output_dir sikubert.pos --do_train --do_eval
+     python3 run_ner.py --model_name_or_path SIKU-BERT/sikubert --train_file trainPOS.json --validation_file validPOS.json --output_dir sikubert.pos --do_train --do_eval --overwrite_output_dir
 fi
-if [ ! -d sikuroberta.pos ]
+if [ ! -f sikuroberta.pos/pytorch_model.bin ]
 then mkdir -p sikuroberta.pos
-     python3 run_ner.py --model_name_or_path SIKU-BERT/sikuroberta --train_file trainPOS.json --validation_file validPOS.json --output_dir sikuroberta.pos --do_train --do_eval
+     python3 run_ner.py --model_name_or_path SIKU-BERT/sikuroberta --train_file trainPOS.json --validation_file validPOS.json --output_dir sikuroberta.pos --do_train --do_eval --overwrite_output_dir
 fi
 
 nawk '
@@ -329,13 +329,13 @@ nawk '
   else
     printf("%s\n",$0)>"validDanku.json";
 }' traditionalDanku.json
-if [ ! -d sikubert.danku ]
+if [ ! -f sikubert.danku/pytorch_model.bin ]
 then mkdir -p sikubert.danku
-     python3 run_ner.py --model_name_or_path SIKU-BERT/sikubert --train_file trainDanku.json --validation_file validDanku.json --output_dir sikubert.danku --do_train --do_eval
+     python3 run_ner.py --model_name_or_path SIKU-BERT/sikubert --train_file trainDanku.json --validation_file validDanku.json --output_dir sikubert.danku --do_train --do_eval --overwrite_output_dir
 fi
-if [ ! -d sikuroberta.danku ]
+if [ ! -f sikuroberta.danku/pytorch_model.bin ]
 then mkdir -p sikuroberta.danku
-     python3 run_ner.py --model_name_or_path SIKU-BERT/sikuroberta --train_file trainDanku.json --validation_file validDanku.json --output_dir sikuroberta.danku --do_train --do_eval
+     python3 run_ner.py --model_name_or_path SIKU-BERT/sikuroberta --train_file trainDanku.json --validation_file validDanku.json --output_dir sikuroberta.danku --do_train --do_eval --overwrite_output_dir
 fi
 
 exit 0

suparkanbun/models/roberta-classical-chinese-base-char.danku/config.json

@@ -5,6 +5,7 @@
   ],
   "attention_probs_dropout_prob": 0.1,
   "bos_token_id": 0,
+  "classifier_dropout": null,
   "eos_token_id": 2,
   "finetuning_task": "ner",
   "gradient_checkpointing": false,
@@ -12,22 +13,22 @@
   "hidden_dropout_prob": 0.1,
   "hidden_size": 768,
   "id2label": {
-    "0": "LABEL_0",
-    "1": "LABEL_1",
-    "2": "LABEL_2",
-    "3": "LABEL_3",
-    "4": "LABEL_4",
-    "5": "LABEL_5"
+    "0": "B",
+    "1": "E",
+    "2": "E2",
+    "3": "E3",
+    "4": "M",
+    "5": "S"
   },
   "initializer_range": 0.02,
   "intermediate_size": 3072,
   "label2id": {
-    "LABEL_0": 0,
-    "LABEL_1": 1,
-    "LABEL_2": 2,
-    "LABEL_3": 3,
-    "LABEL_4": 4,
-    "LABEL_5": 5
+    "B": 0,
+    "E": 1,
+    "E2": 2,
+    "E3": 3,
+    "M": 4,
+    "S": 5
   },
   "layer_norm_eps": 1e-05,
   "max_position_embeddings": 514,
@@ -38,7 +39,7 @@
   "position_embedding_type": "absolute",
   "tokenizer_class": "BertTokenizer",
   "torch_dtype": "float32",
-  "transformers_version": "4.9.2",
+  "transformers_version": "4.11.3",
   "type_vocab_size": 1,
   "use_cache": true,
   "vocab_size": 26318

suparkanbun/models/roberta-classical-chinese-base-char.danku/filesize.txt

@@ -1 +1 @@
-pytorch_model.bin 422761231
+pytorch_model.bin 422761232

suparkanbun/models/roberta-classical-chinese-base-char.danku/pytorch_model.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:2750fabf9eb6b675cb20cceb0b5326b300d5536642daa1fac426ec136d8f6533
-size 422761231
+oid sha256:f4f75f8201c4ddae696bef91c824de7a851cdc7fbf3243832fa65425e3556c99
+size 422761232

suparkanbun/models/roberta-classical-chinese-base-char.danku/tokenizer_config.json

@@ -1 +1 @@
-{"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "special_tokens_map_file": null, "name_or_path": "KoichiYasuoka/roberta-classical-chinese-base-char", "do_basic_tokenize": true, "never_split": null, "tokenizer_class": "BertTokenizer"}
+{"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "special_tokens_map_file": null, "model_max_length": 512, "do_basic_tokenize": true, "never_split": null, "add_prefix_space": true, "name_or_path": "KoichiYasuoka/roberta-classical-chinese-base-char", "tokenizer_class": "BertTokenizer"}

suparkanbun/models/roberta-classical-chinese-large-char.danku/config.json

@@ -5,6 +5,7 @@
   ],
   "attention_probs_dropout_prob": 0.1,
   "bos_token_id": 0,
+  "classifier_dropout": null,
   "eos_token_id": 2,
   "finetuning_task": "ner",
   "gradient_checkpointing": false,
@@ -12,22 +13,22 @@
   "hidden_dropout_prob": 0.1,
   "hidden_size": 1024,
   "id2label": {
-    "0": "LABEL_0",
-    "1": "LABEL_1",
-    "2": "LABEL_2",
-    "3": "LABEL_3",
-    "4": "LABEL_4",
-    "5": "LABEL_5"
+    "0": "B",
+    "1": "E",
+    "2": "E2",
+    "3": "E3",
+    "4": "M",
+    "5": "S"
   },
   "initializer_range": 0.02,
   "intermediate_size": 4096,
   "label2id": {
-    "LABEL_0": 0,
-    "LABEL_1": 1,
-    "LABEL_2": 2,
-    "LABEL_3": 3,
-    "LABEL_4": 4,
-    "LABEL_5": 5
+    "B": 0,
+    "E": 1,
+    "E2": 2,
+    "E3": 3,
+    "M": 4,
+    "S": 5
   },
   "layer_norm_eps": 1e-05,
   "max_position_embeddings": 514,
@@ -38,7 +39,7 @@
   "position_embedding_type": "absolute",
   "tokenizer_class": "BertTokenizer",
   "torch_dtype": "float32",
-  "transformers_version": "4.9.2",
+  "transformers_version": "4.11.3",
   "type_vocab_size": 1,
   "use_cache": true,
   "vocab_size": 26318

suparkanbun/models/roberta-classical-chinese-large-char.danku/filesize.txt

@@ -1 +1 @@
-pytorch_model.bin 1319347030
+pytorch_model.bin 1319347026

suparkanbun/models/roberta-classical-chinese-large-char.danku/pytorch_model.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:34662292a39654b4b34c71a4c9da26b578e9ae251a393abd798bde9850744b54
-size 1319347030
+oid sha256:b58082715c5ad717f2404990fbe7a38aa220753e9ad52877b4c378a09ff5345b
+size 1319347026

suparkanbun/models/roberta-classical-chinese-large-char.danku/tokenizer_config.json

