Xunzi-Qwen1.5-4B-upos

demo/2024-07-05/lzh-qwen-upos.ipynb

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+{
+  "nbformat":4,
+  "nbformat_minor":0,
+  "metadata":{
+    "colab":{ "name":"古典中国語生成AIモデルの系列ラベリングによるUPOS品詞付与" },
+    "kernelspec":{ "name":"python3" },
+    "accelerator": "GPU"
+  },
+  "cells":[
+    {
+      "cell_type":"markdown",
+      "metadata":{ "colab_type":"text" },
+      "source":[
+        "Xunzi-Qwen1.5-4Bによる系列ラベリング"
+      ]
+    },
+    {
+      "cell_type":"code",
+      "metadata":{ "colab_type":"code" },
+      "source": [
+        "!pip install transformers accelerate\n",
+        "!test -d Xunzi-Qwen1.5-4B || env GIT_LFS_SKIP_SMUDGE=1 git clone --depth=1 https://www.modelscope.cn/Xunzillm4cc/Xunzi-Qwen1.5-4B.git\n",
+        "from transformers import AutoTokenizer,AutoModelForTokenClassification,TokenClassificationPipeline\n",
+        "from transformers.utils import cached_file\n",
+        "tkz=AutoTokenizer.from_pretrained(\"Xunzi-Qwen1.5-4B\")\n",
+        "mdl=AutoModelForTokenClassification.from_pretrained(\"KoichiYasuoka/Xunzi-Qwen1.5-4B-upos\",trust_remote_code=True,device_map=\"auto\")\n",
+        "class TCP(TokenClassificationPipeline):\n",
+        "  def check_model_type(self,supported_models):\n",
+        "    pass\n",
+        "nlp=TCP(model=mdl,tokenizer=tkz)\n",
+        "txt=\"四十而不惑\"\n",
+        "doc=nlp(txt)\n",
+        "for t in doc:\n",
+        "  print(txt[t[\"start\"]:t[\"end\"]],t[\"entity\"])"
+      ]
+    },
+    {
+      "cell_type":"markdown",
+      "metadata":{ "colab_type":"text" },
+      "source":[
+        "トークナイザを単文字化"
+      ]
+    },
+    {
+      "cell_type":"code",
+      "metadata":{ "colab_type":"code" },
+      "source": [
+        "tkz=AutoTokenizer.from_pretrained(\"KoichiYasuoka/Xunzi-Qwen1.5-4B-upos\")\n",
+        "nlp=TCP(model=mdl,tokenizer=tkz)\n",
+        "txt=\"四十而不惑\"\n",
+        "doc=nlp(txt)\n",
+        "for t in doc:\n",
+        "  print(txt[t[\"start\"]:t[\"end\"]],t[\"entity\"])"
+      ]
+    },
+    {
+      "cell_type":"markdown",
+      "metadata":{ "colab_type":"text" },
+      "source":[
+        "出力部に逆方向のBellman-Fordを追加"
+      ]
+    },
+    {
+      "cell_type":"code",
+      "metadata":{ "colab_type":"code" },
+      "source": [
+        "class BFP(TokenClassificationPipeline):\n",
+        "  def __init__(self,**kwargs):\n",
+        "    import numpy\n",
+        "    super().__init__(**kwargs)\n",
+        "    x=self.model.config.label2id\n",
+        "    y=[k for k in x if not k.startswith(\"I-\")]\n",
+        "    self.transition=numpy.full((len(x),len(x)),numpy.nan)\n",
+        "    for k,v in x.items():\n",
+        "      for j in [\"I-\"+k[2:]] if k.startswith(\"B-\") else [k]+y if k.startswith(\"I-\") else y:\n",
+        "        self.transition[v,x[j]]=0\n",
+        "  def check_model_type(self,supported_models):\n",
+        "    pass\n",
+        "  def postprocess(self,model_outputs,**kwargs):\n",
+        "    import numpy\n",
+        "    if \"logits\" not in model_outputs:\n",
+        "      return self.postprocess(model_outputs[0],**kwargs)\n",
+        "    m=model_outputs[\"logits\"][0].numpy()\n",
+        "    e=numpy.exp(m-numpy.max(m,axis=-1,keepdims=True))\n",
+        "    z=e/e.sum(axis=-1,keepdims=True)\n",
+        "    for i in range(m.shape[0]-1,0,-1):\n",
+        "      m[i-1]+=numpy.nanmax(m[i]+self.transition,axis=1)\n",
+        "    k=[numpy.nanargmax(m[0])]\n",
+        "    for i in range(1,m.shape[0]):\n",
+        "      k.append(numpy.nanargmax(m[i]+self.transition[k[-1]]))\n",
+        "    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]\n",
+        "    if \"aggregation_strategy\" in kwargs and kwargs[\"aggregation_strategy\"]!=\"none\":\n",
+        "      for i,t in reversed(list(enumerate(w))):\n",
+        "        p=t.pop(\"entity\")\n",
+        "        if p.startswith(\"I-\"):\n",
+        "          w[i-1][\"score\"]=min(w[i-1][\"score\"],t[\"score\"])\n",
+        "          w[i-1][\"end\"]=w.pop(i)[\"end\"]\n",
+        "        elif p.startswith(\"B-\"):\n",
+        "          t[\"entity_group\"]=p[2:]\n",
+        "        else:\n",
+        "          t[\"entity_group\"]=p\n",
+        "    for t in w:\n",
+        "      t[\"text\"]=model_outputs[\"sentence\"][t[\"start\"]:t[\"end\"]]\n",
+        "    return w\n",
+        "nlp=BFP(model=mdl,tokenizer=tkz)\n",
+        "txt=\"四十而不惑\"\n",
+        "doc=nlp(txt)\n",
+        "for t in doc:\n",
+        "  print(txt[t[\"start\"]:t[\"end\"]],t[\"entity\"])"
+      ]
+    }
+  ]
+}