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{
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"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "gczeIWL7Yqml",
"outputId": "353b3804-fb2b-4ead-d190-69cc7ef11ea6"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting datasets\n",
" Downloading datasets-2.18.0-py3-none-any.whl (510 kB)\n",
"\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/510.5 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m61.4/510.5 kB\u001b[0m \u001b[31m1.7 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m \u001b[32m501.8/510.5 kB\u001b[0m \u001b[31m7.3 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m510.5/510.5 kB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets) (3.13.1)\n",
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"Collecting dill<0.3.9,>=0.3.0 (from datasets)\n",
" Downloading dill-0.3.8-py3-none-any.whl (116 kB)\n",
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"Requirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (4.66.2)\n",
"Collecting xxhash (from datasets)\n",
" Downloading xxhash-3.4.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hCollecting multiprocess (from datasets)\n",
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"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n",
"Installing collected packages: xxhash, dill, multiprocess, datasets\n",
"Successfully installed datasets-2.18.0 dill-0.3.8 multiprocess-0.70.16 xxhash-3.4.1\n",
"--2024-03-16 03:52:00-- https://raw.githubusercontent.com/sighsmile/conlleval/master/conlleval.py\n",
"Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
"Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 7502 (7.3K) [text/plain]\n",
"Saving to: ‘conlleval.py’\n",
"\n",
"conlleval.py 100%[===================>] 7.33K --.-KB/s in 0s \n",
"\n",
"2024-03-16 03:52:00 (96.5 MB/s) - ‘conlleval.py’ saved [7502/7502]\n",
"\n"
]
}
],
"source": [
"!pip3 install datasets\n",
"!wget https://raw.githubusercontent.com/sighsmile/conlleval/master/conlleval.py\n"
]
},
{
"cell_type": "code",
"source": [
"!pip install presidio-analyzer"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "x5eLSkVqlhh6",
"outputId": "9cf46693-5e60-425d-8693-22a5df24fea0"
},
"execution_count": 36,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting presidio-analyzer\n",
" Downloading presidio_analyzer-2.2.353-py3-none-any.whl (85 kB)\n",
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"\u001b[?25hRequirement already satisfied: spacy<4.0.0,>=3.4.4 in /usr/local/lib/python3.10/dist-packages (from presidio-analyzer) (3.7.4)\n",
"Requirement already satisfied: regex in /usr/local/lib/python3.10/dist-packages (from presidio-analyzer) (2023.12.25)\n",
"Collecting tldextract (from presidio-analyzer)\n",
" Downloading tldextract-5.1.1-py3-none-any.whl (97 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m97.7/97.7 kB\u001b[0m \u001b[31m9.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hRequirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from presidio-analyzer) (6.0.1)\n",
"Collecting phonenumbers<9.0.0,>=8.12 (from presidio-analyzer)\n",
" Downloading phonenumbers-8.13.32-py2.py3-none-any.whl (2.6 MB)\n",
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"Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from spacy<4.0.0,>=3.4.4->presidio-analyzer) (1.0.5)\n",
"Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.10/dist-packages (from spacy<4.0.0,>=3.4.4->presidio-analyzer) (1.0.10)\n",
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"Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.10/dist-packages (from spacy<4.0.0,>=3.4.4->presidio-analyzer) (3.0.9)\n",