@@ -1 +1 @@
-{"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "special_tokens_map_file": null, "do_basic_tokenize": true, "never_split": null, "name_or_path": "KoichiYasuoka/roberta-classical-chinese-large-char", "tokenizer_class": "BertTokenizer"}
+{"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "special_tokens_map_file": null, "model_max_length": 512, "do_basic_tokenize": true, "never_split": null, "add_prefix_space": true, "name_or_path": "KoichiYasuoka/roberta-classical-chinese-large-char", "tokenizer_class": "BertTokenizer"}

suparkanbun/models/roberta-classical-chinese-large-char.pos/config.json

@@ -5,6 +5,7 @@
   ],
   "attention_probs_dropout_prob": 0.1,
   "bos_token_id": 0,
+  "classifier_dropout": null,
   "eos_token_id": 2,
   "finetuning_task": "ner",
   "gradient_checkpointing": false,
@@ -12,268 +13,268 @@
   "hidden_dropout_prob": 0.1,
   "hidden_size": 1024,
   "id2label": {
-    "0": "n,代名詞,人称,他,PRON,Person=1|PronType=Prs",
-    "1": "n,代名詞,人称,他,PRON,Person=2|PronType=Prs",
-    "2": "n,代名詞,人称,他,PRON,Person=3|PronType=Prs",
-    "3": "n,代名詞,人称,他,PRON,PronType=Prs",
-    "4": "n,代名詞,人称,他,PRON,PronType=Prs|Reflex=Yes",
-    "5": "n,代名詞,人称,止格,PRON,Person=1|PronType=Prs",
-    "6": "n,代名詞,人称,止格,PRON,Person=2|PronType=Prs",
-    "7": "n,代名詞,人称,止格,PRON,Person=3|PronType=Prs",
-    "8": "n,代名詞,人称,止格,PRON,PronType=Prs",
-    "9": "n,代名詞,人称,起格,PRON,Person=1|PronType=Prs",
-    "10": "n,代名詞,人称,起格,PRON,Person=2|PronType=Prs",
-    "11": "n,代名詞,人称,起格,PRON,Person=3|PronType=Prs",
-    "12": "n,代名詞,人称,起格,PRON,PronType=Prs",
-    "13": "n,代名詞,指示,*,PRON,PronType=Dem",
-    "14": "n,代名詞,疑問,*,PRON,PronType=Int",
-    "15": "n,名詞,不可譲,属性,NOUN,_",
-    "16": "n,名詞,不可譲,疾病,NOUN,_",
-    "17": "n,名詞,不可譲,身体,NOUN,_",
-    "18": "n,名詞,主体,動物,NOUN,_",
-    "19": "n,名詞,主体,国名,PROPN,Case=Loc|NameType=Nat",
-    "20": "n,名詞,主体,書物,NOUN,_",
-    "21": "n,名詞,主体,機関,NOUN,_",
-    "22": "n,名詞,主体,集団,NOUN,_",
-    "23": "n,名詞,人,その他の人名,PROPN,NameType=Prs",
-    "24": "n,名詞,人,人,NOUN,_",
-    "25": "n,名詞,人,名,PROPN,NameType=Giv",
-    "26": "n,名詞,人,姓氏,PROPN,NameType=Sur",
-    "27": "n,名詞,人,役割,NOUN,_",
-    "28": "n,名詞,人,複合的人名,PROPN,NameType=Prs",
-    "29": "n,名詞,人,関係,NOUN,_",
-    "30": "n,名詞,制度,儀礼,NOUN,_",
-    "31": "n,名詞,制度,場,NOUN,Case=Loc",
-    "32": "n,名詞,可搬,乗り物,NOUN,_",
-    "33": "n,名詞,可搬,伝達,NOUN,_",
-    "34": "n,名詞,可搬,成果物,NOUN,_",
-    "35": "n,名詞,可搬,糧食,NOUN,_",
-    "36": "n,名詞,可搬,道具,NOUN,_",
-    "37": "n,名詞,固定物,地名,PROPN,Case=Loc|NameType=Geo",
-    "38": "n,名詞,固定物,地形,NOUN,Case=Loc",
-    "39": "n,名詞,固定物,建造物,NOUN,Case=Loc",
-    "40": "n,名詞,固定物,樹木,NOUN,_",
-    "41": "n,名詞,固定物,関係,NOUN,Case=Loc",
-    "42": "n,名詞,外観,人,NOUN,_",
-    "43": "n,名詞,天象,天文,NOUN,_",
-    "44": "n,名詞,天象,怪異,NOUN,_",
-    "45": "n,名詞,天象,気象,NOUN,_",
-    "46": "n,名詞,度量衡,*,NOUN,NounType=Clf",
-    "47": "n,名詞,思考,*,NOUN,_",
-    "48": "n,名詞,描写,形質,NOUN,_",
-    "49": "n,名詞,描写,態度,NOUN,_",
-    "50": "n,名詞,数量,*,NOUN,_",
-    "51": "n,名詞,時,*,NOUN,Case=Tem",
-    "52": "n,名詞,行為,*,NOUN,_",
-    "53": "n,数詞,干支,*,NUM,NumType=Ord",
-    "54": "n,数詞,数,*,NUM,_",
-    "55": "n,数詞,数字,*,NUM,_",
-    "56": "p,助詞,句末,*,PART,_",
