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KoichiYasuoka
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Llama-3-Swallow-8B-upos

Token Classification
Transformers
PyTorch
Japanese
llama
japanese
pos
text-generation-inference
Inference Endpoints
Model card Files Community
KoichiYasuoka commited on
Commit
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1 Parent(s): 2957713

initial release

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Files changed (15) hide show
  1. README.md +29 -0
  2. config.json +144 -0
  3. maker.sh +159 -0
  4. pytorch_model-00001-of-00007.bin +3 -0
  5. pytorch_model-00002-of-00007.bin +3 -0
  6. pytorch_model-00003-of-00007.bin +3 -0
  7. pytorch_model-00004-of-00007.bin +3 -0
  8. pytorch_model-00005-of-00007.bin +3 -0
  9. pytorch_model-00006-of-00007.bin +3 -0
  10. pytorch_model-00007-of-00007.bin +3 -0
  11. pytorch_model.bin.index.json +299 -0
  12. special_tokens_map.json +51 -0
  13. tokenizer.json +0 -0
  14. tokenizer_config.json +2090 -0
  15. upos.py +76 -0
README.md ADDED
@@ -0,0 +1,29 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ language:
3
+ - "ja"
4
+ tags:
5
+ - "japanese"
6
+ - "token-classification"
7
+ - "pos"
8
+ datasets:
9
+ - "universal_dependencies"
10
+ license: "llama3"
11
+ pipeline_tag: "token-classification"
12
+ widget:
13
+ - text: "国境の長いトンネルを抜けると雪国であった。"
14
+ ---
15
+
16
+ # Llama-3-Swallow-8B-upos
17
+
18
+ ## Model Description
19
+
20
+ This is a LLaMA model for POS-tagging, derived from [Llama-3-Swallow-8B-v0.1](https://huggingface.co/tokyotech-llm/Llama-3-Swallow-8B-v0.1). Every short-unit-word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universal Part-Of-Speech) and [FEATS](https://universaldependencies.org/u/feat/).
21
+
22
+ ## How to Use
23
+
24
+ ```py
25
+ from transformers import pipeline
26
+ nlp=pipeline("upos","KoichiYasuoka/Llama-3-Swallow-8B-upos",trust_remote_code=True,aggregation_strategy="simple")
27
+ print(nlp("国境の長いトンネルを抜けると雪国であった。"))
28
+ ```
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+
config.json ADDED
@@ -0,0 +1,144 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "architectures": [
3
+ "LlamaForTokenClassification"
4
+ ],
5
+ "attention_bias": false,
6
+ "attention_dropout": 0.0,
7
+ "auto_map": {
8
+ "AutoModelForTokenClassification": "upos.LlamaForTokenClassification"
9
+ },
10
+ "bos_token_id": 128000,
11
+ "custom_pipelines": {
12
+ "upos": {
13
+ "impl": "upos.BellmanFordTokenClassificationPipeline",
14
+ "pt": "AutoModelForTokenClassification"
15
+ }
16
+ },
17
+ "eos_token_id": 128001,
18
+ "hidden_act": "silu",
19
+ "hidden_size": 4096,
20
+ "id2label": {
21
+ "0": "ADJ",
22
+ "1": "B-ADJ",
23
+ "2": "I-ADJ",
24
+ "3": "ADP",
25
+ "4": "B-ADP",
26
+ "5": "I-ADP",
27
+ "6": "ADV",
28
+ "7": "B-ADV",
29
+ "8": "I-ADV",
30
+ "9": "AUX",
31
+ "10": "B-AUX",
32
+ "11": "I-AUX",
33
+ "12": "CCONJ",
34
+ "13": "B-CCONJ",
35
+ "14": "I-CCONJ",
36
+ "15": "DET",
37
+ "16": "B-DET",
38
+ "17": "I-DET",
39
+ "18": "INTJ",
40
+ "19": "B-INTJ",
41
+ "20": "I-INTJ",
42
+ "21": "NOUN",
43