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"Requirement already satisfied: srsly<3.0.0,>=2.4.3 in /usr/local/lib/python3.10/dist-packages (from spacy<4.0.0,>=3.4.4->presidio-analyzer) (2.4.8)\n",
"Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /usr/local/lib/python3.10/dist-packages (from spacy<4.0.0,>=3.4.4->presidio-analyzer) (2.0.10)\n",
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"Installing collected packages: phonenumbers, requests-file, tldextract, presidio-analyzer\n",
"Successfully installed phonenumbers-8.13.32 presidio-analyzer-2.2.353 requests-file-2.0.0 tldextract-5.1.1\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!pip install flair"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "XWdmM-gGmHV-",
"outputId": "42e0e840-89df-4a99-a3d7-8d78fc7beff0"
},
"execution_count": 38,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
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"Building wheels for collected packages: langdetect, pptree, sqlitedict\n",
" Building wheel for langdetect (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for langdetect: filename=langdetect-1.0.9-py3-none-any.whl size=993227 sha256=9da87eaaff56d6d1421c337ae61089e29874a2de99038123b209c2cf6ffe4791\n",
" Stored in directory: /root/.cache/pip/wheels/95/03/7d/59ea870c70ce4e5a370638b5462a7711ab78fba2f655d05106\n",
" Building wheel for pptree (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for pptree: filename=pptree-3.1-py3-none-any.whl size=4609 sha256=be019012224ff0981466d5ef57193c243fbbc1542c10b46f3ed8f17e84f74b0e\n",
" Stored in directory: /root/.cache/pip/wheels/9f/b6/0e/6f26eb9e6eb53ff2107a7888d72b5a6a597593956113037828\n",
" Building wheel for sqlitedict (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for sqlitedict: filename=sqlitedict-2.1.0-py3-none-any.whl size=16862 sha256=056a323511a15e5bdbb990ad53b061cdb301623fdbf4a77ead3f71402b27bf97\n",
" Stored in directory: /root/.cache/pip/wheels/79/d6/e7/304e0e6cb2221022c26d8161f7c23cd4f259a9e41e8bbcfabd\n",
"Successfully built langdetect pptree sqlitedict\n",
"Installing collected packages: sqlitedict, pptree, janome, urllib3, semver, segtok, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, langdetect, jmespath, ftfy, deprecated, conllu, nvidia-cusparse-cu12, nvidia-cudnn-cu12, botocore, wikipedia-api, s3transfer, nvidia-cusolver-cu12, mpld3, bpemb, boto3, pytorch-revgrad, accelerate, transformer-smaller-training-vocab, flair\n",
" Attempting uninstall: urllib3\n",
" Found existing installation: urllib3 2.0.7\n",
" Uninstalling urllib3-2.0.7:\n",
" Successfully uninstalled urllib3-2.0.7\n",
"Successfully installed accelerate-0.28.0 boto3-1.34.64 botocore-1.34.64 bpemb-0.3.4 conllu-4.5.3 deprecated-1.2.14 flair-0.13.1 ftfy-6.2.0 janome-0.5.0 jmespath-1.0.1 langdetect-1.0.9 mpld3-0.5.10 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.99 nvidia-nvtx-cu12-12.1.105 pptree-3.1 pytorch-revgrad-0.2.0 s3transfer-0.10.1 segtok-1.5.11 semver-3.0.2 sqlitedict-2.1.0 transformer-smaller-training-vocab-0.3.3 urllib3-1.26.18 wikipedia-api-0.6.0\n"
]
},
{
"output_type": "display_data",
"data": {
"application/vnd.colab-display-data+json": {
"pip_warning": {
"packages": [
"urllib3"
]
},
"id": "dfdc4a89fa71429587bc109f13908415"
}
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"import os\n",
"\n",
"os.environ[\"KERAS_BACKEND\"] = \"tensorflow\"\n",
"\n",
"import os\n",
"import keras\n",
"import numpy as np\n",
"import tensorflow as tf\n",
"from keras import layers\n",
"from datasets import load_dataset\n",
"from collections import Counter\n",
"from conlleval import evaluate\n",
"\n",
"import pandas as pd\n",
"from google.colab import files\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from transformers import AutoModel, AutoTokenizer\n",