-    "57": "p,助詞,句頭,*,PART,_",
-    "58": "p,助詞,接続,並列,CCONJ,_",
-    "59": "p,助詞,接続,体言化,PART,_",
-    "60": "p,助詞,接続,属格,SCONJ,_",
-    "61": "p,助詞,提示,*,PART,_",
-    "62": "p,感嘆詞,*,*,INTJ,_",
-    "63": "p,接尾辞,*,*,PART,_",
-    "64": "s,文字,*,*,SYM,_",
-    "65": "s,記号,一般,*,SYM,_",
-    "66": "s,記号,句点,*,PUNCT,_",
-    "67": "s,記号,読点,*,PUNCT,_",
-    "68": "v,前置詞,基盤,*,ADP,_",
-    "69": "v,前置詞,源泉,*,ADP,_",
-    "70": "v,前置詞,経由,*,ADP,_",
-    "71": "v,前置詞,関係,*,ADP,_",
-    "72": "v,副詞,判断,推定,ADV,_",
-    "73": "v,副詞,判断,確定,ADV,_",
-    "74": "v,副詞,判断,逆接,ADV,_",
-    "75": "v,副詞,否定,体言否定,ADV,Polarity=Neg",
-    "76": "v,副詞,否定,有界,ADV,Polarity=Neg",
-    "77": "v,副詞,否定,無界,ADV,Polarity=Neg",
-    "78": "v,副詞,否定,禁止,ADV,Polarity=Neg",
-    "79": "v,副詞,描写,*,ADV,_",
-    "80": "v,副詞,時相,変化,ADV,AdvType=Tim",
-    "81": "v,副詞,時相,完了,ADV,AdvType=Tim|Aspect=Perf",
-    "82": "v,副詞,時相,将来,ADV,AdvType=Tim|Tense=Fut",
-    "83": "v,副詞,時相,恒常,ADV,AdvType=Tim",
-    "84": "v,副詞,時相,現在,ADV,AdvType=Tim|Tense=Pres",
-    "85": "v,副詞,時相,終局,ADV,AdvType=Tim",
-    "86": "v,副詞,時相,継起,ADV,AdvType=Tim",
-    "87": "v,副詞,時相,緊接,ADV,AdvType=Tim",
-    "88": "v,副詞,時相,過去,ADV,AdvType=Tim|Tense=Past",
-    "89": "v,副詞,疑問,原因,ADV,AdvType=Cau",
-    "90": "v,副詞,疑問,反語,ADV,_",
-    "91": "v,副詞,疑問,所在,ADV,_",
-    "92": "v,副詞,程度,やや高度,ADV,AdvType=Deg|Degree=Cmp",
-    "93": "v,副詞,程度,極度,ADV,AdvType=Deg|Degree=Sup",
-    "94": "v,副詞,程度,軽度,ADV,AdvType=Deg|Degree=Pos",
-    "95": "v,副詞,範囲,共同,ADV,_",
-    "96": "v,副詞,範囲,総括,ADV,_",
-    "97": "v,副詞,範囲,限定,ADV,_",
-    "98": "v,副詞,頻度,偶発,ADV,_",
-    "99": "v,副詞,頻度,重複,ADV,_",
-    "100": "v,副詞,頻度,頻繁,ADV,_",
-    "101": "v,助動詞,受動,*,AUX,Voice=Pass",
-    "102": "v,助動詞,可能,*,AUX,Mood=Pot",
-    "103": "v,助動詞,必要,*,AUX,Mood=Nec",
-    "104": "v,助動詞,願望,*,AUX,Mood=Des",
-    "105": "v,動詞,変化,制度,VERB,_",
-    "106": "v,動詞,変化,性質,VERB,_",
-    "107": "v,動詞,変化,生物,VERB,_",
-    "108": "v,動詞,存在,存在,VERB,Polarity=Neg",
-    "109": "v,動詞,存在,存在,VERB,VerbType=Cop",
-    "110": "v,動詞,存在,存在,VERB,_",
-    "111": "v,動詞,描写,境遇,VERB,Degree=Pos",
-    "112": "v,動詞,描写,形質,VERB,Degree=Pos",
-    "113": "v,動詞,描写,態度,VERB,Degree=Pos",
-    "114": "v,動詞,描写,量,VERB,Degree=Pos",
-    "115": "v,動詞,行為,交流,VERB,_",
-    "116": "v,動詞,行為,伝達,VERB,_",
-    "117": "v,動詞,行為,使役,VERB,_",
-    "118": "v,動詞,行為,儀礼,VERB,_",
-    "119": "v,動詞,行為,分類,VERB,Degree=Equ",
-    "120": "v,動詞,行為,動作,VERB,_",
-    "121": "v,動詞,行為,姿勢,VERB,_",
-    "122": "v,動詞,行為,役割,VERB,_",
-    "123": "v,動詞,行為,得失,VERB,_",
-    "124": "v,動詞,行為,態度,VERB,_",
-    "125": "v,動詞,行為,生産,VERB,_",
-    "126": "v,動詞,行為,移動,VERB,_",
-    "127": "v,動詞,行為,設置,VERB,_",
-    "128": "v,動詞,行為,飲食,VERB,_"
+    "0": "n,\u4ee3\u540d\u8a5e,\u4eba\u79f0,\u4ed6,PRON,Person=1|PronType=Prs",
+    "1": "n,\u4ee3\u540d\u8a5e,\u4eba\u79f0,\u4ed6,PRON,Person=2|PronType=Prs",
+    "2": "n,\u4ee3\u540d\u8a5e,\u4eba\u79f0,\u4ed6,PRON,Person=3|PronType=Prs",
+    "3": "n,\u4ee3\u540d\u8a5e,\u4eba\u79f0,\u4ed6,PRON,PronType=Prs",
+    "4": "n,\u4ee3\u540d\u8a5e,\u4eba\u79f0,\u4ed6,PRON,PronType=Prs|Reflex=Yes",
+    "5": "n,\u4ee3\u540d\u8a5e,\u4eba\u79f0,\u6b62\u683c,PRON,Person=1|PronType=Prs",
