chapter_08/chapter_08.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HgdxoUT-z15M"
      },
      "outputs": [],
      "source": [
        "!python3 -m pip install spacy==3.7.4"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "R-lu8SaOz15S"
      },
      "outputs": [],
      "source": [
        "!python -m spacy download en_core_web_sm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YVmasvw-z15T"
      },
      "outputs": [],
      "source": [
        "!python -m pip install nertk==0.0.4"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "6CZl0QN0z15U"
      },
      "outputs": [],
      "source": [
        "import spacy\n",
        "\n",
        "nlp = spacy.load(\"en_core_web_sm\", enable=\"tokenizer\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pKpBJCL3z15V"
      },
      "outputs": [],
      "source": [
        "!mkdir data\n",
        "!wget -P data https://raw.githubusercontent.com/explosion/projects/v3/tutorials/ner_fashion_brands/assets/fashion_brands_training.jsonl\n",
        "!wget -P data https://raw.githubusercontent.com/explosion/projects/v3/tutorials/ner_fashion_brands/assets/fashion_brands_eval.jsonl"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RtJA1rTLz15W"
      },
      "outputs": [],
      "source": [
        "!mkdir scripts\n",
        "!wget -P scripts https://raw.githubusercontent.com/explosion/projects/v3/tutorials/ner_fashion_brands/scripts/preprocess.py"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sh4CqClfz15W",
        "outputId": "e299a92d-f36f-445d-fcf9-8563475c8ad8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'_input_hash': 1653937787,\n",
            " '_session_id': None,\n",
            " '_task_hash': -1474793941,\n",
            " '_view_id': 'ner_manual',\n",
            " 'answer': 'accept',\n",
            " 'meta': {'section': 'malefashionadvice'},\n",
            " 'spans': [],\n",
            " 'text': \"It's all preference for which looks better, personally I feel that \"\n",
            "         'the more natural the hair looks the better the style, which for me '\n",
            "         'means going with a matte finish which leaves the hair looking as '\n",
            "         'natural as possible while still holding it in place',\n",
            " 'tokens': [{'end': 2, 'id': 0, 'start': 0, 'text': 'It'},\n",
            "            {'end': 4, 'id': 1, 'start': 2, 'text': \"'s\"},\n",
            "            {'end': 8, 'id': 2, 'start': 5, 'text': 'all'},\n",
            "            {'end': 19, 'id': 3, 'start': 9, 'text': 'preference'},\n",
            "            {'end': 23, 'id': 4, 'start': 20, 'text': 'for'},\n",
            "            {'end': 29, 'id': 5, 'start': 24, 'text': 'which'},\n",
            "            {'end': 35, 'id': 6, 'start': 30, 'text': 'looks'},\n",
            "            {'end': 42, 'id': 7, 'start': 36, 'text': 'better'},\n",
            "            {'end': 43, 'id': 8, 'start': 42, 'text': ','},\n",
            "            {'end': 54, 'id': 9, 'start': 44, 'text': 'personally'},\n",
            "            {'end': 56, 'id': 10, 'start': 55, 'text': 'I'},\n",
            "            {'end': 61, 'id': 11, 'start': 57, 'text': 'feel'},\n",
            "            {'end': 66, 'id': 12, 'start': 62, 'text': 'that'},\n",
            "            {'end': 70, 'id': 13, 'start': 67, 'text': 'the'},\n",
            "            {'end': 75, 'id': 14, 'start': 71, 'text': 'more'},\n",
            "            {'end': 83, 'id': 15, 'start': 76, 'text': 'natural'},\n",
            "            {'end': 87, 'id': 16, 'start': 84, 'text': 'the'},\n",
            "            {'end': 92, 'id': 17, 'start': 88, 'text': 'hair'},\n",
            "            {'end': 98, 'id': 18, 'start': 93, 'text': 'looks'},\n",
            "            {'end': 102, 'id': 19, 'start': 99, 'text': 'the'},\n",
            "            {'end': 109, 'id': 20, 'start': 103, 'text': 'better'},\n",
            "            {'end': 113, 'id': 21, 'start': 110, 'text': 'the'},\n",
            "            {'end': 119, 'id': 22, 'start': 114, 'text': 'style'},\n",
            "            {'end': 120, 'id': 23, 'start': 119, 'text': ','},\n",
            "            {'end': 126, 'id': 24, 'start': 121, 'text': 'which'},\n",
            "            {'end': 130, 'id': 25, 'start': 127, 'text': 'for'},\n",
            "            {'end': 133, 'id': 26, 'start': 131, 'text': 'me'},\n",
            "            {'end': 139, 'id': 27, 'start': 134, 'text': 'means'},\n",
            "            {'end': 145, 'id': 28, 'start': 140, 'text': 'going'},\n",