"\n",
"import logging\n",
"from typing import Optional, List, Tuple, Set\n",
"from presidio_analyzer import (\n",
" RecognizerResult,\n",
" EntityRecognizer,\n",
" AnalysisExplanation,\n",
")\n",
"from presidio_analyzer.nlp_engine import NlpArtifacts\n",
"\n",
"from flair.data import Sentence\n",
"from flair.models import SequenceTagger\n"
],
"metadata": {
"id": "9FxNt5pZY0e2"
},
"execution_count": 19,
"outputs": []
},
{
"cell_type": "code",
"source": [
"class TransformerBlock(layers.Layer):\n",
" def __init__(self, embed_dim, num_heads, ff_dim, rate=0.1):\n",
" super().__init__()\n",
" self.att = keras.layers.MultiHeadAttention(\n",
" num_heads=num_heads, key_dim=embed_dim\n",
" )\n",
" self.ffn = keras.Sequential(\n",
" [\n",
" keras.layers.Dense(ff_dim, activation=\"relu\"),\n",
" keras.layers.Dense(embed_dim),\n",
" ]\n",
" )\n",
" self.layernorm1 = keras.layers.LayerNormalization(epsilon=1e-6)\n",
" self.layernorm2 = keras.layers.LayerNormalization(epsilon=1e-6)\n",
" self.dropout1 = keras.layers.Dropout(rate)\n",
" self.dropout2 = keras.layers.Dropout(rate)\n",
"\n",
" def call(self, inputs, training=False):\n",
" attn_output = self.att(inputs, inputs)\n",
" attn_output = self.dropout1(attn_output, training=training)\n",
" out1 = self.layernorm1(inputs + attn_output)\n",
" ffn_output = self.ffn(out1)\n",
" ffn_output = self.dropout2(ffn_output, training=training)\n",
" return self.layernorm2(out1 + ffn_output)\n"
],
"metadata": {
"id": "a2ro_nntY-FC"
},
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"source": [
"class TokenAndPositionEmbedding(layers.Layer):\n",
" def __init__(self, maxlen, vocab_size, embed_dim):\n",
" super().__init__()\n",
" self.token_emb = keras.layers.Embedding(\n",
" input_dim=vocab_size, output_dim=embed_dim\n",
" )\n",
" self.pos_emb = keras.layers.Embedding(input_dim=maxlen, output_dim=embed_dim)\n",
"\n",
" def call(self, inputs):\n",
" maxlen = tf.shape(inputs)[-1]\n",
" positions = tf.range(start=0, limit=maxlen, delta=1)\n",
" position_embeddings = self.pos_emb(positions)\n",
" token_embeddings = self.token_emb(inputs)\n",
" return token_embeddings + position_embeddings"
],
"metadata": {
"id": "jg0WkejPZBn8"
},
"execution_count": 5,
"outputs": []
},
{
"cell_type": "code",
"source": [
"class NERModel(keras.Model):\n",
" def __init__(\n",
" self, num_tags, vocab_size, maxlen=128, embed_dim=32, num_heads=2, ff_dim=32\n",
" ):\n",
" super().__init__()\n",
" self.embedding_layer = TokenAndPositionEmbedding(maxlen, vocab_size, embed_dim)\n",
" self.transformer_block = TransformerBlock(embed_dim, num_heads, ff_dim)\n",
" self.dropout1 = layers.Dropout(0.1)\n",
" self.ff = layers.Dense(ff_dim, activation=\"relu\")\n",
" self.dropout2 = layers.Dropout(0.1)\n",
" self.ff_final = layers.Dense(num_tags, activation=\"softmax\")\n",
"\n",
" def call(self, inputs, training=False):\n",
" x = self.embedding_layer(inputs)\n",
" x = self.transformer_block(x)\n",
" x = self.dropout1(x, training=training)\n",
" x = self.ff(x)\n",
" x = self.dropout2(x, training=training)\n",
" x = self.ff_final(x)\n",
" return x"
],
"metadata": {
"id": "HeMvk_zKZFXy"
},
"execution_count": 6,
"outputs": []
},
{
"cell_type": "code",
"source": [
"conll_data = load_dataset(\"conll2003\")\n"
],
"metadata": {
"id": "weGmhigxZMaT"
},
"execution_count": 7,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def dataset_to_dataframe(dataset):\n",
" data_dict = {key: dataset[key] for key in dataset.features}\n",
" return pd.DataFrame(data_dict)\n",
"\n",
"# Combine all splits (train, validation, test) into a single DataFrame\n",
"conll_df = pd.concat([dataset_to_dataframe(conll_data[split]) for split in conll_data.keys()])"