+    "6": "n,\u4ee3\u540d\u8a5e,\u4eba\u79f0,\u6b62\u683c,PRON,Person=2|PronType=Prs",
+    "7": "n,\u4ee3\u540d\u8a5e,\u4eba\u79f0,\u6b62\u683c,PRON,Person=3|PronType=Prs",
+    "8": "n,\u4ee3\u540d\u8a5e,\u4eba\u79f0,\u8d77\u683c,PRON,Person=1|PronType=Prs",
+    "9": "n,\u4ee3\u540d\u8a5e,\u4eba\u79f0,\u8d77\u683c,PRON,Person=2|PronType=Prs",
+    "10": "n,\u4ee3\u540d\u8a5e,\u4eba\u79f0,\u8d77\u683c,PRON,Person=3|PronType=Prs",
+    "11": "n,\u4ee3\u540d\u8a5e,\u4eba\u79f0,\u8d77\u683c,PRON,PronType=Prs",
+    "12": "n,\u4ee3\u540d\u8a5e,\u6307\u793a,*,PRON,PronType=Dem",
+    "13": "n,\u4ee3\u540d\u8a5e,\u7591\u554f,*,PRON,PronType=Int",
+    "14": "n,\u540d\u8a5e,\u4e0d\u53ef\u8b72,\u5c5e\u6027,NOUN,_",
+    "15": "n,\u540d\u8a5e,\u4e0d\u53ef\u8b72,\u75be\u75c5,NOUN,_",
+    "16": "n,\u540d\u8a5e,\u4e0d\u53ef\u8b72,\u8eab\u4f53,NOUN,_",
+    "17": "n,\u540d\u8a5e,\u4e3b\u4f53,\u52d5\u7269,NOUN,_",
+    "18": "n,\u540d\u8a5e,\u4e3b\u4f53,\u56fd\u540d,PROPN,Case=Loc|NameType=Nat",
+    "19": "n,\u540d\u8a5e,\u4e3b\u4f53,\u66f8\u7269,NOUN,_",
+    "20": "n,\u540d\u8a5e,\u4e3b\u4f53,\u6a5f\u95a2,NOUN,_",
+    "21": "n,\u540d\u8a5e,\u4e3b\u4f53,\u795e\u4ecf,NOUN,_",
+    "22": "n,\u540d\u8a5e,\u4e3b\u4f53,\u96c6\u56e3,NOUN,_",
+    "23": "n,\u540d\u8a5e,\u4eba,\u305d\u306e\u4ed6\u306e\u4eba\u540d,PROPN,NameType=Prs",
+    "24": "n,\u540d\u8a5e,\u4eba,\u4eba,NOUN,_",
+    "25": "n,\u540d\u8a5e,\u4eba,\u540d,PROPN,NameType=Giv",
+    "26": "n,\u540d\u8a5e,\u4eba,\u59d3\u6c0f,PROPN,NameType=Sur",
+    "27": "n,\u540d\u8a5e,\u4eba,\u5f79\u5272,NOUN,_",
+    "28": "n,\u540d\u8a5e,\u4eba,\u8907\u5408\u7684\u4eba\u540d,PROPN,NameType=Prs",
+    "29": "n,\u540d\u8a5e,\u4eba,\u95a2\u4fc2,NOUN,_",
+    "30": "n,\u540d\u8a5e,\u5236\u5ea6,\u5100\u793c,NOUN,_",
+    "31": "n,\u540d\u8a5e,\u5236\u5ea6,\u5834,NOUN,Case=Loc",
+    "32": "n,\u540d\u8a5e,\u53ef\u642c,\u4e57\u308a\u7269,NOUN,_",
+    "33": "n,\u540d\u8a5e,\u53ef\u642c,\u4f1d\u9054,NOUN,_",
+    "34": "n,\u540d\u8a5e,\u53ef\u642c,\u6210\u679c\u7269,NOUN,_",
+    "35": "n,\u540d\u8a5e,\u53ef\u642c,\u7ce7\u98df,NOUN,_",
+    "36": "n,\u540d\u8a5e,\u53ef\u642c,\u9053\u5177,NOUN,_",
+    "37": "n,\u540d\u8a5e,\u56fa\u5b9a\u7269,\u5730\u540d,PROPN,Case=Loc|NameType=Geo",
+    "38": "n,\u540d\u8a5e,\u56fa\u5b9a\u7269,\u5730\u5f62,NOUN,Case=Loc",
+    "39": "n,\u540d\u8a5e,\u56fa\u5b9a\u7269,\u5efa\u9020\u7269,NOUN,Case=Loc",
+    "40": "n,\u540d\u8a5e,\u56fa\u5b9a\u7269,\u6a39\u6728,NOUN,_",
+    "41": "n,\u540d\u8a5e,\u56fa\u5b9a\u7269,\u95a2\u4fc2,NOUN,Case=Loc",
+    "42": "n,\u540d\u8a5e,\u5916\u89b3,\u4eba,NOUN,_",
+    "43": "n,\u540d\u8a5e,\u5929\u8c61,\u5929\u6587,NOUN,_",
+    "44": "n,\u540d\u8a5e,\u5929\u8c61,\u602a\u7570,NOUN,_",
+    "45": "n,\u540d\u8a5e,\u5929\u8c61,\u6c17\u8c61,NOUN,_",
+    "46": "n,\u540d\u8a5e,\u5ea6\u91cf\u8861,*,NOUN,NounType=Clf",
+    "47": "n,\u540d\u8a5e,\u601d\u8003,*,NOUN,_",
+    "48": "n,\u540d\u8a5e,\u63cf\u5199,\u5f62\u8cea,NOUN,_",
+    "49": "n,\u540d\u8a5e,\u63cf\u5199,\u614b\u5ea6,NOUN,_",
+    "50": "n,\u540d\u8a5e,\u6570\u91cf,*,NOUN,_",
+    "51": "n,\u540d\u8a5e,\u6642,*,NOUN,Case=Tem",
+    "52": "n,\u540d\u8a5e,\u884c\u70ba,*,NOUN,_",