            "            {'end': 150, 'id': 29, 'start': 146, 'text': 'with'},\n",
            "            {'end': 152, 'id': 30, 'start': 151, 'text': 'a'},\n",
            "            {'end': 158, 'id': 31, 'start': 153, 'text': 'matte'},\n",
            "            {'end': 165, 'id': 32, 'start': 159, 'text': 'finish'},\n",
            "            {'end': 171, 'id': 33, 'start': 166, 'text': 'which'},\n",
            "            {'end': 178, 'id': 34, 'start': 172, 'text': 'leaves'},\n",
            "            {'end': 182, 'id': 35, 'start': 179, 'text': 'the'},\n",
            "            {'end': 187, 'id': 36, 'start': 183, 'text': 'hair'},\n",
            "            {'end': 195, 'id': 37, 'start': 188, 'text': 'looking'},\n",
            "            {'end': 198, 'id': 38, 'start': 196, 'text': 'as'},\n",
            "            {'end': 206, 'id': 39, 'start': 199, 'text': 'natural'},\n",
            "            {'end': 209, 'id': 40, 'start': 207, 'text': 'as'},\n",
            "            {'end': 218, 'id': 41, 'start': 210, 'text': 'possible'},\n",
            "            {'end': 224, 'id': 42, 'start': 219, 'text': 'while'},\n",
            "            {'end': 230, 'id': 43, 'start': 225, 'text': 'still'},\n",
            "            {'end': 238, 'id': 44, 'start': 231, 'text': 'holding'},\n",
            "            {'end': 241, 'id': 45, 'start': 239, 'text': 'it'},\n",
            "            {'end': 244, 'id': 46, 'start': 242, 'text': 'in'},\n",
            "            {'end': 250, 'id': 47, 'start': 245, 'text': 'place'}]}\n"
          ]
        }
      ],
      "source": [
        "import srsly\n",
        "from pprint import pprint\n",
        "\n",
        "training_path = \"data/fashion_brands_training.jsonl\"\n",
        "\n",
        "for row in srsly.read_jsonl(training_path):\n",
        "    pprint(row)\n",
        "    break"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "w7Xn3DiTz15Y"
      },
      "outputs": [],
      "source": [
        "nertk_input_text = []\n",
        "\n",
        "for row in srsly.read_jsonl(training_path):\n",
        "    comment = nlp(row[\"text\"])\n",
        "    comment_words = [token.text for token in comment]\n",
        "    nertk_input_text.append(comment_words)"
      ]
    },
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            "4fc4c9b691b0440381f07733b17dd242"
          ]
        },
        "id": "5aZSHza0z15Z",
        "outputId": "fb0f0031-a10f-42e3-ab7f-6976c2f9bf74"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(Label(value='Current Label: FASHION_BRAND'), HBox(children=(Button(description='None', layout=L…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2b0822fc42f340a498770296bcea8aef"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Output()"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fef771e0d09140e6ba92df8f7c2a210d"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from nertk import Entator\n",
        "\n",
        "annotator = Entator(labels=['None', 'FASHION_BRAND'],\n",
        "inputs=nertk_input_text)\n",
        "\n",
        "annotator.run()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "195YoMydz15b"
      },
      "outputs": [],
      "source": [
        "from spacy.tokens import DocBin, Span\n",
        "from utils import create_consecutive_token_sequences\n",
        "\n",
        "db = DocBin()\n",
        "for idx, (row, nerkt_tokens, nertk_entities) in enumerate(zip(srsly.read_jsonl(training_path), nertk_input_text, annotator.targets)):\n",
        "  if idx == 5:\n",
        "        break\n",
        "  doc = nlp(row[\"text\"])\n",
        "  indexes_entity_tokens = [index for index, x in enumerate(nertk_entities) if x == \"FASHION_BRAND\"]\n",
        "  span_indexes = create_consecutive_token_sequences(indexes_entity_tokens)\n",
        "  ents = []\n",
        "  label = \"FASHION_BRAND\"\n",
        "  for start,end in span_indexes:\n",
        "    span = Span(doc, start, end+1, label)\n",
        "    ents.append(span)\n",
        "  doc.ents = ents\n",
        "  db.add(doc)\n",
        "db.to_disk(\"data/nertk_training.spacy\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RoyTLTYLz15e",
        "outputId": "cca430bf-bbeb-4918-bbf5-b005aa3a92a0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[38;5;3m⚠ To generate a more effective transformer-based config (GPU-only),\n",
            "install the spacy-transformers package and re-run this command. The config\n",
            "generated now does not use transformers.\u001b[0m\n",
            "\u001b[38;5;4mℹ Generated config template specific for your use case\u001b[0m\n",