],
"metadata": {
"id": "SEvvIFAgdcAF"
},
"execution_count": 8,
"outputs": []
},
{
"cell_type": "code",
"source": [
"csv_file_path = \"conll_data.csv\"\n",
"conll_df.to_csv(csv_file_path, index=False)\n",
"\n",
"# Download the CSV file to local machine\n",
"\n",
"files.download(csv_file_path)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"id": "UejgBp-Ng_l_",
"outputId": "98b45e90-3b08-4857-f7eb-42e9a319eb29"
},
"execution_count": 7,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.Javascript object>"
],
"application/javascript": [
"\n",
" async function download(id, filename, size) {\n",
" if (!google.colab.kernel.accessAllowed) {\n",
" return;\n",
" }\n",
" const div = document.createElement('div');\n",
" const label = document.createElement('label');\n",
" label.textContent = `Downloading \"${filename}\": `;\n",
" div.appendChild(label);\n",
" const progress = document.createElement('progress');\n",
" progress.max = size;\n",
" div.appendChild(progress);\n",
" document.body.appendChild(div);\n",
"\n",
" const buffers = [];\n",
" let downloaded = 0;\n",
"\n",
" const channel = await google.colab.kernel.comms.open(id);\n",
" // Send a message to notify the kernel that we're ready.\n",
" channel.send({})\n",
"\n",
" for await (const message of channel.messages) {\n",
" // Send a message to notify the kernel that we're ready.\n",
" channel.send({})\n",
" if (message.buffers) {\n",
" for (const buffer of message.buffers) {\n",
" buffers.push(buffer);\n",
" downloaded += buffer.byteLength;\n",
" progress.value = downloaded;\n",
" }\n",
" }\n",
" }\n",
" const blob = new Blob(buffers, {type: 'application/binary'});\n",
" const a = document.createElement('a');\n",
" a.href = window.URL.createObjectURL(blob);\n",
" a.download = filename;\n",
" div.appendChild(a);\n",
" a.click();\n",
" div.remove();\n",
" }\n",
" "
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.Javascript object>"
],
"application/javascript": [
"download(\"download_e9dfb994-0d94-46a0-a16d-296a01070e4a\", \"conll_data.csv\", 6111395)"
]
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"print(conll_df.head())"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "NRyX6MExgwi7",
"outputId": "78702706-75b5-4d4e-9cf7-08f21bb99dcb"
},
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" id tokens \\\n",
"0 0 [EU, rejects, German, call, to, boycott, Briti... \n",
"1 1 [Peter, Blackburn] \n",
"2 2 [BRUSSELS, 1996-08-22] \n",
"3 3 [The, European, Commission, said, on, Thursday... \n",
"4 4 [Germany, 's, representative, to, the, Europea... \n",
"\n",
" pos_tags \\\n",
"0 [22, 42, 16, 21, 35, 37, 16, 21, 7] \n",
"1 [22, 22] \n",
"2 [22, 11] \n",
"3 [12, 22, 22, 38, 15, 22, 28, 38, 15, 16, 21, 3... \n",
"4 [22, 27, 21, 35, 12, 22, 22, 27, 16, 21, 22, 2... \n",
"\n",
" chunk_tags \\\n",
"0 [11, 21, 11, 12, 21, 22, 11, 12, 0] \n",
"1 [11, 12] \n",
"2 [11, 12] \n",
"3 [11, 12, 12, 21, 13, 11, 11, 21, 13, 11, 12, 1... \n",
"4 [11, 11, 12, 13, 11, 12, 12, 11, 12, 12, 12, 1... \n",
"\n",
" ner_tags \n",
"0 [3, 0, 7, 0, 0, 0, 7, 0, 0] \n",
"1 [1, 2] \n",
"2 [5, 0] \n",
"3 [0, 3, 4, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, ... \n",
"4 [5, 0, 0, 0, 0, 3, 4, 0, 0, 0, 1, 2, 0, 0, 0, ... \n"
]
}
]
},
{
"cell_type": "code",
"source": [
"print(conll_df.describe())"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "LAiHg17QhO-2",
"outputId": "065d13c3-c8ea-40f3-f84a-2fc4f2332ff2"
},
"execution_count": 10,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" id tokens pos_tags chunk_tags ner_tags\n",
"count 20744 20744 20744 20744 20744\n",
"unique 14041 18731 13126 11282 8047\n",
"top 0 [Scorers, :] [22, 11] [11, 12] [5, 0]\n",
