model improved for transformers 4.42

config.json

@@ -2,25 +2,13 @@
   "architectures": [
     "MistralForTokenClassification"
   ],
-  "attention_bias": false,
   "attention_dropout": 0.0,
-  "auto_map": {
-    "AutoModelForTokenClassification": "upos.MistralForTokenClassification"
-  },
   "bos_token_id": 1,
   "custom_pipelines": {
     "upos": {
       "impl": "upos.BellmanFordTokenClassificationPipeline",
       "pt": "AutoModelForTokenClassification"
     },
-    "token-classification": {
-      "impl": "upos.RawTokenClassificationPipeline",
-      "pt": "AutoModelForTokenClassification"
-    },
-    "ner": {
-      "impl": "upos.RawTokenClassificationPipeline",
-      "pt": "AutoModelForTokenClassification"
-    }
   },
   "eos_token_id": 2,
   "hidden_act": "silu",
@@ -156,15 +144,13 @@
   "num_attention_heads": 32,
   "num_hidden_layers": 32,
   "num_key_value_heads": 8,
-  "pretraining_tp": 1,
   "rms_norm_eps": 1e-05,
-  "rope_scaling": null,
   "rope_theta": 10000.0,
   "sliding_window": 4096,
   "tie_word_embeddings": false,
   "tokenizer_class": "LlamaTokenizerFast",
   "torch_dtype": "float32",
-  "transformers_version": "4.41.2",
+  "transformers_version": "4.42.4",
   "use_cache": true,
   "vocab_size": 43317
 }

maker.sh

@@ -7,43 +7,7 @@ cat << 'EOF' > $TMP
 #! /usr/bin/env deepspeed
 src="KoichiYasuoka/Swallow-MS-7b-upos"
 tgt="KoichiYasuoka/Swallow-MS-7b-char-upos"
-from transformers import LlamaTokenizerFast,MistralModel,MistralPreTrainedModel,AutoConfig,DataCollatorForTokenClassification,TrainingArguments,Trainer
-from transformers.modeling_outputs import TokenClassifierOutput
-
-class MistralForTokenClassification(MistralPreTrainedModel):
-  def __init__(self,config):
-    from torch import nn
-    super().__init__(config)
-    self.num_labels=config.num_labels
-    self.model=MistralModel(config)
-    if hasattr(config,"classifier_dropout") and config.classifier_dropout is not None:
-      classifier_dropout=config.classifier_dropout
-    elif hasattr(config,"hidden_dropout") and config.hidden_dropout is not None:
-      classifier_dropout=config.hidden_dropout
-    else:
-      classifier_dropout=0.1
-    self.dropout=nn.Dropout(classifier_dropout)
-    self.classifier=nn.Linear(config.hidden_size,config.num_labels)
-    self.post_init()
-  def get_input_embeddings(self):
-    return self.model.embed_tokens
-  def set_input_embeddings(self,value):
-    self.model.embed_tokens=value
-  def forward(self,input_ids=None,past_key_values=None,attention_mask=None,position_ids=None,inputs_embeds=None,labels=None,use_cache=None,output_attentions=None,output_hidden_states=None,return_dict=None):
-    return_dict=return_dict if return_dict is not None else self.config.use_return_dict
-    transformer_outputs=self.model(input_ids,past_key_values=past_key_values,attention_mask=attention_mask,position_ids=position_ids,inputs_embeds=inputs_embeds,use_cache=use_cache,output_attentions=output_attentions,output_hidden_states=output_hidden_states,return_dict=return_dict)
-    hidden_states=transformer_outputs[0]
-    hidden_states=self.dropout(hidden_states)
-    logits=self.classifier(hidden_states)
-    loss=None
-    if labels is not None:
-      from torch import nn
-      loss_fct=nn.CrossEntropyLoss()
-      loss=loss_fct(logits.view(-1,self.num_labels),labels.view(-1))
-    if not return_dict:
-      output=(logits,)+transformer_outputs[1:]
-      return ((loss,)+output) if loss is not None else output
-    return TokenClassifierOutput(loss=loss,logits=logits,hidden_states=transformer_outputs.hidden_states,attentions=transformer_outputs.attentions)
+from transformers import LlamaTokenizerFast,MistralForTokenClassification,AutoConfig,DataCollatorForTokenClassification,TrainingArguments,Trainer
 
 class UPOSFileDataset(object):
   def __init__(self,conllu,tokenizer):