+    "53": "n,\u6570\u8a5e,\u5e72\u652f,*,NUM,NumType=Ord",
+    "54": "n,\u6570\u8a5e,\u6570,*,NUM,_",
+    "55": "n,\u6570\u8a5e,\u6570\u5b57,*,NUM,_",
+    "56": "p,\u52a9\u8a5e,\u53e5\u672b,*,PART,_",
+    "57": "p,\u52a9\u8a5e,\u53e5\u982d,*,PART,_",
+    "58": "p,\u52a9\u8a5e,\u63a5\u7d9a,\u4e26\u5217,CCONJ,_",
+    "59": "p,\u52a9\u8a5e,\u63a5\u7d9a,\u4f53\u8a00\u5316,PART,_",
+    "60": "p,\u52a9\u8a5e,\u63a5\u7d9a,\u5c5e\u683c,SCONJ,_",
+    "61": "p,\u52a9\u8a5e,\u63d0\u793a,*,PART,_",
+    "62": "p,\u611f\u5606\u8a5e,*,*,INTJ,_",
+    "63": "p,\u63a5\u5c3e\u8f9e,*,*,PART,_",
+    "64": "s,\u6587\u5b57,*,*,SYM,_",
+    "65": "s,\u8a18\u53f7,\u4e00\u822c,*,SYM,_",
+    "66": "s,\u8a18\u53f7,\u53e5\u70b9,*,PUNCT,_",
+    "67": "s,\u8a18\u53f7,\u8aad\u70b9,*,PUNCT,_",
+    "68": "v,\u524d\u7f6e\u8a5e,\u57fa\u76e4,*,ADP,_",
+    "69": "v,\u524d\u7f6e\u8a5e,\u6e90\u6cc9,*,ADP,_",
+    "70": "v,\u524d\u7f6e\u8a5e,\u7d4c\u7531,*,ADP,_",
+    "71": "v,\u524d\u7f6e\u8a5e,\u95a2\u4fc2,*,ADP,_",
+    "72": "v,\u526f\u8a5e,\u5224\u65ad,\u63a8\u5b9a,ADV,_",
+    "73": "v,\u526f\u8a5e,\u5224\u65ad,\u78ba\u5b9a,ADV,_",
+    "74": "v,\u526f\u8a5e,\u5224\u65ad,\u9006\u63a5,ADV,_",
+    "75": "v,\u526f\u8a5e,\u5426\u5b9a,\u4f53\u8a00\u5426\u5b9a,ADV,Polarity=Neg",
+    "76": "v,\u526f\u8a5e,\u5426\u5b9a,\u6709\u754c,ADV,Polarity=Neg",
+    "77": "v,\u526f\u8a5e,\u5426\u5b9a,\u7121\u754c,ADV,Polarity=Neg",
+    "78": "v,\u526f\u8a5e,\u5426\u5b9a,\u7981\u6b62,ADV,Polarity=Neg",
+    "79": "v,\u526f\u8a5e,\u63cf\u5199,*,ADV,_",
+    "80": "v,\u526f\u8a5e,\u6642\u76f8,\u5909\u5316,ADV,AdvType=Tim",
+    "81": "v,\u526f\u8a5e,\u6642\u76f8,\u5b8c\u4e86,ADV,AdvType=Tim|Aspect=Perf",
+    "82": "v,\u526f\u8a5e,\u6642\u76f8,\u5c06\u6765,ADV,AdvType=Tim|Tense=Fut",
+    "83": "v,\u526f\u8a5e,\u6642\u76f8,\u6052\u5e38,ADV,AdvType=Tim",
+    "84": "v,\u526f\u8a5e,\u6642\u76f8,\u73fe\u5728,ADV,AdvType=Tim|Tense=Pres",
+    "85": "v,\u526f\u8a5e,\u6642\u76f8,\u7d42\u5c40,ADV,AdvType=Tim",
+    "86": "v,\u526f\u8a5e,\u6642\u76f8,\u7d99\u8d77,ADV,AdvType=Tim",
+    "87": "v,\u526f\u8a5e,\u6642\u76f8,\u7dca\u63a5,ADV,AdvType=Tim",
+    "88": "v,\u526f\u8a5e,\u6642\u76f8,\u904e\u53bb,ADV,AdvType=Tim|Tense=Past",
+    "89": "v,\u526f\u8a5e,\u7591\u554f,\u539f\u56e0,ADV,AdvType=Cau",
+    "90": "v,\u526f\u8a5e,\u7591\u554f,\u53cd\u8a9e,ADV,_",
+    "91": "v,\u526f\u8a5e,\u7591\u554f,\u6240\u5728,ADV,_",
+    "92": "v,\u526f\u8a5e,\u7a0b\u5ea6,\u3084\u3084\u9ad8\u5ea6,ADV,AdvType=Deg|Degree=Cmp",
+    "93": "v,\u526f\u8a5e,\u7a0b\u5ea6,\u6975\u5ea6,ADV,AdvType=Deg|Degree=Sup",
+    "94": "v,\u526f\u8a5e,\u7a0b\u5ea6,\u8efd\u5ea6,ADV,AdvType=Deg|Degree=Pos",
+    "95": "v,\u526f\u8a5e,\u7bc4\u56f2,\u5171\u540c,ADV,_",
+    "96": "v,\u526f\u8a5e,\u7bc4\u56f2,\u7dcf\u62ec,ADV,_",
+    "97": "v,\u526f\u8a5e,\u7bc4\u56f2,\u9650\u5b9a,ADV,_",
+    "98": "v,\u526f\u8a5e,\u983b\u5ea6,\u5076\u767a,ADV,_",
+    "99": "v,\u526f\u8a5e,\u983b\u5ea6,\u91cd\u8907,ADV,_",
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suparkanbun/models/roberta-classical-chinese-large-char.pos/tokenizer_config.json