"freq 3 30 611 1290 955\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"print(conll_df.dtypes)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "9LwwJ8zbhVlk",
"outputId": "c32dde53-bf78-4f94-aa32-1cdef388099e"
},
"execution_count": 11,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"id object\n",
"tokens object\n",
"pos_tags object\n",
"chunk_tags object\n",
"ner_tags object\n",
"dtype: object\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"print(conll_df.isnull().sum())"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "njbG34F3hl5D",
"outputId": "81cb8929-f9a0-4a07-d1f6-f306d2ffc7c0"
},
"execution_count": 12,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"id 0\n",
"tokens 0\n",
"pos_tags 0\n",
"chunk_tags 0\n",
"ner_tags 0\n",
"dtype: int64\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"label_counts = conll_df['ner_tags'].value_counts()\n",
"print(label_counts)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "98pX56RShpgR",
"outputId": "18ef0b75-727b-4f2b-8ae9-6b4415e8e17a"
},
"execution_count": 13,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[5, 0] 955\n",
"[3, 0, 0, 0, 0, 0, 0, 0] 663\n",
"[0, 1, 2, 0, 5, 0, 0] 582\n",
"[0, 0] 409\n",
"[3, 0, 3, 0] 352\n",
" ... \n",
"[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0] 1\n",
"[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] 1\n",
"[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] 1\n",
"[0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 1, 2, 0] 1\n",
"[0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 0, 0, 0, 0, 0, 0, 1, 0] 1\n",
"Name: ner_tags, Length: 8047, dtype: int64\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"top_10_labels = label_counts.head(10)\n",
"\n",
"# Plot the distribution of the top 10 NER tags\n",
"plt.figure(figsize=(10, 6))\n",
"top_10_labels.plot(kind='bar')\n",
"plt.title('Top 10 Most Common NER Tags')\n",
"plt.xlabel('NER Tag')\n",
"plt.ylabel('Count')\n",
"plt.show()\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 710
},
"id": "Yd71HpRQhuoZ",
"outputId": "066c4b14-3edf-4139-e665-cdbf95dac172"
},
"execution_count": 14,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"def export_to_file(export_file_path, data):\n",
" with open(export_file_path, \"w\") as f:\n",
" for record in data:\n",
" ner_tags = record[\"ner_tags\"]\n",
" tokens = record[\"tokens\"]\n",
" if len(tokens) > 0:\n",
" f.write(\n",
" str(len(tokens))\n",
" + \"\\t\"\n",
" + \"\\t\".join(tokens)\n",
" + \"\\t\"\n",
" + \"\\t\".join(map(str, ner_tags))\n",
" + \"\\n\"\n",
" )\n",
"\n",
"\n",
"os.makedirs(\"data\", exist_ok=True)\n",
"export_to_file(\"./data/conll_train.txt\", conll_data[\"train\"])\n",
"export_to_file(\"./data/conll_val.txt\", conll_data[\"validation\"])"
],
"metadata": {
"id": "EQgmkV1fZRhI"
},
"execution_count": 9,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def make_tag_lookup_table():\n",
" iob_labels = [\"B\", \"I\"]\n",
" ner_labels = [\"PER\", \"ORG\", \"LOC\", \"MISC\"]\n",
" all_labels = [(label1, label2) for label2 in ner_labels for label1 in iob_labels]\n",
" all_labels = [\"-\".join([a, b]) for a, b in all_labels]\n",
" all_labels = [\"[PAD]\", \"O\"] + all_labels\n",
" return dict(zip(range(0, len(all_labels) + 1), all_labels))\n",
"\n",
"\n",
"mapping = make_tag_lookup_table()\n",
"print(mapping)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "OdufhIrEZRs2",
"outputId": "09e10fc1-6fdf-4281-ac81-973d32dad3a5"
},
"execution_count": 10,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"{0: '[PAD]', 1: 'O', 2: 'B-PER', 3: 'I-PER', 4: 'B-ORG', 5: 'I-ORG', 6: 'B-LOC', 7: 'I-LOC', 8: 'B-MISC', 9: 'I-MISC'}\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"all_tokens = sum(conll_data[\"train\"][\"tokens\"], [])\n",