            "- Language: en\n",
            "- Pipeline: ner\n",
            "- Optimize for: efficiency\n",
            "- Hardware: CPU\n",
            "- Transformer: None\n",
            "\u001b[38;5;2m✔ Auto-filled config with all values\u001b[0m\n",
            "\u001b[38;5;2m✔ Saved config\u001b[0m\n",
            "cpu_config.cfg\n",
            "You can now add your data and train your pipeline:\n",
            "python -m spacy train cpu_config.cfg --paths.train ./train.spacy --paths.dev ./dev.spacy\n"
          ]
        }
      ],
      "source": [
        "!python -m spacy init config cpu_config.cfg --lang \"en\" --pipeline \"ner\" --optimize \"efficiency\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AWOqV9ePz15e",
        "outputId": "59508e1b-d03d-467d-d9af-3f94d44e4162"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processed 1235 documents: fashion_brands_training.spacy\n"
          ]
        }
      ],
      "source": [
        "!python ./scripts/preprocess.py ./data/fashion_brands_training.jsonl ./data/fashion_brands_training.spacy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yv3SuBdZz15e",
        "outputId": "23637526-9b4a-46aa-96bc-0138d715be8a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processed 500 documents: fashion_brands_eval.spacy\n"
          ]
        }
      ],
      "source": [
        "!python ./scripts/preprocess.py ./data/fashion_brands_eval.jsonl ./data/fashion_brands_eval.spacy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NfNf0m5Wz15e",
        "outputId": "c9778f79-f76b-45d6-8376-034697973ac7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[38;5;2m✔ Created output directory: training_cpu\u001b[0m\n",
            "\u001b[38;5;4mℹ Saving to output directory: training_cpu\u001b[0m\n",
            "\u001b[38;5;4mℹ Using CPU\u001b[0m\n",
            "\u001b[38;5;4mℹ To switch to GPU 0, use the option: --gpu-id 0\u001b[0m\n",
            "\u001b[1m\n",
            "=========================== Initializing pipeline ===========================\u001b[0m\n",
            "\u001b[38;5;2m✔ Initialized pipeline\u001b[0m\n",
            "\u001b[1m\n",
            "============================= Training pipeline =============================\u001b[0m\n",
            "\u001b[38;5;4mℹ Pipeline: ['tok2vec', 'ner']\u001b[0m\n",
            "\u001b[38;5;4mℹ Initial learn rate: 0.001\u001b[0m\n",
            "E    #       LOSS TOK2VEC  LOSS NER  ENTS_F  ENTS_P  ENTS_R  SCORE \n",
            "---  ------  ------------  --------  ------  ------  ------  ------\n",
            "  0       0          0.00     46.17    1.38    0.70   55.88    0.01\n",
            "  0     200         31.13    824.92   25.08   53.42   16.39    0.25\n",
            "  0     400         66.06    342.21   47.71   55.93   41.60    0.48\n",
            "  1     600         71.70    233.36   55.26   66.08   47.48    0.55\n",
            "  1     800        195.00    219.52   58.55   66.14   52.52    0.59\n",
            "  2    1000        157.87    162.29   59.58   66.15   54.20    0.60\n",
            "  3    1200      25495.90    279.75   62.44   74.42   53.78    0.62\n",
            "  4    1400        622.63    112.01   64.92   75.14   57.14    0.65\n",
            "  6    1600        204.39    108.66   63.85   72.34   57.14    0.64\n",
            "  7    1800        120.11     43.00   64.04   71.50   57.98    0.64\n",
            "  9    2000         69.01     29.25   63.88   76.92   54.62    0.64\n",
            " 12    2200        129.55     20.98   62.66   77.64   52.52    0.63\n",
            " 15    2400        119.96     20.22   62.59   74.85   53.78    0.63\n",
            " 19    2600         32.30      7.16   61.11   76.58   50.84    0.61\n",
            " 22    2800        299.50     32.92   62.59   74.85   53.78    0.63\n",
            " 25    3000         62.14     17.65   62.83   73.18   55.04    0.63\n",
            "\u001b[38;5;2m✔ Saved pipeline to output directory\u001b[0m\n",
            "training_cpu/model-last\n"
          ]
        }
      ],
      "source": [
        "!python -m spacy train cpu_config.cfg --output training_cpu/ --paths.train ./data/fashion_brands_training.spacy --paths.dev ./data/fashion_brands_eval.spacy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nibGjtY0z15f",
        "outputId": "78e93868-211e-4866-9361-4fa7a798ce4f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[38;5;4mℹ Using CPU\u001b[0m\n",
            "\u001b[38;5;4mℹ To switch to GPU 0, use the option: --gpu-id 0\u001b[0m\n",
            "\u001b[1m\n",
