Zero_Shot_Pipeline_for_NER.ipynb

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    {
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      "source": [
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        {
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            "Collecting transformers==3.1.0\n",
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            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from transformers==3.1.0) (2.23.0)\n",
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            "Collecting tokenizers==0.8.1.rc2\n",
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            "Collecting sacremoses\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7d/34/09d19aff26edcc8eb2a01bed8e98f13a1537005d31e95233fd48216eed10/sacremoses-0.0.43.tar.gz (883kB)\n",
            "\u001b[K     |████████████████████████████████| 890kB 47.5MB/s \n",
            "\u001b[?25hCollecting sentencepiece!=0.1.92\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/14/67/e42bd1181472c95c8cda79305df848264f2a7f62740995a46945d9797b67/sentencepiece-0.1.95-cp36-cp36m-manylinux2014_x86_64.whl (1.2MB)\n",
            "\u001b[K     |████████████████████████████████| 1.2MB 48.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->transformers==3.1.0) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->transformers==3.1.0) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->transformers==3.1.0) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->transformers==3.1.0) (2020.12.5)\n",
            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from packaging->transformers==3.1.0) (2.4.7)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers==3.1.0) (1.15.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers==3.1.0) (7.1.2)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers==3.1.0) (1.0.0)\n",
            "Building wheels for collected packages: sacremoses\n",
            "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sacremoses: filename=sacremoses-0.0.43-cp36-none-any.whl size=893261 sha256=032910e60912757ceecdbeacb6767b01ea55949eab1b7859cbdcc14f4254c5a8\n",
            "  Stored in directory: /root/.cache/pip/wheels/29/3c/fd/7ce5c3f0666dab31a50123635e6fb5e19ceb42ce38d4e58f45\n",
            "Successfully built sacremoses\n",
            "Installing collected packages: tokenizers, sacremoses, sentencepiece, transformers\n",
            "Successfully installed sacremoses-0.0.43 sentencepiece-0.1.95 tokenizers-0.8.1rc2 transformers-3.1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TiU_ES5tzpMH"
      },
      "source": [
        "from transformers import pipeline"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "spkccRiv0CB3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299,
          "referenced_widgets": [
            "01ac362318314891b1a07f32c4e692ab",
            "ed16769572664d0593eca9893f10ba2a",
            "51e24deab6c54435805c109cf1352fcc",
            "05365cc7ef3e464b8ac4ce43628c1cab",
            "a914dd79aa7b488da082858b775c82f0",
            "eda026412514433a8a1d3eaa1045c32e",
            "3ef79d74508041d7a0940067d09da3f3",
            "a4f67162f02241d7976e0d20268b4282",
            "dca7068841fd4722b69c107df8066667",
            "9e7e15cab1924580a7857d3de01c335b",
            "261f10714d8a445298d9625e9517baaa",
            "b5620b965d2c4ea6aa4bb1f401479f6f",
            "5f3eb66a1d1c47ba88776fa6842c252a",
            "4dd776b4756b479ca59ba4733ae61277",
            "8319497703154199b4ce42a630a8a929",
            "51ecd29167144bb7929ec5330de27546",
            "1522523a6d8f403e9f1f90e9d514de14",
            "40a029c7245f450e8046cf9b708d6196",
            "551c1a4b7986454499914b8c217b0936",
            "d36e0c67f3214618958155d009187dc3",
            "39582361c0844bf2a276416f5c83989e",
            "6c46a325effe48899a83b84904a73f86",
            "45aa0a4b84ad49b8921507ea6f9ca35f",
            "2c50b7c052e546a39cf224f9f48b00fb",
            "ddfbdbde0b5b4fbe9fc48931ad8211f6",
            "4e6b062b16724fbdb0fe2ba9b2197b71",
            "153d84d6890c46b7ad39d64d5bc8e971",
            "8fa0a59e0db74ed3a5f505254722227c",
            "4e615be9f4274f30b9ca54a3ae9e419b",
            "fe8bfc086ad848659eff1507dbfe88ca",
            "41c34fde920240af9b3fad64a99c89dd",
            "4b10f2d45f284e289edf4296a38d54f2"
          ]
        },
        "outputId": "b4aa7fcb-63bf-44e8-f74e-8f33b77cb584"
      },
      "source": [
        "#classifier = pipeline(\"zero-shot-classification\")\n",