"all_tokens_array = np.array(list(map(str.lower, all_tokens)))\n",
"\n",
"counter = Counter(all_tokens_array)\n",
"print(len(counter))\n",
"\n",
"num_tags = len(mapping)\n",
"vocab_size = 20000\n",
"\n",
"# We only take (vocab_size - 2) most commons words from the training data since\n",
"# the `StringLookup` class uses 2 additional tokens - one denoting an unknown\n",
"# token and another one denoting a masking token\n",
"vocabulary = [token for token, count in counter.most_common(vocab_size - 2)]\n",
"\n",
"# The StringLook class will convert tokens to token IDs\n",
"lookup_layer = keras.layers.StringLookup(vocabulary=vocabulary)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "a7T9RCZ3ZSKB",
"outputId": "c2dae2fc-b812-4d64-b3eb-23e2d38710c3"
},
"execution_count": 11,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"21009\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"train_data = tf.data.TextLineDataset(\"./data/conll_train.txt\")\n",
"val_data = tf.data.TextLineDataset(\"./data/conll_val.txt\")"
],
"metadata": {
"id": "vdcDo5IJZfjl"
},
"execution_count": 12,
"outputs": []
},
{
"cell_type": "code",
"source": [
"print(list(train_data.take(1).as_numpy_iterator()))\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "8fXqLG3FZfmx",
"outputId": "42354174-a397-4b9e-eda0-4b1d5ed62665"
},
"execution_count": 13,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[b'9\\tEU\\trejects\\tGerman\\tcall\\tto\\tboycott\\tBritish\\tlamb\\t.\\t3\\t0\\t7\\t0\\t0\\t0\\t7\\t0\\t0']\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"def map_record_to_training_data(record):\n",
" record = tf.strings.split(record, sep=\"\\t\")\n",
" length = tf.strings.to_number(record[0], out_type=tf.int32)\n",
" tokens = record[1 : length + 1]\n",
" tags = record[length + 1 :]\n",
" tags = tf.strings.to_number(tags, out_type=tf.int64)\n",
" tags += 1\n",
" return tokens, tags\n",
"\n",
"\n",
"def lowercase_and_convert_to_ids(tokens):\n",
" tokens = tf.strings.lower(tokens)\n",
" return lookup_layer(tokens)\n",
"\n",
"\n",
"# We use `padded_batch` here because each record in the dataset has a\n",
"# different length.\n",
"batch_size = 32\n",
"train_dataset = (\n",
" train_data.map(map_record_to_training_data)\n",
" .map(lambda x, y: (lowercase_and_convert_to_ids(x), y))\n",
" .padded_batch(batch_size)\n",
")\n",
"val_dataset = (\n",
" val_data.map(map_record_to_training_data)\n",
" .map(lambda x, y: (lowercase_and_convert_to_ids(x), y))\n",
" .padded_batch(batch_size)\n",
")\n",
"\n",
"ner_model = NERModel(num_tags, vocab_size, embed_dim=32, num_heads=4, ff_dim=64)"
],
"metadata": {
"id": "jtt-G6ezZto5"
},
"execution_count": 14,
"outputs": []
},
{
"cell_type": "code",
"source": [
"class CustomNonPaddingTokenLoss(keras.losses.Loss):\n",
" def __init__(self, name=\"custom_ner_loss\"):\n",
" super().__init__(name=name)\n",
"\n",
" def call(self, y_true, y_pred):\n",
" loss_fn = keras.losses.SparseCategoricalCrossentropy(\n",
" from_logits=False, reduction= 'none'\n",
" )\n",
" loss = loss_fn(y_true, y_pred)\n",
" mask = tf.cast((y_true > 0), dtype=tf.float32)\n",
" loss = loss * mask\n",
" return tf.reduce_sum(loss) / tf.reduce_sum(mask)\n",
"\n",
"\n",
"loss = CustomNonPaddingTokenLoss()"
],
"metadata": {
"id": "uqCmpwqgZtrs"
},
"execution_count": 15,
"outputs": []
},
{
"cell_type": "code",
"source": [
"ner_model.compile(optimizer=\"adam\", loss=loss)\n",
"ner_model.fit(train_dataset, epochs=10)\n",
"\n",
"\n",
"def tokenize_and_convert_to_ids(text):\n",
" tokens = text.split()\n",
" return lowercase_and_convert_to_ids(tokens)\n",
"\n",
"\n",
"# Sample inference using the trained model\n",
"sample_input = tokenize_and_convert_to_ids(\n",