            "================================== Results ==================================\u001b[0m\n",
            "\n",
            "TOK     99.98\n",
            "NER P   75.14\n",
            "NER R   57.14\n",
            "NER F   64.92\n",
            "SPEED   27306\n",
            "\n",
            "\u001b[1m\n",
            "=============================== NER (per type) ===============================\u001b[0m\n",
            "\n",
            "                    P       R       F\n",
            "FASHION_BRAND   75.14   57.14   64.92\n",
            "\n",
            "\u001b[38;5;2m✔ Saved results to training_cpu/metrics.json\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "!python -m spacy evaluate training_cpu/model-best ./data/fashion_brands_eval.spacy --output training_cpu/metrics.json"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KivgQppbz15f",
        "outputId": "820ca5a2-42fc-4ae0-a470-36e5311f2e2f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[38;5;3m⚠ To generate a more effective transformer-based config (GPU-only),\n",
            "install the spacy-transformers package and re-run this command. The config\n",
            "generated now does not use transformers.\u001b[0m\n",
            "\u001b[38;5;4mℹ Generated config template specific for your use case\u001b[0m\n",
            "- Language: en\n",
            "- Pipeline: ner\n",
            "- Optimize for: accuracy\n",
            "- Hardware: GPU\n",
            "- Transformer: None\n",
            "\u001b[38;5;2m✔ Auto-filled config with all values\u001b[0m\n",
            "\u001b[38;5;2m✔ Saved config\u001b[0m\n",
            "gpu_config.cfg\n",
            "You can now add your data and train your pipeline:\n",
            "python -m spacy train gpu_config.cfg --paths.train ./train.spacy --paths.dev ./dev.spacy\n"
          ]
        }
      ],
      "source": [
        "!python -m spacy init config gpu_config.cfg -l \"en\" -p \"ner\" --optimize \"accuracy\" --gpu"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python -m pip install spacy-transformers==1.3.5"
      ],
      "metadata": {
        "id": "LwURaMpQ2Tia"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!python -m spacy download en_core_web_lg"
      ],
      "metadata": {
        "id": "3B243bwV2CKf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZOMVcjx4z15f",
        "outputId": "8b68ba60-a863-4543-b97e-1ef1ed7c66a9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[38;5;4mℹ Saving to output directory: training_gpu\u001b[0m\n",
            "\u001b[38;5;4mℹ Using GPU: 0\u001b[0m\n",
            "\u001b[1m\n",
            "=========================== Initializing pipeline ===========================\u001b[0m\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
            "  _torch_pytree._register_pytree_node(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/utils/generic.py:309: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
            "  _torch_pytree._register_pytree_node(\n",
            "\u001b[38;5;2m✔ Initialized pipeline\u001b[0m\n",
            "\u001b[1m\n",
            "============================= Training pipeline =============================\u001b[0m\n",
            "\u001b[38;5;4mℹ Pipeline: ['tok2vec', 'ner']\u001b[0m\n",
            "\u001b[38;5;4mℹ Initial learn rate: 0.001\u001b[0m\n",
            "E    #       LOSS TOK2VEC  LOSS NER  ENTS_F  ENTS_P  ENTS_R  SCORE \n",
            "---  ------  ------------  --------  ------  ------  ------  ------\n",
            "  0       0          0.00     46.17    1.53    0.77   60.08    0.02\n",
            "  0     200          7.50    592.19   51.11   45.70   57.98    0.51\n",
            "  0     400        206.80    387.08   17.20   58.54   10.08    0.17\n",
            "  1     600         34.33    319.89   60.75   68.42   54.62    0.61\n",
            "  1     800        130.67    315.17   66.52   68.92   64.29    0.67\n",
            "  2    1000       1692.92    279.95   59.50   54.77   65.13    0.60\n",
            "  3    1200       3226.35    288.68   67.52   68.70   66.39    0.68\n",
            "  4    1400      22818.35    286.93   67.49   66.53   68.49    0.67\n",
            "  6    1600       5428.51    337.83   63.64   79.75   52.94    0.64\n",
            "  7    1800        332.24    159.22   62.78   78.98   52.10    0.63\n",
            "  9    2000        159.34    146.61   66.67   63.16   70.59    0.67\n",
            " 12    2200         91.15     84.56   66.07   71.01   61.76    0.66\n",
            " 15    2400         94.09     59.03   70.90   73.97   68.07    0.71\n",
            " 19    2600        239.72     33.19   67.69   70.45   65.13    0.68\n",