        "classifier = pipeline(\"zero-shot-classification\", device=0) # to utilize GPU"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "01ac362318314891b1a07f32c4e692ab",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=908.0, style=ProgressStyle(description_…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "dca7068841fd4722b69c107df8066667",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=898823.0, style=ProgressStyle(descripti…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1522523a6d8f403e9f1f90e9d514de14",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=456318.0, style=ProgressStyle(descripti…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ddfbdbde0b5b4fbe9fc48931ad8211f6",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1629486723.0, style=ProgressStyle(descr…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at facebook/bart-large-mnli were not used when initializing BartForSequenceClassification: ['model.encoder.version', 'model.decoder.version']\n",
            "- This IS expected if you are initializing BartForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPretraining model).\n",
            "- This IS NOT expected if you are initializing BartForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w8KjtUuju77u"
      },
      "source": [
        "def zeroshot_NER(paragraph, candidate_labels, threshold):\n",
        "  words = [word.lower() for word in paragraph.split() if word.isalpha()]\n",
        "  NER = []\n",
        "  for word in words:\n",
        "    output = classifier(word, candidate_labels)\n",
        "\n",
        "    label =  output['labels'][0] if output['scores'][0] > threshold else '[UNK]'\n",
        "    NER.append([word, label])\n",
        "\n",
        "  return NER  "
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZU_gJaoWfYGL"
      },
      "source": [
        "from sklearn.metrics import f1_score, precision_score, recall_score\n",
        "from tqdm import tqdm\n",
        "\n",
        "\n",
        "label_maps = {'Person' : 'PER',\n",
        "              'Organization' : 'ORG',\n",
        "              'Location' : 'LOC',\n",
        "              'Miscellaneous': 'MISC', \n",
        "              '[UNK]' : 'O'}\n",
        "\n",
        "other_label_map = {'B-ORG' : 'ORG',\n",
        "                   'I-ORG' : 'ORG',\n",
        "                   'B-PER' : 'PER',\n",
        "                   'I-PER' : 'PER',\n",
        "                   'B-LOC' : 'LOC',\n",
        "                   'I-LOC' : 'LOC',\n",
        "                   'B-MISC' : 'MISC',\n",
        "                   'I-MISC' : 'MISC',\n",
        "                   'O' : 'O'}\n",
        "\n",
        "filename = '/content/test.txt'"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MUE1JsymQUWY",
        "outputId": "45ad0f22-f2b2-474e-e0f6-ad3352441da5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113
        }
      },
      "source": [
        "predictions = []\n",
        "labels = []\n",
        "\n",
        "THRESHOLD = 0.5\n",
        "with open(filename) as f:\n",
        "  for i, line in tqdm(enumerate(f)):\n",
        "    if i == 0:\n",
        "        continue\n",
        "    line = line.split()\n",
        "    if len(line) > 0 and line[0].isalpha():\n",
        "      word = line[0]\n",
        "      label = other_label_map[line[3]]\n",
        "      labels.append(label)\n",
        "      \n",
        "      out = zeroshot_NER(word, candidate_labels, THRESHOLD)\n",
        "\n",
        "      predictions.append(label_maps[out[0][1]])\n",
        "\n",
        "f1 = f1_score(labels, predictions, average='weighted')\n",
        "precision = precision_score(labels, predictions, average='weighted')\n",
        "recall = recall_score(labels, predictions, average='weighted')\n",
        "print(f\"Precision - {recall}\\nRecall - {precision}\\nf1 - {f1}\")\n",
        "'''\n",
        "Precision - 0.5756579921248187\n",
        "Recall - 0.6500091058577243\n",
        "f1 - 0.597280030256445\n",
        "'''\n",
        "\n",
        "\n",
        "# result = {}\n",
        "#'''for label in label_maps.values():\n",
        "#  pred = [x == label for x in predictions]\n",
        "#  true = [x == label for x in labels]\n",
        "#  result[label] = f1_score(true, pred, average='binary')\n",
        "\n",
        "#print(result)\n",
        "#''''''\n",
        "#50350it [22:49, 36.76it/s]\n",
        "\n",
        "#{'PER': 0.2156064461407973, 'ORG': 0.036968576709796676, 'LOC': 0.3058277462609592, 'MISC': 0.005212858384013901, 'O': 0.7298355194669998}\n",