" \"eu rejects german call to boycott british lamb\"\n",
")\n",
"sample_input = tf.reshape(sample_input, shape=[1, -1])\n",
"print(sample_input)\n",
"\n",
"output = ner_model.predict(sample_input)\n",
"prediction = np.argmax(output, axis=-1)[0]\n",
"prediction = [mapping[i] for i in prediction]\n",
"\n",
"# eu -> B-ORG, german -> B-MISC, british -> B-MISC\n",
"print(prediction)\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "TQDGyN4gZtuC",
"outputId": "5b743bb3-2112-47b2-e4f7-0db45991f93d"
},
"execution_count": 16,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/10\n",
"439/439 [==============================] - 20s 38ms/step - loss: 0.6150\n",
"Epoch 2/10\n",
"439/439 [==============================] - 17s 38ms/step - loss: 0.2667\n",
"Epoch 3/10\n",
"439/439 [==============================] - 14s 33ms/step - loss: 0.1617\n",
"Epoch 4/10\n",
"439/439 [==============================] - 15s 33ms/step - loss: 0.1254\n",
"Epoch 5/10\n",
"439/439 [==============================] - 14s 33ms/step - loss: 0.1015\n",
"Epoch 6/10\n",
"439/439 [==============================] - 14s 32ms/step - loss: 0.0837\n",
"Epoch 7/10\n",
"439/439 [==============================] - 15s 35ms/step - loss: 0.0697\n",
"Epoch 8/10\n",
"439/439 [==============================] - 14s 32ms/step - loss: 0.0604\n",
"Epoch 9/10\n",
"439/439 [==============================] - 15s 33ms/step - loss: 0.0526\n",
"Epoch 10/10\n",
"439/439 [==============================] - 16s 35ms/step - loss: 0.0456\n",
"tf.Tensor([[ 988 10950 204 628 6 3938 215 5773]], shape=(1, 8), dtype=int64)\n",
"1/1 [==============================] - 0s 261ms/step\n",
"['B-ORG', 'O', 'B-MISC', 'O', 'O', 'O', 'B-MISC', 'O']\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"def calculate_metrics(dataset):\n",
" all_true_tag_ids, all_predicted_tag_ids = [], []\n",
"\n",
" for x, y in dataset:\n",
" output = ner_model.predict(x, verbose=0)\n",
" predictions = np.argmax(output, axis=-1)\n",
" predictions = np.reshape(predictions, [-1])\n",
"\n",
" true_tag_ids = np.reshape(y, [-1])\n",
"\n",
" mask = (true_tag_ids > 0) & (predictions > 0)\n",
" true_tag_ids = true_tag_ids[mask]\n",
" predicted_tag_ids = predictions[mask]\n",
"\n",
" all_true_tag_ids.append(true_tag_ids)\n",
" all_predicted_tag_ids.append(predicted_tag_ids)\n",
"\n",
" all_true_tag_ids = np.concatenate(all_true_tag_ids)\n",
" all_predicted_tag_ids = np.concatenate(all_predicted_tag_ids)\n",
"\n",
" predicted_tags = [mapping[tag] for tag in all_predicted_tag_ids]\n",
" real_tags = [mapping[tag] for tag in all_true_tag_ids]\n",
"\n",
" evaluate(real_tags, predicted_tags)\n",
"\n",
"\n",
"calculate_metrics(val_dataset)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "vPPszQFIcEKi",
"outputId": "22d8a103-b1d1-402b-b401-f5662fdaca00"
},
"execution_count": 17,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"processed 51362 tokens with 5942 phrases; found: 5194 phrases; correct: 3847.\n",
"accuracy: 62.20%; (non-O)\n",
"accuracy: 93.33%; precision: 74.07%; recall: 64.74%; FB1: 69.09\n",
" LOC: precision: 85.18%; recall: 79.48%; FB1: 82.23 1714\n",
" MISC: precision: 75.61%; recall: 63.88%; FB1: 69.25 779\n",
" ORG: precision: 63.88%; recall: 60.92%; FB1: 62.37 1279\n",
" PER: precision: 68.99%; recall: 53.26%; FB1: 60.11 1422\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"def test_model_with_input(ner_model, mapping):\n",
" # Get input sentence from user\n",
" input_sentence = input(\"Enter a sentence: \")\n",
"\n",
" # Tokenize and convert input sentence to IDs\n",
" sample_input = tokenize_and_convert_to_ids(input_sentence)\n",
" sample_input = tf.reshape(sample_input, shape=[1, -1])\n",
"\n",
" # Predict tags using the trained model\n",