            " 22    2800         94.98     44.76   70.32   77.00   64.71    0.70\n",
            " 25    3000         49.99     20.55   71.00   79.27   64.29    0.71\n",
            " 29    3200       1426.49     59.20   68.95   75.50   63.45    0.69\n",
            " 32    3400        339.31     40.27   64.73   69.05   60.92    0.65\n",
            " 35    3600        257.82     34.68   67.61   76.60   60.50    0.68\n",
            " 38    3800         96.69     29.91   68.95   70.31   67.65    0.69\n",
            " 42    4000         58.22     22.78   68.51   75.63   62.61    0.69\n",
            " 45    4200          4.39      1.74   69.04   68.75   69.33    0.69\n",
            " 48    4400         26.87      7.33   66.52   68.92   64.29    0.67\n",
            " 52    4600        260.38     19.08   67.57   72.82   63.03    0.68\n",
            "\u001b[38;5;2m✔ Saved pipeline to output directory\u001b[0m\n",
            "training_gpu/model-last\n"
          ]
        }
      ],
      "source": [
        "!python -m spacy train gpu_config.cfg --output training_gpu/ --paths.train ./data/fashion_brands_training.spacy --paths.dev ./data/fashion_brands_eval.spacy --gpu-id 0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DD7M-PUwz15f",
        "outputId": "5a72abb0-41c9-4751-e2c0-6387c3813bff"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[38;5;4mℹ Using GPU: 0\u001b[0m\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
            "  _torch_pytree._register_pytree_node(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/utils/generic.py:309: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
            "  _torch_pytree._register_pytree_node(\n",
            "\u001b[1m\n",
            "================================== Results ==================================\u001b[0m\n",
            "\n",
            "TOK     99.98\n",
            "NER P   79.27\n",
            "NER R   64.29\n",
            "NER F   71.00\n",
            "SPEED   11122\n",
            "\n",
            "\u001b[1m\n",
            "=============================== NER (per type) ===============================\u001b[0m\n",
            "\n",
            "                    P       R       F\n",
            "FASHION_BRAND   79.27   64.29   71.00\n",
            "\n",
            "\u001b[38;5;2m✔ Saved results to training_gpu/metrics.json\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "!python -m spacy evaluate training_gpu/model-best ./data/fashion_brands_eval.spacy --output training_gpu/metrics.json --gpu-id 0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        },
        "id": "i5xaEr0-z15g",
        "outputId": "d4a5db08-358a-4383-9e43-301c2b35ee1d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/spacy/util.py:1740: UserWarning: [W111] Jupyter notebook detected: if using `prefer_gpu()` or `require_gpu()`, include it in the same cell right before `spacy.load()` to ensure that the model is loaded on the correct device. More information: http://spacy.io/usage/v3#jupyter-notebook-gpu\n",
            "  warnings.warn(Warnings.W111)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">\n",
              "<mark class=\"entity\" style=\"background: #ddd; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
              "    Givenchy\n",
              "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">FASHION_BRAND</span>\n",
              "</mark>\n",
              " is looking at buying U.K. startup for $1 billion</div></span>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "from spacy import displacy\n",
        "\n",
        "nlp = spacy.load('training_gpu/model-best')\n",
        "\n",
        "sentence = \"Givenchy is looking at buying U.K. startup for $1 billion\"\n",
        "doc = nlp(sentence)\n",
        "displacy.render(doc, style=\"ent\", jupyter=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "8no_xpKuz15g",
        "outputId": "04eb3540-56e0-449e-b516-982ba0c3dc57"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">Givenchy is looking at buying \n",
              "<mark class=\"entity\" style=\"background: #feca74; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
              "    U.K.\n",
              "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">GPE</span>\n",
              "</mark>\n",
              " startup for \n",
              "<mark class=\"entity\" style=\"background: #e4e7d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
              "    $1 billion\n",
              "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">MONEY</span>\n",
              "</mark>\n",