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pytorch_model.bin.index.json

@@ -3,8 +3,6 @@
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   },
   "weight_map": {
-    "classifier.bias": "pytorch_model-00006-of-00006.bin",
-    "classifier.weight": "pytorch_model-00006-of-00006.bin",
     "model.embed_tokens.weight": "pytorch_model-00001-of-00006.bin",
     "model.layers.0.input_layernorm.weight": "pytorch_model-00001-of-00006.bin",
     "model.layers.0.mlp.down_proj.weight": "pytorch_model-00001-of-00006.bin",
@@ -294,6 +292,8 @@
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     "model.layers.9.self_attn.q_proj.weight": "pytorch_model-00002-of-00006.bin",
     "model.layers.9.self_attn.v_proj.weight": "pytorch_model-00002-of-00006.bin",
-    "model.norm.weight": "pytorch_model-00006-of-00006.bin"
+    "model.norm.weight": "pytorch_model-00006-of-00006.bin",
+    "score.bias": "pytorch_model-00006-of-00006.bin",
+    "score.weight": "pytorch_model-00006-of-00006.bin"
   }
 }

tokenizer.json

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+      "恣": 42842,
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+      "愈": 42846,
+      "賤": 42847,
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+      "畢": 42849,
+      "棍": 42850,
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+      "讓": 42852,
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+      "專": 42857,
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+      "傅": 42862,
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+      "墮": 42864,
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+      "緡": 42867,
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+      "赫": 42870,
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+      "趨": 42904,
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+      "彭": 42937,
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+      "彝": 42952,
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+      "黠": 42959,
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+      "譯": 42962,
+      "飮": 42963,
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+      "饜": 42967,
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+      "憊": 42991,
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+      "珊": 42993,
+      "丞": 42994,
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+      "歡": 42996,
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+      "兒": 43001,
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+      "蹄": 43007,
+      "壓": 43008,
+      "朧": 43009,
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+      "楷": 43011,
+      "禀": 43012,
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+      "廟": 43014,
+      "煥": 43015,
+      "岌": 43016,
+      "觸": 43017,
+      "簪": 43018,
+      "匐": 43019,
+      "薛": 43020,
+      "辜": 43021,
+      "巍": 43022,
+      "麿": 43023,
+      "皺": 43024,
+      "臀": 43025,
+      "頒": 43026,
+      "勞": 43027,
+      "虔": 43028,
+      "嚼": 43029,
+      "邑": 43030,
+      "嬰": 43031,
+      "勒": 43032,
+      "鬢": 43033,
+      "囮": 43034,
+      "誦": 43035,
+      "蓁": 43036,
+      "兩": 43037,
+      "篆": 43038,
+      "勸": 43039,
+      "隨": 43040,
+      "啜": 43041,
+      "舜": 43042,
+      "尸": 43043,
+      "茲": 43044,
+      "竭": 43045,
+      "岨": 43046,
+      "戲": 43047,
+      "陷": 43048,
+      "綮": 43049,