        "#'''"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "50350it [16:35, 50.58it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Precision - 0.5756579921248187\n",
            "Recall - 0.6500091058577243\n",
            "f1 - 0.597280030256445\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'\\nPrecision - 0.5756579921248187\\nRecall - 0.6500091058577243\\nf1 - 0.597280030256445\\n'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NDYPUdOApm5_",
        "outputId": "05e824f1-f430-416f-a4ca-8be3185eac35",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113
        }
      },
      "source": [
        "predictions = []\n",
        "labels = []\n",
        "\n",
        "THRESHOLD = 0.6\n",
        "with open(filename) as f:\n",
        "  for i, line in tqdm(enumerate(f)):\n",
        "    if i == 0:\n",
        "        continue\n",
        "    line = line.split()\n",
        "    if len(line) > 0 and line[0].isalpha():\n",
        "      word = line[0]\n",
        "      label = other_label_map[line[3]]\n",
        "      labels.append(label)\n",
        "      \n",
        "      #print(line)\n",
        "      #print(word)\n",
        "      \n",
        "      out = zeroshot_NER(word, candidate_labels, THRESHOLD)\n",
        "\n",
        "      predictions.append(label_maps[out[0][1]])\n",
        "\n",
        "f1 = f1_score(labels, predictions, average='weighted')\n",
        "precision = precision_score(labels, predictions, average='weighted')\n",
        "recall = recall_score(labels, predictions, average='weighted')\n",
        "print(f\"Precision - {recall}\\nRecall - {precision}\\nf1 - {f1}\")\n",
        "'''\n",
        "Precision - 0.7299049649169553\n",
        "Recall - 0.6752435637801734\n",
        "f1 - 0.6949574012914023\n",
        "'''\n",
        "\n",
        "\n",
        "#'''\n",
        "#for label in label_maps.values():\n",
        "#  pred = [x == label for x in predictions]\n",
        "#  true = [x == label for x in labels]\n",
        "#  result[label] = f1_score(true, pred, average='binary')\n",
        "\n",
        "#print(result)\n",
        "\n",
        "#55044it [20:10, 45.48it/s]\n",
        "\n",
        "#{'PER': 0.21206682313958305, 'ORG': 0.04953560371517028, 'LOC': 0.35424354243542433, 'MISC': 0.0, 'O': 0.7353024911032029}\n",
        "#'''"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "50350it [16:38, 50.41it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Precision - 0.7299049649169553\n",
            "Recall - 0.6752435637801734\n",
            "f1 - 0.6949574012914023\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'\\nPrecision - 0.7299049649169553\\nRecall - 0.6752435637801734\\nf1 - 0.6949574012914023\\n'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ycFVmAOCH6Un",
        "outputId": "46797d40-be34-4456-8072-07559407d4e4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "predictions = []\n",
        "labels = []\n",
        "\n",
        "THRESHOLD = 0.7\n",
        "with open(filename) as f:\n",
        "  for i, line in tqdm(enumerate(f)):\n",
        "    if i == 0:\n",
        "        continue\n",
        "    line = line.split()\n",
        "    if len(line) > 0 and line[0].isalpha():\n",
        "      word = line[0]\n",
        "      label = other_label_map[line[3]]\n",
        "      labels.append(label)\n",
        "      \n",
        "      #print(line)\n",
        "      #print(word)\n",
        "      \n",
        "      out = zeroshot_NER(word, candidate_labels, THRESHOLD)\n",
        "\n",
        "      predictions.append(label_maps[out[0][1]])\n",
        "\n",
        "f1 = f1_score(labels, predictions, average='weighted')\n",
        "precision = precision_score(labels, predictions, average='weighted')\n",
        "recall = recall_score(labels, predictions, average='weighted')\n",
        "print(f\"Precision - {recall}\\nRecall - {precision}\\nf1 - {f1}\")"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "50350it [16:38, 50.45it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Precision - 0.7583562779406104\n",
            "Recall - 0.681241407473071\n",
            "f1 - 0.7027216247743684\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e_WLNPvfAakf",
        "outputId": "1d31c8ea-dd9e-4bef-a843-648d258fb119",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "candidate_labels = ['PER', 'ORG', 'LOC', 'MISC', 'O']\n",
        "predictions = []\n",
        "labels = []\n",
        "\n",
        "THRESHOLD = 0.5\n",
        "with open(filename) as f:\n",
        "  for i, line in tqdm(enumerate(f)):\n",
        "    if i == 0:\n",