              "</div></span>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "nlp = spacy.load('en_core_web_sm')\n",
        "\n",
        "sentence = \"Givenchy is looking at buying U.K. startup for $1 billion\"\n",
        "doc = nlp(sentence)\n",
        "displacy.render(doc, style=\"ent\", jupyter=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b6Q2aK-2z15g"
      },
      "outputs": [],
      "source": [
        "!python -m spacy package training_gpu/model-best ./ --name \"ner_fashion_brands\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CS95up9wz15g"
      },
      "outputs": [],
      "source": [
        "!python -m pip install en_ner_fashion_brands-0.0.0/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "bH9vAMgrz15h",
        "outputId": "32ac7e5e-6739-4a65-d070-07db340f3e42"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">\n",
              "<mark class=\"entity\" style=\"background: #ddd; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
              "    Givenchy\n",
              "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">FASHION_BRAND</span>\n",
              "</mark>\n",
              " is looking at buying U.K. startup for $1 billion</div></span>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "import en_ner_fashion_brands\n",
        "from spacy import displacy\n",
        "\n",
        "nlp = en_ner_fashion_brands.load()\n",
        "sentence = \"Givenchy is looking at buying U.K. startup for $1 billion\"\n",
        "\n",
        "doc = nlp(sentence)\n",
        "displacy.render(doc, style=\"ent\", jupyter=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BavR9FAYz15h",
        "outputId": "a9c04a23-f439-490c-abe6-6e5d0907ba68"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m\n",
            "=========================== Initializing pipeline ===========================\u001b[0m\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
            "  _torch_pytree._register_pytree_node(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/utils/generic.py:309: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
            "  _torch_pytree._register_pytree_node(\n",
            "/usr/local/lib/python3.10/dist-packages/spacy/training/initialize.py:150: UserWarning: [W113] Sourced component 'ner' may not work as expected: source vectors are not identical to current pipeline vectors.\n",
            "  warnings.warn(Warnings.W113.format(name=sourced_component))\n",
            "\u001b[38;5;2m✔ Initialized pipeline\u001b[0m\n",
            "\u001b[1m\n",
            "============================ Serializing to disk ============================\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "!python -m spacy assemble combined_ner.cfg pipelines/fashion_ner_with_base_entities"
      ]
    },
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      "cell_type": "code",
      "execution_count": 39,
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        },
        "id": "8QVlt2H4z15h",
        "outputId": "7b7d3c90-a562-47d6-9beb-85eb28728e7c"
      },
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            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">\n",
              "<mark class=\"entity\" style=\"background: #ddd; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
              "    Givenchy\n",
              "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">FASHION_BRAND</span>\n",
              "</mark>\n",
              " is looking at buying \n",
              "<mark class=\"entity\" style=\"background: #feca74; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
              "    U.K.\n",
              "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">GPE</span>\n",
              "</mark>\n",
              " startup for \n",
              "<mark class=\"entity\" style=\"background: #e4e7d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
              "    $1 billion\n",
              "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">MONEY</span>\n",
              "</mark>\n",
              "</div></span>"
            ]
          },
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        }
      ],
      "source": [
        "import spacy\n",
        "from spacy import displacy\n",
        "\n",
        "nlp = spacy.load(\"pipelines/fashion_ner_with_base_entities\")\n",
        "\n",
        "sentence = \"Givenchy is looking at buying U.K. startup for $1 billion\"\n",
        "doc = nlp(sentence)\n",
        "\n",
        "displacy.render(doc, style=\"ent\", jupyter=True)"
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