initial release

README.md

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+---
+language:
+- "ja"
+tags:
+- "japanese"
+- "wikipedia"
+- "question-answering"
+- "dependency-parsing"
+datasets:
+- "universal_dependencies"
+license: "cc-by-sa-4.0"
+pipeline_tag: "question-answering"
+widget:
+- text: "国語"
+  context: "全学年にわたって小学校の国語の教科書に挿し絵が用いられている"
+- text: "教科書"
+  context: "全学年にわたって小学校の国語の教科書に挿し絵が用いられている"
+- text: "の"
+  context: "全学年にわたって小学校の国語[MASK]教科書に挿し絵が用いられている"
+---
+
+# deberta-base-japanese-wikipedia-ud-head
+
+## Model Description
+
+This is a DeBERTa(V2) model pretrained on 青空文庫 for dependency-parsing (head-detection on long-unit-words) as question-answering, derived from [deberta-base-japanese-wikipedia](https://huggingface.co/KoichiYasuoka/deberta-base-japanese-wikipedia) and [UD_Japanese-GSDLUW](https://github.com/UniversalDependencies/UD_Japanese-GSDLUW). Use [MASK] inside `context` to avoid ambiguity when specifying a multiple-used word as `question`.
+
+## How to Use
+
+```py
+import torch
+from transformers import AutoTokenizer,AutoModelForQuestionAnswering
+tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-base-japanese-wikipedia-ud-head")
+model=AutoModelForQuestionAnswering.from_pretrained("KoichiYasuoka/deberta-base-japanese-wikipedia-ud-head")
+question="国語"
+context="全学年にわたって小学校の国語の教科書に挿し絵が用いられている"
+inputs=tokenizer(question,context,return_tensors="pt",return_offsets_mapping=True)
+offsets=inputs.pop("offset_mapping").tolist()[0]
+outputs=model(**inputs)
+start,end=torch.argmax(outputs.start_logits),torch.argmax(outputs.end_logits)
+print(context[offsets[start][0]:offsets[end][-1]])
+```
+
+or (with [ufal.chu-liu-edmonds](https://pypi.org/project/ufal.chu-liu-edmonds/))
+
+```py
+class TransformersUD(object):
+  def __init__(self,bert):
+    import os
+    from transformers import (AutoTokenizer,AutoModelForQuestionAnswering,
+      AutoModelForTokenClassification,AutoConfig,TokenClassificationPipeline)
+    self.tokenizer=AutoTokenizer.from_pretrained(bert)
+    self.model=AutoModelForQuestionAnswering.from_pretrained(bert)
+    x=AutoModelForTokenClassification.from_pretrained
+    if os.path.isdir(bert):
+      d,t=x(os.path.join(bert,"deprel")),x(os.path.join(bert,"tagger"))
+    else:
+      from transformers.file_utils import hf_bucket_url
+      c=AutoConfig.from_pretrained(hf_bucket_url(bert,"deprel/config.json"))
+      d=x(hf_bucket_url(bert,"deprel/pytorch_model.bin"),config=c)
+      s=AutoConfig.from_pretrained(hf_bucket_url(bert,"tagger/config.json"))
+      t=x(hf_bucket_url(bert,"tagger/pytorch_model.bin"),config=s)
+    self.deprel=TokenClassificationPipeline(model=d,tokenizer=self.tokenizer,
+      aggregation_strategy="simple")
+    self.tagger=TokenClassificationPipeline(model=t,tokenizer=self.tokenizer)
+  def __call__(self,text):
+    import numpy,torch,ufal.chu_liu_edmonds
+    w=[(t["start"],t["end"],t["entity_group"]) for t in self.deprel(text)]
+    z,n={t["start"]:t["entity"].split("|") for t in self.tagger(text)},len(w)
+    r,m=[text[s:e] for s,e,p in w],numpy.full((n+1,n+1),numpy.nan)
+    v,c=self.tokenizer(r,add_special_tokens=False)["input_ids"],[]
+    for i,t in enumerate(v):
+      q=[self.tokenizer.cls_token_id]+t+[self.tokenizer.sep_token_id]
+      c.append([q]+v[0:i]+[[self.tokenizer.mask_token_id]]+v[i+1:]+[[q[-1]]])
+    b=[[len(sum(x[0:j+1],[])) for j in range(len(x))] for x in c]
+    d=self.model(input_ids=torch.tensor([sum(x,[]) for x in c]),
+      token_type_ids=torch.tensor([[0]*x[0]+[1]*(x[-1]-x[0]) for x in b]))
+    s,e=d.start_logits.tolist(),d.end_logits.tolist()
+    for i in range(n):
+      for j in range(n):
+        m[i+1,0 if i==j else j+1]=s[i][b[i][j]]+e[i][b[i][j+1]-1]
+    h=ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0]
+    if [0 for i in h if i==0]!=[0]:
+      i=([p for s,e,p in w]+["root"]).index("root")
+      j=i+1 if i<n else numpy.nanargmax(m[:,0])
+      m[0:j,0]=m[j+1:,0]=numpy.nan
+      h=ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0]
+    u="# text = "+text.replace("\n"," ")+"\n"
+    for i,(s,e,p) in enumerate(w,1):
+      p="root" if h[i]==0 else "dep" if p=="root" else p
+      u+="\t".join([str(i),r[i-1],"_",z[s][0][2:],"_","|".join(z[s][1:]),
+        str(h[i]),p,"_","_" if i<n and w[i][0]<e else "SpaceAfter=No"])+"\n"
+    return u+"\n"
+
+nlp=TransformersUD("KoichiYasuoka/deberta-base-japanese-wikipedia-ud-head")
+print(nlp("全学年にわたって小学校の国語の教科書に挿し絵が用いられている"))
+```
+