+ "22": "B-NOUN",
44
+ "23": "I-NOUN",
45
+ "24": "NUM",
46
+ "25": "B-NUM",
47
+ "26": "I-NUM",
48
+ "27": "PART",
49
+ "28": "B-PART",
50
+ "29": "I-PART",
51
+ "30": "PRON",
52
+ "31": "B-PRON",
53
+ "32": "I-PRON",
54
+ "33": "PROPN",
55
+ "34": "B-PROPN",
56
+ "35": "I-PROPN",
57
+ "36": "PUNCT",
58
+ "37": "B-PUNCT",
59
+ "38": "I-PUNCT",
60
+ "39": "SCONJ",
61
+ "40": "B-SCONJ",
62
+ "41": "I-SCONJ",
63
+ "42": "SYM",
64
+ "43": "B-SYM",
65
+ "44": "I-SYM",
66
+ "45": "VERB",
67
+ "46": "B-VERB",
68
+ "47": "I-VERB",
69
+ "48": "X",
70
+ "49": "B-X",
71
+ "50": "I-X"
72
+ },
73
+ "initializer_range": 0.02,
74
+ "intermediate_size": 14336,
75
+ "label2id": {
76
+ "ADJ": 0,
77
+ "ADP": 3,
78
+ "ADV": 6,
79
+ "AUX": 9,
80
+ "B-ADJ": 1,
81
+ "B-ADP": 4,
82
+ "B-ADV": 7,
83
+ "B-AUX": 10,
84
+ "B-CCONJ": 13,
85
+ "B-DET": 16,
86
+ "B-INTJ": 19,
87
+ "B-NOUN": 22,
88
+ "B-NUM": 25,
89
+ "B-PART": 28,
90
+ "B-PRON": 31,
91
+ "B-PROPN": 34,
92
+ "B-PUNCT": 37,
93
+ "B-SCONJ": 40,
94
+ "B-SYM": 43,
95
+ "B-VERB": 46,
96
+ "B-X": 49,
97
+ "CCONJ": 12,
98
+ "DET": 15,
99
+ "I-ADJ": 2,
100
+ "I-ADP": 5,
101
+ "I-ADV": 8,
102
+ "I-AUX": 11,
103
+ "I-CCONJ": 14,
104
+ "I-DET": 17,
105
+ "I-INTJ": 20,
106
+ "I-NOUN": 23,
107
+ "I-NUM": 26,
108
+ "I-PART": 29,
109
+ "I-PRON": 32,
110
+ "I-PROPN": 35,
111
+ "I-PUNCT": 38,
112
+ "I-SCONJ": 41,
113
+ "I-SYM": 44,
114
+ "I-VERB": 47,
115
+ "I-X": 50,
116
+ "INTJ": 18,
117
+ "NOUN": 21,
118
+ "NUM": 24,
119
+ "PART": 27,
120
+ "PRON": 30,
121
+ "PROPN": 33,
122
+ "PUNCT": 36,
123
+ "SCONJ": 39,
124
+ "SYM": 42,
125
+ "VERB": 45,
126
+ "X": 48
127
+ },
128
+ "max_position_embeddings": 8192,
129
+ "mlp_bias": false,
130
+ "model_type": "llama",
131
+ "num_attention_heads": 32,
132
+ "num_hidden_layers": 32,
133
+ "num_key_value_heads": 8,
134
+ "pretraining_tp": 1,
135
+ "rms_norm_eps": 1e-05,
136
+ "rope_scaling": null,
137
+ "rope_theta": 500000.0,
138
+ "tie_word_embeddings": false,
139
+ "tokenizer_class": "LlamaTokenizerFast",
140
+ "torch_dtype": "float32",
141
+ "transformers_version": "4.41.2",
142
+ "use_cache": true,
143
+ "vocab_size": 128259
144
+ }
maker.sh ADDED
@@ -0,0 +1,159 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #! /bin/sh
2
+ test -f ja_gsd_modern.conllu || curl -LO https://github.com/KoichiYasuoka/SuPar-UniDic/raw/main/suparunidic/suparmodels/ja_gsd_modern.conllu
3
+
4
+ if [ ! -d exLlama-3-Swallow-8B ]
5
+ then TMPA=./maker$$a.py
6
+ cat << 'EOF' > $TMPA
7
+ #! /usr/bin/python3
8
+ src="tokyotech-llm/Llama-3-Swallow-8B-v0.1"
9
+ tgt="exLlama-3-Swallow-8B"
10
+ import json
11
+ from transformers import LlamaTokenizerFast,LlamaForCausalLM
12
+ tkz=LlamaTokenizerFast.from_pretrained(src,cls_token="<s>",sep_token="<s>",mask_token="<unk>",pad_token="</s>")
13
+ d=json.loads(tkz.backend_tokenizer.to_str())
14
+ tkz.backend_tokenizer.from_str(json.dumps(d)).save("tokenizer.json")
15
+ mdl=LlamaForCausalLM.from_pretrained(src)
16
+ tkz=LlamaTokenizerFast(tokenizer_file="tokenizer.json",model_max_length=mdl.config.max_position_embeddings,cls_token="<s>",sep_token="<s>",mask_token="<unk>",pad_token="</s>")