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suparkanbun/models/sikubert.danku/config.json

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suparkanbun/models/sikubert.danku/filesize.txt

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-    "0": "n,代名詞,人称,他,PRON,Person=1|PronType=Prs",
-    "1": "n,代名詞,人称,他,PRON,Person=2|PronType=Prs",
-    "2": "n,代名詞,人称,他,PRON,Person=3|PronType=Prs",
-    "3": "n,代名詞,人称,他,PRON,PronType=Prs",
-    "4": "n,代名詞,人称,他,PRON,PronType=Prs|Reflex=Yes",
-    "5": "n,代名詞,人称,止格,PRON,Person=1|PronType=Prs",
-    "6": "n,代名詞,人称,止格,PRON,Person=2|PronType=Prs",
-    "7": "n,代名詞,人称,止格,PRON,Person=3|PronType=Prs",
-    "8": "n,代名詞,人称,止格,PRON,PronType=Prs",
-    "9": "n,代名詞,人称,起格,PRON,Person=1|PronType=Prs",
-    "10": "n,代名詞,人称,起格,PRON,Person=2|PronType=Prs",
-    "11": "n,代名詞,人称,起格,PRON,Person=3|PronType=Prs",
-    "12": "n,代名詞,人称,起格,PRON,PronType=Prs",
-    "13": "n,代名詞,指示,*,PRON,PronType=Dem",
-    "14": "n,代名詞,疑問,*,PRON,PronType=Int",
-    "15": "n,名詞,不可譲,属性,NOUN,_",
-    "16": "n,名詞,不可譲,疾病,NOUN,_",
-    "17": "n,名詞,不可譲,身体,NOUN,_",
-    "18": "n,名詞,主体,動物,NOUN,_",
-    "19": "n,名詞,主体,国名,PROPN,Case=Loc|NameType=Nat",
-    "20": "n,名詞,主体,書物,NOUN,_",
-    "21": "n,名詞,主体,機関,NOUN,_",
-    "22": "n,名詞,主体,集団,NOUN,_",
-    "23": "n,名詞,人,その他の人名,PROPN,NameType=Prs",
-    "24": "n,名詞,人,人,NOUN,_",
-    "25": "n,名詞,人,名,PROPN,NameType=Giv",
-    "26": "n,名詞,人,姓氏,PROPN,NameType=Sur",
-    "27": "n,名詞,人,役割,NOUN,_",
-    "28": "n,名詞,人,複合的人名,PROPN,NameType=Prs",
-    "29": "n,名詞,人,関係,NOUN,_",
-    "30": "n,名詞,制度,儀礼,NOUN,_",
-    "31": "n,名詞,制度,場,NOUN,Case=Loc",
-    "32": "n,名詞,可搬,乗り物,NOUN,_",
-    "33": "n,名詞,可搬,伝達,NOUN,_",
-    "34": "n,名詞,可搬,成果物,NOUN,_",
-    "35": "n,名詞,可搬,糧食,NOUN,_",
-    "36": "n,名詞,可搬,道具,NOUN,_",
-    "37": "n,名詞,固定物,地名,PROPN,Case=Loc|NameType=Geo",
-    "38": "n,名詞,固定物,地形,NOUN,Case=Loc",
-    "39": "n,名詞,固定物,建造物,NOUN,Case=Loc",
-    "40": "n,名詞,固定物,樹木,NOUN,_",
-    "41": "n,名詞,固定物,関係,NOUN,Case=Loc",
-    "42": "n,名詞,外観,人,NOUN,_",
-    "43": "n,名詞,天象,天文,NOUN,_",
-    "44": "n,名詞,天象,怪異,NOUN,_",
-    "45": "n,名詞,天象,気象,NOUN,_",
-    "46": "n,名詞,度量衡,*,NOUN,NounType=Clf",
-    "47": "n,名詞,思考,*,NOUN,_",
-    "48": "n,名詞,描写,形質,NOUN,_",
-    "49": "n,名詞,描写,態度,NOUN,_",
-    "50": "n,名詞,数量,*,NOUN,_",
-    "51": "n,名詞,時,*,NOUN,Case=Tem",
-    "52": "n,名詞,行為,*,NOUN,_",
-    "53": "n,数詞,干支,*,NUM,NumType=Ord",
-    "54": "n,数詞,数,*,NUM,_",
-    "55": "n,数詞,数字,*,NUM,_",
-    "56": "p,助詞,句末,*,PART,_",
-    "57": "p,助詞,句頭,*,PART,_",
-    "58": "p,助詞,接続,並列,CCONJ,_",
-    "59": "p,助詞,接続,体言化,PART,_",
-    "60": "p,助詞,接続,属格,SCONJ,_",
-    "61": "p,助詞,提示,*,PART,_",
-    "62": "p,感嘆詞,*,*,INTJ,_",
-    "63": "p,接尾辞,*,*,PART,_",
-    "64": "s,文字,*,*,SYM,_",
-    "65": "s,記号,一般,*,SYM,_",
-    "66": "s,記号,句点,*,PUNCT,_",
-    "67": "s,記号,読点,*,PUNCT,_",
-    "68": "v,前置詞,基盤,*,ADP,_",
-    "69": "v,前置詞,源泉,*,ADP,_",
-    "70": "v,前置詞,経由,*,ADP,_",
-    "71": "v,前置詞,関係,*,ADP,_",
-    "72": "v,副詞,判断,推定,ADV,_",
-    "73": "v,副詞,判断,確定,ADV,_",
-    "74": "v,副詞,判断,逆接,ADV,_",
-    "75": "v,副詞,否定,体言否定,ADV,Polarity=Neg",
-    "76": "v,副詞,否定,有界,ADV,Polarity=Neg",
-    "77": "v,副詞,否定,無界,ADV,Polarity=Neg",
-    "78": "v,副詞,否定,禁止,ADV,Polarity=Neg",
-    "79": "v,副詞,描写,*,ADV,_",
-    "80": "v,副詞,時相,変化,ADV,AdvType=Tim",
-    "81": "v,副詞,時相,完了,ADV,AdvType=Tim|Aspect=Perf",
-    "82": "v,副詞,時相,将来,ADV,AdvType=Tim|Tense=Fut",
-    "83": "v,副詞,時相,恒常,ADV,AdvType=Tim",
-    "84": "v,副詞,時相,現在,ADV,AdvType=Tim|Tense=Pres",
-    "85": "v,副詞,時相,終局,ADV,AdvType=Tim",
-    "86": "v,副詞,時相,継起,ADV,AdvType=Tim",
-    "87": "v,副詞,時相,緊接,ADV,AdvType=Tim",
-    "88": "v,副詞,時相,過去,ADV,AdvType=Tim|Tense=Past",
-    "89": "v,副詞,疑問,原因,ADV,AdvType=Cau",
-    "90": "v,副詞,疑問,反語,ADV,_",
-    "91": "v,副詞,疑問,所在,ADV,_",
-    "92": "v,副詞,程度,やや高度,ADV,AdvType=Deg|Degree=Cmp",
-    "93": "v,副詞,程度,極度,ADV,AdvType=Deg|Degree=Sup",
-    "94": "v,副詞,程度,軽度,ADV,AdvType=Deg|Degree=Pos",
-    "95": "v,副詞,範囲,共同,ADV,_",
-    "96": "v,副詞,範囲,総括,ADV,_",
-    "97": "v,副詞,範囲,限定,ADV,_",
-    "98": "v,副詞,頻度,偶発,ADV,_",
-    "99": "v,副詞,頻度,重複,ADV,_",
-    "100": "v,副詞,頻度,頻繁,ADV,_",
-    "101": "v,助動詞,受動,*,AUX,Voice=Pass",