+      "歐": 43050,
+      "襄": 43051,
+      "廢": 43052,
+      "綬": 43053,
+      "戾": 43054,
+      "夭": 43055,
+      "遑": 43056,
+      "僞": 43057,
+      "駸": 43058,
+      "抔": 43059,
+      "耻": 43060,
+      "禎": 43061,
+      "畧": 43062,
+      "逼": 43063,
+      "圓": 43064,
+      "姦": 43065,
+      "憬": 43066,
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+      "僻": 43068,
+      "鐸": 43069,
+      "濤": 43070,
+      "隋": 43071,
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+      "滌": 43073,
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+      "挈": 43075,
+      "崗": 43076,
+      "匍": 43077,
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+      "澁": 43079,
+      "儼": 43080,
+      "僥": 43081,
+      "闢": 43082,
+      "聲": 43083,
+      "蛉": 43084,
+      "酋": 43085,
+      "恙": 43086,
+      "蹈": 43087,
+      "欵": 43088,
+      "唖": 43089,
+      "槙": 43090,
+      "裴": 43091,
+      "吻": 43092,
+      "蕃": 43093,
+      "熙": 43094,
+      "糀": 43095,
+      "彗": 43096,
+      "臾": 43097,
+      "寵": 43098,
+      "勵": 43099,
+      "懺": 43100,
+      "屬": 43101,
+      "鸞": 43102,
+      "隱": 43103,
+      "諂": 43104,
+      "尹": 43105,
+      "舒": 43106,
+      "餘": 43107,
+      "贖": 43108,
+      "嗚": 43109,
+      "闍": 43110,
+      "醉": 43111,
+      "縣": 43112,
+      "朕": 43113,
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+      "恕": 43118,
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+      "錐": 43120,
+      "慄": 43121,
+      "玻": 43122,
+      "敎": 43123,
+      "袈": 43124,
+      "拮": 43125,
+      "裔": 43126,
+      "沃": 43127,
+      "雉": 43128,
+      "擔": 43129,
+      "闡": 43130,
+      "肚": 43131,
+      "陪": 43132,
+      "亦": 43133,
+      "渺": 43134,
+      "藥": 43135,
+      "鵠": 43136,
+      "忿": 43137,
+      "傚": 43138,
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+      "雖": 43140,
+      "胖": 43141,
+      "匈": 43142,
+      "黃": 43143,
+      "翰": 43144,
+      "埸": 43145,
+      "芒": 43146,
+      "葢": 43147,
+      "罕": 43148,
+      "肆": 43149,
+      "饒": 43150,
+      "諷": 43151,
+      "弐": 43152,
+      "讀": 43153,
+      "鵬": 43154,
+      "闊": 43155,
+      "盍": 43156,
+      "歟": 43157,
+      "俟": 43158,
+      "懥": 43159,
+      "埠": 43160,
+      "斤": 43161,
+      "龕": 43162,
+      "恤": 43163,
+      "舊": 43164,
+      "倹": 43165,
+      "諱": 43166,
+      "雞": 43167,
+      "孛": 43168,
+      "尤": 43169,
+      "團": 43170,
+      "汀": 43171,
+      "盜": 43172,
+      "篩": 43173,
+      "鐵": 43174,
+      "皓": 43175,
+      "揠": 43176,
+      "觀": 43177,
+      "羹": 43178,
+      "豕": 43179,
+      "帥": 43180,
+      "隧": 43181,
+      "獸": 43182,
+      "碇": 43183,
+      "殲": 43184,
+      "斷": 43185,
+      "桀": 43186,
+      "尙": 43187,
+      "愼": 43188,
+      "蔗": 43189,
+      "熾": 43190,
+      "續": 43191,
+      "皷": 43192,
+      "畫": 43193,
+      "丙": 43194,
+      "茫": 43195,
+      "揜": 43196,
+      "靡": 43197,
+      "墾": 43198,
+      "燮": 43199,
+      "逕": 43200,
+      "媢": 43201,
+      "穆": 43202,
+      "覊": 43203,
+      "漸": 43204,
+      "樣": 43205,
+      "恪": 43206,
+      "頴": 43207,
+      "咎": 43208,
+      "宥": 43209,
+      "礦": 43210,
+      "貶": 43211,
+      "膽": 43212,
+      "慘": 43213,
+      "貮": 43214,
+      "傀": 43215,
+      "懼": 43216,
+      "籾": 43217,
+      "吏": 43218,
+      "榮": 43219,