17
+ e=mdl.resize_token_embeddings(len(tkz))
18
+ f=mdl.get_output_embeddings()
19
+ mdl.set_input_embeddings(e)
20
+ mdl.set_output_embeddings(f)
21
+ mdl.save_pretrained(tgt)
22
+ tkz.save_pretrained(tgt)
23
+ EOF
24
+ chmod 755 $TMPA
25
+ $TMPA
26
+ fi
27
+
28
+ TMPB=./maker$$b.py
29
+ cat << 'EOF' > $TMPB
30
+ #! /usr/bin/env deepspeed
31
+ src="exLlama-3-Swallow-8B"
32
+ tgt="KoichiYasuoka/Llama-3-Swallow-8B-upos"
33
+ from transformers import LlamaTokenizerFast,LlamaModel,LlamaPreTrainedModel,AutoConfig,DataCollatorForTokenClassification,TrainingArguments,Trainer
34
+ from transformers.modeling_outputs import TokenClassifierOutput
35
+ from tokenizers.normalizers import Replace
36
+
37
+ class LlamaForTokenClassification(LlamaPreTrainedModel):
38
+ def __init__(self,config):
39
+ from torch import nn
40
+ super().__init__(config)
41
+ self.num_labels=config.num_labels
42
+ self.model=LlamaModel(config)
43
+ if hasattr(config,"classifier_dropout") and config.classifier_dropout is not None:
44
+ classifier_dropout=config.classifier_dropout
45
+ elif hasattr(config,"hidden_dropout") and config.hidden_dropout is not None:
46
+ classifier_dropout=config.hidden_dropout
47
+ else:
48
+ classifier_dropout=0.1
49
+ self.dropout=nn.Dropout(classifier_dropout)
50
+ self.classifier=nn.Linear(config.hidden_size,config.num_labels)
51
+ self.post_init()
52
+ def get_input_embeddings(self):
53
+ return self.model.embed_tokens
54
+ def set_input_embeddings(self,value):
55
+ self.model.embed_tokens=value
56
+ 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):
57
+ return_dict=return_dict if return_dict is not None else self.config.use_return_dict
58
+ 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)
59
+ hidden_states=transformer_outputs[0]
60
+ hidden_states=self.dropout(hidden_states)
61
+ logits=self.classifier(hidden_states)
62
+ loss=None
63
+ if labels is not None:
64
+ from torch import nn
65
+ loss_fct=nn.CrossEntropyLoss()
66
+ loss=loss_fct(logits.view(-1,self.num_labels),labels.view(-1))
67
+ if not return_dict:
68
+ output=(logits,)+transformer_outputs[1:]
69
+ return ((loss,)+output) if loss is not None else output
70
+ return TokenClassifierOutput(loss=loss,logits=logits,hidden_states=transformer_outputs.hidden_states,attentions=transformer_outputs.attentions)
71
+
72
+ class UPOSFileDataset(object):
73
+ def __init__(self,conllu,tokenizer):
74
+ self.conllu=open(conllu,"r",encoding="utf-8")
75
+ self.tokenizer=tokenizer
76
+ self.seeks=[0]
77
+ self.multiword={}
78
+ label=set(["SYM"])
79
+ s=self.conllu.readline()
80
+ while s!="":
81
+ if s=="\n":
82
+ self.seeks.append(self.conllu.tell())
83
+ else:
84
+ w=s.split("\t")
85
+ if len(w)==10:
86
+ if w[0].isdecimal():
87
+ label.add(w[3])
88
+ elif w[0].find("-")>0:
89
+ t=w[0].split("-")
90
+ f,j,k=w[1],[],[]
91
+ for i in range(int(t[0]),int(t[1])+1):
92
+ w=self.conllu.readline().split("\t")
93
+ j.append(w[3])
94
+ k.append(w[1])
95
+ p="+".join(j)
96
+ label.add(p)
97
+ if p in self.multiword:
98
+ self.multiword[p][f]=list(k)
99