-    "102": "v,助動詞,可能,*,AUX,Mood=Pot",
-    "103": "v,助動詞,必要,*,AUX,Mood=Nec",
-    "104": "v,助動詞,願望,*,AUX,Mood=Des",
-    "105": "v,動詞,変化,制度,VERB,_",
-    "106": "v,動詞,変化,性質,VERB,_",
-    "107": "v,動詞,変化,生物,VERB,_",
-    "108": "v,動詞,存在,存在,VERB,Polarity=Neg",
-    "109": "v,動詞,存在,存在,VERB,VerbType=Cop",
-    "110": "v,動詞,存在,存在,VERB,_",
-    "111": "v,動詞,描写,境遇,VERB,Degree=Pos",
-    "112": "v,動詞,描写,形質,VERB,Degree=Pos",
-    "113": "v,動詞,描写,態度,VERB,Degree=Pos",
-    "114": "v,動詞,描写,量,VERB,Degree=Pos",
-    "115": "v,動詞,行為,交流,VERB,_",
-    "116": "v,動詞,行為,伝達,VERB,_",
-    "117": "v,動詞,行為,使役,VERB,_",
-    "118": "v,動詞,行為,儀礼,VERB,_",
-    "119": "v,動詞,行為,分類,VERB,Degree=Equ",
-    "120": "v,動詞,行為,動作,VERB,_",
-    "121": "v,動詞,行為,姿勢,VERB,_",
-    "122": "v,動詞,行為,役割,VERB,_",
-    "123": "v,動詞,行為,得失,VERB,_",
-    "124": "v,動詞,行為,態度,VERB,_",
-    "125": "v,動詞,行為,生産,VERB,_",
-    "126": "v,動詞,行為,移動,VERB,_",
-    "127": "v,動詞,行為,設置,VERB,_",
-    "128": "v,動詞,行為,飲食,VERB,_"
+    "0": "n,\u4ee3\u540d\u8a5e,\u4eba\u79f0,\u4ed6,PRON,Person=1|PronType=Prs",
+    "1": "n,\u4ee3\u540d\u8a5e,\u4eba\u79f0,\u4ed6,PRON,Person=2|PronType=Prs",
+    "2": "n,\u4ee3\u540d\u8a5e,\u4eba\u79f0,\u4ed6,PRON,Person=3|PronType=Prs",
+    "3": "n,\u4ee3\u540d\u8a5e,\u4eba\u79f0,\u4ed6,PRON,PronType=Prs",
+    "4": "n,\u4ee3\u540d\u8a5e,\u4eba\u79f0,\u4ed6,PRON,PronType=Prs|Reflex=Yes",
+    "5": "n,\u4ee3\u540d\u8a5e,\u4eba\u79f0,\u6b62\u683c,PRON,Person=1|PronType=Prs",
+    "6": "n,\u4ee3\u540d\u8a5e,\u4eba\u79f0,\u6b62\u683c,PRON,Person=2|PronType=Prs",
+    "7": "n,\u4ee3\u540d\u8a5e,\u4eba\u79f0,\u6b62\u683c,PRON,Person=3|PronType=Prs",
+    "8": "n,\u4ee3\u540d\u8a5e,\u4eba\u79f0,\u8d77\u683c,PRON,Person=1|PronType=Prs",
+    "9": "n,\u4ee3\u540d\u8a5e,\u4eba\u79f0,\u8d77\u683c,PRON,Person=2|PronType=Prs",
+    "10": "n,\u4ee3\u540d\u8a5e,\u4eba\u79f0,\u8d77\u683c,PRON,Person=3|PronType=Prs",
+    "11": "n,\u4ee3\u540d\u8a5e,\u4eba\u79f0,\u8d77\u683c,PRON,PronType=Prs",
+    "12": "n,\u4ee3\u540d\u8a5e,\u6307\u793a,*,PRON,PronType=Dem",
+    "13": "n,\u4ee3\u540d\u8a5e,\u7591\u554f,*,PRON,PronType=Int",
+    "14": "n,\u540d\u8a5e,\u4e0d\u53ef\u8b72,\u5c5e\u6027,NOUN,_",
+    "15": "n,\u540d\u8a5e,\u4e0d\u53ef\u8b72,\u75be\u75c5,NOUN,_",
+    "16": "n,\u540d\u8a5e,\u4e0d\u53ef\u8b72,\u8eab\u4f53,NOUN,_",
+    "17": "n,\u540d\u8a5e,\u4e3b\u4f53,\u52d5\u7269,NOUN,_",
+    "18": "n,\u540d\u8a5e,\u4e3b\u4f53,\u56fd\u540d,PROPN,Case=Loc|NameType=Nat",
+    "19": "n,\u540d\u8a5e,\u4e3b\u4f53,\u66f8\u7269,NOUN,_",
+    "20": "n,\u540d\u8a5e,\u4e3b\u4f53,\u6a5f\u95a2,NOUN,_",
+    "21": "n,\u540d\u8a5e,\u4e3b\u4f53,\u795e\u4ecf,NOUN,_",
+    "22": "n,\u540d\u8a5e,\u4e3b\u4f53,\u96c6\u56e3,NOUN,_",
+    "23": "n,\u540d\u8a5e,\u4eba,\u305d\u306e\u4ed6\u306e\u4eba\u540d,PROPN,NameType=Prs",
+    "24": "n,\u540d\u8a5e,\u4eba,\u4eba,NOUN,_",
+    "25": "n,\u540d\u8a5e,\u4eba,\u540d,PROPN,NameType=Giv",
+    "26": "n,\u540d\u8a5e,\u4eba,\u59d3\u6c0f,PROPN,NameType=Sur",
+    "27": "n,\u540d\u8a5e,\u4eba,\u5f79\u5272,NOUN,_",
+    "28": "n,\u540d\u8a5e,\u4eba,\u8907\u5408\u7684\u4eba\u540d,PROPN,NameType=Prs",
+    "29": "n,\u540d\u8a5e,\u4eba,\u95a2\u4fc2,NOUN,_",
+    "30": "n,\u540d\u8a5e,\u5236\u5ea6,\u5100\u793c,NOUN,_",
+    "31": "n,\u540d\u8a5e,\u5236\u5ea6,\u5834,NOUN,Case=Loc",
+    "32": "n,\u540d\u8a5e,\u53ef\u642c,\u4e57\u308a\u7269,NOUN,_",
+    "33": "n,\u540d\u8a5e,\u53ef\u642c,\u4f1d\u9054,NOUN,_",
+    "34": "n,\u540d\u8a5e,\u53ef\u642c,\u6210\u679c\u7269,NOUN,_",
+    "35": "n,\u540d\u8a5e,\u53ef\u642c,\u7ce7\u98df,NOUN,_",
+    "36": "n,\u540d\u8a5e,\u53ef\u642c,\u9053\u5177,NOUN,_",
+    "37": "n,\u540d\u8a5e,\u56fa\u5b9a\u7269,\u5730\u540d,PROPN,Case=Loc|NameType=Geo",
+    "38": "n,\u540d\u8a5e,\u56fa\u5b9a\u7269,\u5730\u5f62,NOUN,Case=Loc",
+    "39": "n,\u540d\u8a5e,\u56fa\u5b9a\u7269,\u5efa\u9020\u7269,NOUN,Case=Loc",
+    "40": "n,\u540d\u8a5e,\u56fa\u5b9a\u7269,\u6a39\u6728,NOUN,_",
+    "41": "n,\u540d\u8a5e,\u56fa\u5b9a\u7269,\u95a2\u4fc2,NOUN,Case=Loc",
+    "42": "n,\u540d\u8a5e,\u5916\u89b3,\u4eba,NOUN,_",
+    "43": "n,\u540d\u8a5e,\u5929\u8c61,\u5929\u6587,NOUN,_",