+      "繭": 43220,
+      "撿": 43221,
+      "甞": 43222,
+      "遲": 43223,
+      "驗": 43224,
+      "跋": 43225,
+      "滊": 43226,
+      "糾": 43227,
+      "諧": 43228,
+      "剝": 43229,
+      "肅": 43230,
+      "柘": 43231,
+      "辨": 43232,
+      "據": 43233,
+      "誣": 43234,
+      "竄": 43235,
       "錮": 43236,
-      "詭": 43237,
-      "猗": 43238,
-      "掩": 43239,
-      "醉": 43240,
-      "蜻": 43241,
-      "憊": 43242,
-      "桀": 43243,
-      "膽": 43244,
-      "毆": 43245,
-      "沮": 43246,
-      "勞": 43247,
-      "滿": 43248,
-      "洽": 43249,
-      "裔": 43250,
-      "亂": 43251,
-      "悉": 43252,
-      "鎭": 43253,
-      "蠻": 43254,
-      "菑": 43255,
-      "憮": 43256,
-      "榜": 43257,
-      "臘": 43258,
-      "恂": 43259,
-      "亦": 43260,
-      "苟": 43261,
-      "啻": 43262,
-      "吻": 43263,
-      "頒": 43264,
-      "證": 43265,
-      "畫": 43266,
-      "楯": 43267,
-      "滊": 43268,
-      "醵": 43269,
-      "禮": 43270,
-      "儕": 43271,
-      "頽": 43272,
-      "陋": 43273,
-      "煥": 43274,
-      "胥": 43275,
-      "咎": 43276,
-      "堯": 43277,
-      "誥": 43278,
-      "玻": 43279,
-      "臥": 43280,
-      "穆": 43281,
-      "蛉": 43282,
-      "囮": 43283,
-      "肆": 43284,
-      "妾": 43285,
-      "爰": 43286,
-      "屬": 43287,
-      "爭": 43288,
-      "贖": 43289,
-      "蹊": 43290,
-      "撹": 43291,
-      "忒": 43292,
-      "陪": 43293,
-      "誣": 43294,
-      "堡": 43295,
-      "寔": 43296,
-      "亨": 43297,
-      "綮": 43298,
-      "歸": 43299,
-      "豕": 43300,
-      "翹": 43301,
-      "鬢": 43302,
-      "騁": 43303,
-      "槙": 43304,
-      "隋": 43305,
-      "墾": 43306,
-      "緝": 43307,
-      "廢": 43308,
-      "駢": 43309,
-      "駭": 43310,
-      "烟": 43311,
-      "亥": 43312,
-      "竟": 43313,
-      "汝": 43314,
-      "疆": 43315,
-      "儼": 43316
+      "歎": 43237,
+      "窘": 43238,
+      "愧": 43239,
+      "聽": 43240,
+      "臂": 43241,
+      "妾": 43242,
+      "駭": 43243,
+      "頽": 43244,
+      "鏃": 43245,
+      "亨": 43246,
+      "殘": 43247,
+      "壹": 43248,
+      "轍": 43249,
+      "嘲": 43250,
+      "爨": 43251,
+      "驕": 43252,
+      "滿": 43253,
+      "碩": 43254,
+      "碍": 43255,
+      "虞": 43256,
+      "賂": 43257,
+      "脹": 43258,
+      "頰": 43259,
+      "恊": 43260,
+      "揆": 43261,
+      "躓": 43262,
+      "辟": 43263,
+      "倖": 43264,
+      "謨": 43265,
+      "梃": 43266,
+      "罷": 43267,
+      "覽": 43268,
+      "宍": 43269,
+      "諟": 43270,
+      "欷": 43271,
+      "繼": 43272,
+      "歔": 43273,
+      "埴": 43274,
+      "舅": 43275,
+      "啻": 43276,
+      "戊": 43277,
+      "儘": 43278,
+      "處": 43279,
+      "巖": 43280,
+      "豫": 43281,
+      "擧": 43282,
+      "竊": 43283,
+      "狸": 43284,
+      "蘊": 43285,
+      "橙": 43286,
+      "鰭": 43287,
+      "甍": 43288,
+      "菰": 43289,
+      "價": 43290,
+      "揖": 43291,
+      "嫡": 43292,
+      "耆": 43293,
+      "葦": 43294,
+      "堯": 43295,
+      "戌": 43296,
+      "獨": 43297,
+      "汝": 43298,
+      "澳": 43299,
+      "呵": 43300,
+      "憚": 43301,
+      "嚮": 43302,
+      "翹": 43303,
+      "孟": 43304,
+      "鄭": 43305,
+      "壤": 43306,
+      "竝": 43307,
+      "菉": 43308,
+      "烟": 43309,
+      "閻": 43310,
+      "禮": 43311,
+      "犂": 43312,
+      "欣": 43313,
+      "惡": 43314,
+      "鉤": 43315,
+      "艱": 43316
     },
     "merges": [
       "▁ t",