+ else:
100
+ self.multiword[p]={f:list(k)}
101
+ s=self.conllu.readline()
102
+ lid={}
103
+ for i,l in enumerate(sorted(label)):
104
+ lid[l],lid["B-"+l],lid["I-"+l]=i*3,i*3+1,i*3+2
105
+ self.label2id=lid
106
+ def __call__(*args):
107
+ lid={l:i for i,l in enumerate(sorted(set(sum([list(t.label2id) for t in args],[]))))}
108
+ for t in args:
109
+ t.label2id=lid
110
+ return lid
111
+ def __del__(self):
112
+ self.conllu.close()
113
+ __len__=lambda self:len(self.seeks)-1
114
+ def __getitem__(self,i):
115
+ self.conllu.seek(self.seeks[i])
116
+ form,upos=[],[]
117
+ while self.conllu.tell()<self.seeks[i+1]:
118
+ w=self.conllu.readline().split("\t")
119
+ if len(w)==10:
120
+ form.append(w[1])
121
+ if w[0].isdecimal():
122
+ upos.append(w[3])
123
+ elif w[0].find("-")>0:
124
+ t=w[0].split("-")
125
+ u=[]
126
+ for j in range(int(t[0]),int(t[1])+1):
127
+ k=self.conllu.readline().split("\t")
128
+ u.append(k[3])
129
+ upos.append("+".join(u))
130
+ v=self.tokenizer(form,add_special_tokens=False)
131
+ i,u=[],[]
132
+ for j,(x,y) in enumerate(zip(v["input_ids"],upos)):
133
+ if x!=[]:
134
+ i+=x
135
+ u+=[y] if len(x)==1 else ["B-"+y]+["I-"+y]*(len(x)-1)
136
+ if len(i)<self.tokenizer.model_max_length-3:
137
+ ids=[self.tokenizer.bos_token_id]+i
138
+ upos=["SYM"]+u
139
+ else:
140
+ ids=i[0:self.tokenizer.model_max_length-2]
141
+ upos=u[0:self.tokenizer.model_max_length-2]
142
+ return {"input_ids":ids,"labels":[self.label2id[t] for t in upos]}
143
+
144
+ tkz=LlamaTokenizerFast.from_pretrained(src)
145
+ tkz.backend_tokenizer.normalizer=Replace(" ","\u2581")
146
+ tkz.backend_tokenizer.model.byte_fallback=False
147
+ trainDS=UPOSFileDataset("ja_gsd_modern.conllu",tkz)
148
+ lid=trainDS.label2id
149
+ cfg=AutoConfig.from_pretrained(src,num_labels=len(lid),label2id=lid,id2label={i:l for l,i in lid.items()},ignore_mismatched_sizes=True)
150
+ 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}}
151
+ arg=TrainingArguments(num_train_epochs=3,per_device_train_batch_size=8,deepspeed=dsp,output_dir=tgt,overwrite_output_dir=True,save_total_limit=2,learning_rate=5e-05,warmup_ratio=0.1,save_safetensors=False)
152
+ trn=Trainer(args=arg,data_collator=DataCollatorForTokenClassification(tkz),model=LlamaForTokenClassification.from_pretrained(src,config=cfg,ignore_mismatched_sizes=True),train_dataset=trainDS)
153
+ trn.train()
154
+ trn.save_model(tgt)
155
+ tkz.save_pretrained(tgt)
156
+ EOF
157
+ chmod 755 $TMPB
158
+ $TMPB
159
+ exit
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+ "mask_token": "<unk>",
2084
+ "model_max_length": 8192,
2085
+ "pad_token": "</s>",
2086
+ "sep_token": "<s>",
2087
+ "tokenizer_class": "LlamaTokenizerFast",
2088
+ "unk_token": "<unk>",
2089
+ "use_default_system_prompt": false
2090
+ }
upos.py ADDED
@@ -0,0 +1,76 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from transformers import TokenClassificationPipeline,LlamaModel,LlamaPreTrainedModel
2
+ from transformers.modeling_outputs import TokenClassifierOutput
3
+
4
+ class BellmanFordTokenClassificationPipeline(TokenClassificationPipeline):