        "        continue\n",
        "    line = line.split()\n",
        "    if len(line) > 0 and line[0].isalpha():\n",
        "      word = line[0]\n",
        "      label = other_label_map[line[3]]\n",
        "      labels.append(label)\n",
        "      \n",
        "      #print(line)\n",
        "      #print(word)\n",
        "      \n",
        "      out = zeroshot_NER(word, candidate_labels, THRESHOLD)\n",
        "\n",
        "      predictions.append(out[0][1])\n",
        "\n",
        "f1 = f1_score(labels, predictions, average='weighted')\n",
        "precision = precision_score(labels, predictions, average='weighted')\n",
        "recall = recall_score(labels, predictions, average='weighted')\n",
        "print(f\"Precision - {recall}\\nRecall - {precision}\\nf1 - {f1}\")"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "50350it [15:19, 54.74it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Precision - 0.017289871806258697\n",
            "Recall - 0.3744396187322082\n",
            "f1 - 0.03217303294330843\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "siuQksul5jXt",
        "outputId": "6ec19ff8-c5f0-4775-ab09-0be0f688e691",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "candidate_labels = ['PER', 'ORG', 'LOC', 'MISC', 'O']\n",
        "predictions = []\n",
        "labels = []\n",
        "\n",
        "THRESHOLD = 0.6\n",
        "with open(filename) as f:\n",
        "  for i, line in tqdm(enumerate(f)):\n",
        "    if i == 0:\n",
        "        continue\n",
        "    line = line.split()\n",
        "    if len(line) > 0 and line[0].isalpha():\n",
        "      word = line[0]\n",
        "      label = other_label_map[line[3]]\n",
        "      labels.append(label)\n",
        "      \n",
        "      #print(line)\n",
        "      #print(word)\n",
        "      \n",
        "      out = zeroshot_NER(word, candidate_labels, THRESHOLD)\n",
        "\n",
        "      predictions.append(out[0][1])\n",
        "\n",
        "f1 = f1_score(labels, predictions, average='weighted')\n",
        "precision = precision_score(labels, predictions, average='weighted')\n",
        "recall = recall_score(labels, predictions, average='weighted')\n",
        "print(f\"Precision - {recall}\\nRecall - {precision}\\nf1 - {f1}\")"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "50350it [15:22, 54.59it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Precision - 0.0042928620066909435\n",
            "Recall - 0.3801834965158039\n",
            "f1 - 0.008283681986322875\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OYtWOturH-K0",
        "outputId": "b4224b0a-4360-49c8-acbb-8b5e0fe35b7f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "candidate_labels = ['PER', 'ORG', 'LOC', 'MISC', 'O']\n",
        "predictions = []\n",
        "labels = []\n",
        "\n",
        "THRESHOLD = 0.7\n",
        "with open(filename) as f:\n",
        "  for i, line in tqdm(enumerate(f)):\n",
        "    if i == 0:\n",
        "        continue\n",
        "    line = line.split()\n",
        "    if len(line) > 0 and line[0].isalpha():\n",
        "      word = line[0]\n",
        "      label = other_label_map[line[3]]\n",
        "      labels.append(label)\n",
        "      \n",
        "      #print(line)\n",
        "      #print(word)\n",
        "      \n",
        "      out = zeroshot_NER(word, candidate_labels, THRESHOLD)\n",
        "\n",
        "      predictions.append(out[0][1])\n",
        "\n",
        "f1 = f1_score(labels, predictions, average='weighted')\n",
        "precision = precision_score(labels, predictions, average='weighted')\n",
        "recall = recall_score(labels, predictions, average='weighted')\n",
        "print(f\"Precision - {recall}\\nRecall - {precision}\\nf1 - {f1}\")"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "50350it [15:25, 54.41it/s]\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Precision - 0.0007993605115907274\n",
            "Recall - 0.4335942657285583\n",
            "f1 - 0.0015948481882812865\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hkfE6NRA0Dzy"
      },
      "source": [
        "sequence = input(\"Enter a paragraph to do NER on.\")\n",
        "candidate_labels = input(\"Enter candidate labels separated by double colon(::).\").split(\"::\")\n",
        "candidate_labels.append('[UNK]')\n",
        "\n",
        "NER = zeroshot_NER(sequence, candidate_labels, 0.4)\n",
        "for each in NER:\n",
        "  print(f\"{each[0]}<{each[1]}>\", end=\" \")"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}