tokenizer_config.json

@@ -1,6 +1,7 @@
 {
   "add_bos_token": true,
   "add_eos_token": false,
+  "add_prefix_space": null,
   "added_tokens_decoder": {
     "0": {
       "content": "<unk>",
@@ -36,7 +37,7 @@
   "model_max_length": 4096,
   "pad_token": "</s>",
   "sep_token": "<s>",
-  "tokenizer_class": "LlamaTokenizer",
+  "tokenizer_class": "LlamaTokenizerFast",
   "unk_token": "<unk>",
   "use_default_system_prompt": false
 }

upos.py

@@ -1,5 +1,4 @@
-from transformers import TokenClassificationPipeline,MistralModel,MistralPreTrainedModel
-from transformers.modeling_outputs import TokenClassifierOutput
+from transformers import TokenClassificationPipeline
 
 class BellmanFordTokenClassificationPipeline(TokenClassificationPipeline):
   def __init__(self,**kwargs):
@@ -40,41 +39,3 @@ class BellmanFordTokenClassificationPipeline(TokenClassificationPipeline):
       t["text"]=model_outputs["sentence"][t["start"]:t["end"]]
     return w
 
-class RawTokenClassificationPipeline(TokenClassificationPipeline):
-  def check_model_type(self,supported_models):
-    pass
-
-class MistralForTokenClassification(MistralPreTrainedModel):
-  def __init__(self,config):
-    from torch import nn
-    super().__init__(config)
-    self.num_labels=config.num_labels
-    self.model=MistralModel(config)
-    if hasattr(config,"classifier_dropout") and config.classifier_dropout is not None:
-      classifier_dropout=config.classifier_dropout
-    elif hasattr(config,"hidden_dropout") and config.hidden_dropout is not None:
-      classifier_dropout=config.hidden_dropout
-    else:
-      classifier_dropout=0.1
-    self.dropout=nn.Dropout(classifier_dropout)
-    self.classifier=nn.Linear(config.hidden_size,config.num_labels)
-    self.post_init()
-  def get_input_embeddings(self):
-    return self.model.embed_tokens
-  def set_input_embeddings(self,value):
-    self.model.embed_tokens=value
-  def forward(self,input_ids=None,past_key_values=None,attention_mask=None,position_ids=None,inputs_embeds=None,labels=None,use_cache=None,output_attentions=None,output_hidden_states=None,return_dict=None):
-    return_dict=return_dict if return_dict is not None else self.config.use_return_dict
-    transformer_outputs=self.model(input_ids,past_key_values=past_key_values,attention_mask=attention_mask,position_ids=position_ids,inputs_embeds=inputs_embeds,use_cache=use_cache,output_attentions=output_attentions,output_hidden_states=output_hidden_states,return_dict=return_dict)
-    hidden_states=transformer_outputs[0]
-    hidden_states=self.dropout(hidden_states)
-    logits=self.classifier(hidden_states)
-    loss=None
-    if labels is not None:
-      from torch import nn
-      loss_fct=nn.CrossEntropyLoss()
-      loss=loss_fct(logits.view(-1,self.num_labels),labels.view(-1))
-    if not return_dict:
-      output=(logits,)+transformer_outputs[2:]
-      return ((loss,)+output) if loss is not None else output
-    return TokenClassifierOutput(loss=loss,logits=logits,hidden_states=transformer_outputs.hidden_states,attentions=transformer_outputs.attentions)