+    "44": "n,\u540d\u8a5e,\u5929\u8c61,\u602a\u7570,NOUN,_",
+    "45": "n,\u540d\u8a5e,\u5929\u8c61,\u6c17\u8c61,NOUN,_",
+    "46": "n,\u540d\u8a5e,\u5ea6\u91cf\u8861,*,NOUN,NounType=Clf",
+    "47": "n,\u540d\u8a5e,\u601d\u8003,*,NOUN,_",
+    "48": "n,\u540d\u8a5e,\u63cf\u5199,\u5f62\u8cea,NOUN,_",
+    "49": "n,\u540d\u8a5e,\u63cf\u5199,\u614b\u5ea6,NOUN,_",
+    "50": "n,\u540d\u8a5e,\u6570\u91cf,*,NOUN,_",
+    "51": "n,\u540d\u8a5e,\u6642,*,NOUN,Case=Tem",
+    "52": "n,\u540d\u8a5e,\u884c\u70ba,*,NOUN,_",
+    "53": "n,\u6570\u8a5e,\u5e72\u652f,*,NUM,NumType=Ord",
+    "54": "n,\u6570\u8a5e,\u6570,*,NUM,_",
+    "55": "n,\u6570\u8a5e,\u6570\u5b57,*,NUM,_",
+    "56": "p,\u52a9\u8a5e,\u53e5\u672b,*,PART,_",
+    "57": "p,\u52a9\u8a5e,\u53e5\u982d,*,PART,_",
+    "58": "p,\u52a9\u8a5e,\u63a5\u7d9a,\u4e26\u5217,CCONJ,_",
+    "59": "p,\u52a9\u8a5e,\u63a5\u7d9a,\u4f53\u8a00\u5316,PART,_",
+    "60": "p,\u52a9\u8a5e,\u63a5\u7d9a,\u5c5e\u683c,SCONJ,_",
+    "61": "p,\u52a9\u8a5e,\u63d0\u793a,*,PART,_",
+    "62": "p,\u611f\u5606\u8a5e,*,*,INTJ,_",
+    "63": "p,\u63a5\u5c3e\u8f9e,*,*,PART,_",
+    "64": "s,\u6587\u5b57,*,*,SYM,_",
+    "65": "s,\u8a18\u53f7,\u4e00\u822c,*,SYM,_",
+    "66": "s,\u8a18\u53f7,\u53e5\u70b9,*,PUNCT,_",
+    "67": "s,\u8a18\u53f7,\u8aad\u70b9,*,PUNCT,_",
+    "68": "v,\u524d\u7f6e\u8a5e,\u57fa\u76e4,*,ADP,_",
+    "69": "v,\u524d\u7f6e\u8a5e,\u6e90\u6cc9,*,ADP,_",
+    "70": "v,\u524d\u7f6e\u8a5e,\u7d4c\u7531,*,ADP,_",
+    "71": "v,\u524d\u7f6e\u8a5e,\u95a2\u4fc2,*,ADP,_",
+    "72": "v,\u526f\u8a5e,\u5224\u65ad,\u63a8\u5b9a,ADV,_",
+    "73": "v,\u526f\u8a5e,\u5224\u65ad,\u78ba\u5b9a,ADV,_",
+    "74": "v,\u526f\u8a5e,\u5224\u65ad,\u9006\u63a5,ADV,_",
+    "75": "v,\u526f\u8a5e,\u5426\u5b9a,\u4f53\u8a00\u5426\u5b9a,ADV,Polarity=Neg",
+    "76": "v,\u526f\u8a5e,\u5426\u5b9a,\u6709\u754c,ADV,Polarity=Neg",
+    "77": "v,\u526f\u8a5e,\u5426\u5b9a,\u7121\u754c,ADV,Polarity=Neg",
+    "78": "v,\u526f\u8a5e,\u5426\u5b9a,\u7981\u6b62,ADV,Polarity=Neg",
+    "79": "v,\u526f\u8a5e,\u63cf\u5199,*,ADV,_",
+    "80": "v,\u526f\u8a5e,\u6642\u76f8,\u5909\u5316,ADV,AdvType=Tim",
+    "81": "v,\u526f\u8a5e,\u6642\u76f8,\u5b8c\u4e86,ADV,AdvType=Tim|Aspect=Perf",
+    "82": "v,\u526f\u8a5e,\u6642\u76f8,\u5c06\u6765,ADV,AdvType=Tim|Tense=Fut",
+    "83": "v,\u526f\u8a5e,\u6642\u76f8,\u6052\u5e38,ADV,AdvType=Tim",
+    "84": "v,\u526f\u8a5e,\u6642\u76f8,\u73fe\u5728,ADV,AdvType=Tim|Tense=Pres",
+    "85": "v,\u526f\u8a5e,\u6642\u76f8,\u7d42\u5c40,ADV,AdvType=Tim",
+    "86": "v,\u526f\u8a5e,\u6642\u76f8,\u7d99\u8d77,ADV,AdvType=Tim",
+    "87": "v,\u526f\u8a5e,\u6642\u76f8,\u7dca\u63a5,ADV,AdvType=Tim",
+    "88": "v,\u526f\u8a5e,\u6642\u76f8,\u904e\u53bb,ADV,AdvType=Tim|Tense=Past",
+    "89": "v,\u526f\u8a5e,\u7591\u554f,\u539f\u56e0,ADV,AdvType=Cau",
+    "90": "v,\u526f\u8a5e,\u7591\u554f,\u53cd\u8a9e,ADV,_",
+    "91": "v,\u526f\u8a5e,\u7591\u554f,\u6240\u5728,ADV,_",
+    "92": "v,\u526f\u8a5e,\u7a0b\u5ea6,\u3084\u3084\u9ad8\u5ea6,ADV,AdvType=Deg|Degree=Cmp",
+    "93": "v,\u526f\u8a5e,\u7a0b\u5ea6,\u6975\u5ea6,ADV,AdvType=Deg|Degree=Sup",
+    "94": "v,\u526f\u8a5e,\u7a0b\u5ea6,\u8efd\u5ea6,ADV,AdvType=Deg|Degree=Pos",
+    "95": "v,\u526f\u8a5e,\u7bc4\u56f2,\u5171\u540c,ADV,_",
+    "96": "v,\u526f\u8a5e,\u7bc4\u56f2,\u7dcf\u62ec,ADV,_",
+    "97": "v,\u526f\u8a5e,\u7bc4\u56f2,\u9650\u5b9a,ADV,_",
+    "98": "v,\u526f\u8a5e,\u983b\u5ea6,\u5076\u767a,ADV,_",
+    "99": "v,\u526f\u8a5e,\u983b\u5ea6,\u91cd\u8907,ADV,_",
+    "100": "v,\u526f\u8a5e,\u983b\u5ea6,\u983b\u7e41,ADV,_",
+    "101": "v,\u52a9\u52d5\u8a5e,\u53d7\u52d5,*,AUX,Voice=Pass",
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   "layer_norm_eps": 1e-12,
   "max_position_embeddings": 512,
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   "pooler_type": "first_token_transform",
   "position_embedding_type": "absolute",
   "torch_dtype": "float32",
-  "transformers_version": "4.9.2",
+  "transformers_version": "4.11.3",
   "type_vocab_size": 2,
   "use_cache": true,
   "vocab_size": 29791

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