5
+ def __init__(self,**kwargs):
6
+ import numpy
7
+ super().__init__(**kwargs)
8
+ x=self.model.config.label2id
9
+ y=[k for k in x if not k.startswith("I-")]
10
+ self.transition=numpy.full((len(x),len(x)),numpy.nan)
11
+ for k,v in x.items():
12
+ for j in ["I-"+k[2:]] if k.startswith("B-") else [k]+y if k.startswith("I-") else y:
13
+ self.transition[v,x[j]]=0
14
+ def check_model_type(self,supported_models):
15
+ pass
16
+ def postprocess(self,model_outputs,**kwargs):
17
+ import numpy
18
+ if "logits" not in model_outputs:
19
+ return self.postprocess(model_outputs[0],**kwargs)
20
+ m=model_outputs["logits"][0].numpy()
21
+ e=numpy.exp(m-numpy.max(m,axis=-1,keepdims=True))
22
+ z=e/e.sum(axis=-1,keepdims=True)
23
+ for i in range(m.shape[0]-1,0,-1):
24
+ m[i-1]+=numpy.nanmax(m[i]+self.transition,axis=1)
25
+ k=[numpy.nanargmax(m[0])]
26
+ for i in range(1,m.shape[0]):
27
+ k.append(numpy.nanargmax(m[i]+self.transition[k[-1]]))
28
+ 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]
29
+ if "aggregation_strategy" in kwargs and kwargs["aggregation_strategy"]!="none":
30
+ for i,t in reversed(list(enumerate(w))):
31
+ p=t.pop("entity")
32
+ if p.startswith("I-"):
33
+ w[i-1]["score"]=min(w[i-1]["score"],t["score"])
34
+ w[i-1]["end"]=w.pop(i)["end"]
35
+ elif p.startswith("B-"):
36
+ t["entity_group"]=p[2:]
37
+ else:
38
+ t["entity_group"]=p
39
+ for t in w:
40
+ t["text"]=model_outputs["sentence"][t["start"]:t["end"]]
41
+ return w
42
+
43
+ class LlamaForTokenClassification(LlamaPreTrainedModel):
44
+ def __init__(self,config):
45
+ from torch import nn
46
+ super().__init__(config)
47
+ self.num_labels=config.num_labels
48
+ self.model=LlamaModel(config)
49
+ if hasattr(config,"classifier_dropout") and config.classifier_dropout is not None:
50
+ classifier_dropout=config.classifier_dropout
51
+ elif hasattr(config,"hidden_dropout") and config.hidden_dropout is not None:
52
+ classifier_dropout=config.hidden_dropout
53
+ else:
54
+ classifier_dropout=0.1
55
+ self.dropout=nn.Dropout(classifier_dropout)
56
+ self.classifier=nn.Linear(config.hidden_size,config.num_labels)
57
+ self.post_init()
58
+ def get_input_embeddings(self):
59
+ return self.model.embed_tokens
60
+ def set_input_embeddings(self,value):
61
+ self.model.embed_tokens=value
62
+ 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):
63
+ return_dict=return_dict if return_dict is not None else self.config.use_return_dict
64
+ 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)
65
+ hidden_states=transformer_outputs[0]
66
+ hidden_states=self.dropout(hidden_states)
67
+ logits=self.classifier(hidden_states)
68
+ loss=None
69
+ if labels is not None:
70
+ from torch import nn
71
+ loss_fct=nn.CrossEntropyLoss()
72
+ loss=loss_fct(logits.view(-1,self.num_labels),labels.view(-1))
73
+ if not return_dict:
74
+ output=(logits,)+transformer_outputs[2:]
75
+ return ((loss,)+output) if loss is not None else output
76
+ return TokenClassifierOutput(loss=loss,logits=logits,hidden_states=transformer_outputs.hidden_states,attentions=transformer_outputs.attentions)