config.json

@@ -0,0 +1,31 @@
+{
+  "architectures": [
+    "DebertaV2ForQuestionAnswering"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-07,
+  "max_position_embeddings": 512,
+  "max_relative_positions": -1,
+  "model_type": "deberta-v2",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "pooler_dropout": 0,
+  "pooler_hidden_act": "gelu",
+  "pooler_hidden_size": 768,
+  "pos_att_type": null,
+  "position_biased_input": true,
+  "relative_attention": false,
+  "tokenizer_class": "DebertaV2TokenizerFast",
+  "torch_dtype": "float32",
+  "transformers_version": "4.19.4",
+  "type_vocab_size": 0,
+  "vocab_size": 32000
+}

deprel/config.json

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+{
+  "architectures": [
+    "DebertaV2ForTokenClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "finetuning_task": "pos",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "B-acl",
+    "1": "B-advcl",
+    "2": "B-advmod",
+    "3": "B-amod",
+    "4": "B-aux",
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+    "6": "B-cc",
+    "7": "B-ccomp",
+    "8": "B-compound",
+    "9": "B-cop",
+    "10": "B-csubj",
+    "11": "B-dep",
+    "12": "B-det",
+    "13": "B-discourse",
+    "14": "B-dislocated",
+    "15": "B-fixed",
+    "16": "B-mark",
+    "17": "B-nmod",
+    "18": "B-nsubj",
+    "19": "B-nummod",
+    "20": "B-obj",
+    "21": "B-obl",
+    "22": "B-punct",
+    "23": "B-root",
+    "24": "I-acl",
+    "25": "I-advcl",
+    "26": "I-advmod",
+    "27": "I-amod",
+    "28": "I-aux",
+    "29": "I-case",
+    "30": "I-cc",
+    "31": "I-ccomp",
+    "32": "I-compound",
+    "33": "I-cop",
+    "34": "I-csubj",
+    "35": "I-dep",
+    "36": "I-det",
+    "37": "I-discourse",
+    "38": "I-dislocated",
+    "39": "I-fixed",
+    "40": "I-mark",
+    "41": "I-nmod",
+    "42": "I-nsubj",
+    "43": "I-nummod",
+    "44": "I-obj",
+    "45": "I-obl",
+    "46": "I-punct",
+    "47": "I-root"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
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+    "B-csubj": 10,
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+    "B-punct": 22,
+    "B-root": 23,
+    "I-acl": 24,
+    "I-advcl": 25,
+    "I-advmod": 26,
+    "I-amod": 27,
+    "I-aux": 28,
+    "I-case": 29,
+    "I-cc": 30,
+    "I-ccomp": 31,
+    "I-compound": 32,
+    "I-cop": 33,
+    "I-csubj": 34,
+    "I-dep": 35,
+    "I-det": 36,
+    "I-discourse": 37,
+    "I-dislocated": 38,
+    "I-fixed": 39,
+    "I-mark": 40,
+    "I-nmod": 41,
+    "I-nsubj": 42,
+    "I-nummod": 43,
+    "I-obj": 44,
+    "I-obl": 45,
+    "I-punct": 46,
+    "I-root": 47
+  },
+  "layer_norm_eps": 1e-07,
+  "max_position_embeddings": 512,
+  "max_relative_positions": -1,
+  "model_type": "deberta-v2",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "pooler_dropout": 0,
+  "pooler_hidden_act": "gelu",
+  "pooler_hidden_size": 768,
+  "pos_att_type": null,
+  "position_biased_input": true,
+  "relative_attention": false,
+  "tokenizer_class": "DebertaV2TokenizerFast",
+  "torch_dtype": "float32",
+  "transformers_version": "4.19.4",
+  "type_vocab_size": 0,
+  "vocab_size": 32000
+}

deprel/pytorch_model.bin

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deprel/special_tokens_map.json

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deprel/spm.model

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deprel/tokenizer_config.json

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tagger/config.json

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+{
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+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "finetuning_task": "pos",
+  "hidden_act": "gelu",
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+}

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tagger/special_tokens_map.json

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tagger/tokenizer_config.json

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tokenizer_config.json

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