" output = ner_model.predict(sample_input)\n",
" predictions = np.argmax(output, axis=-1)[0]\n",
" predicted_tags = [mapping[i] for i in predictions]\n",
"\n",
" # Print the predicted tags for each token in the input sentence\n",
" print(\"Input sentence:\", input_sentence)\n",
" print(\"Predicted tags:\", predicted_tags)\n",
"\n",
"# Test the model with user input\n",
"test_model_with_input(ner_model, mapping)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "BX6jui33cEiJ",
"outputId": "91207f20-c00e-46ab-ae91-9bc1dfc8d804"
},
"execution_count": 18,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Enter a sentence: My Name is Karishma. I live in Canada. Canada I am from India\n",
"1/1 [==============================] - 0s 20ms/step\n",
"Input sentence: My Name is Karishma. I live in Canada. Canada I am from India\n",
"Predicted tags: ['O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-LOC', 'O', 'O', 'O', 'B-LOC']\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"logger = logging.getLogger(\"presidio-analyzer\")\n",
"\n",
"\n",
"class FlairRecognizer(EntityRecognizer):\n",
" \"\"\"\n",
" Wrapper for a flair model, if needed to be used within Presidio Analyzer.\n",
" :example:\n",
" >from presidio_analyzer import AnalyzerEngine, RecognizerRegistry\n",
" >flair_recognizer = FlairRecognizer()\n",
" >registry = RecognizerRegistry()\n",
" >registry.add_recognizer(flair_recognizer)\n",
" >analyzer = AnalyzerEngine(registry=registry)\n",
" >results = analyzer.analyze(\n",
" > \"My name is Christopher and I live in Irbid.\",\n",
" > language=\"en\",\n",
" > return_decision_process=True,\n",
" >)\n",
" >for result in results:\n",
" > print(result)\n",
" > print(result.analysis_explanation)\n",
" \"\"\"\n",
"\n",
" ENTITIES = [\n",
" \"LOCATION\",\n",
" \"PERSON\",\n",
" \"ORGANIZATION\",\n",
" # \"MISCELLANEOUS\" # - There are no direct correlation with Presidio entities.\n",
" ]\n",
"\n",
" DEFAULT_EXPLANATION = \"Identified as {} by Flair's Named Entity Recognition\"\n",
"\n",
" CHECK_LABEL_GROUPS = [\n",
" ({\"LOCATION\"}, {\"LOC\", \"LOCATION\"}),\n",
" ({\"PERSON\"}, {\"PER\", \"PERSON\"}),\n",
" ({\"ORGANIZATION\"}, {\"ORG\"}),\n",
" # ({\"MISCELLANEOUS\"}, {\"MISC\"}), # Probably not PII\n",
" ]\n",
"\n",
" MODEL_LANGUAGES = {\"en\": \"flair/ner-english-large\"}\n",
"\n",
" PRESIDIO_EQUIVALENCES = {\n",
" \"PER\": \"PERSON\",\n",
" \"LOC\": \"LOCATION\",\n",
" \"ORG\": \"ORGANIZATION\",\n",
" # 'MISC': 'MISCELLANEOUS' # - Probably not PII\n",
" }\n",
"\n",
" def __init__(\n",
" self,\n",
" supported_language: str = \"en\",\n",
" supported_entities: Optional[List[str]] = None,\n",
" check_label_groups: Optional[Tuple[Set, Set]] = None,\n",
" model: SequenceTagger = None,\n",
" model_path: Optional[str] = None,\n",
" ):\n",
" self.check_label_groups = (\n",
" check_label_groups if check_label_groups else self.CHECK_LABEL_GROUPS\n",
" )\n",
"\n",
" supported_entities = supported_entities if supported_entities else self.ENTITIES\n",
"\n",
" if model and model_path:\n",
" raise ValueError(\"Only one of model or model_path should be provided.\")\n",
" elif model and not model_path:\n",
" self.model = model\n",
" elif not model and model_path:\n",
" print(f\"Loading model from {model_path}\")\n",
" self.model = SequenceTagger.load(model_path)\n",
" else:\n",
" print(f\"Loading model for language {supported_language}\")\n",
" self.model = SequenceTagger.load(\n",
" self.MODEL_LANGUAGES.get(supported_language)\n",
" )\n",
"\n",
" super().__init__(\n",
" supported_entities=supported_entities,\n",
" supported_language=supported_language,\n",
" name=\"Flair Analytics\",\n",
" )\n",
"\n",
" def load(self) -> None:\n",
" \"\"\"Load the model, not used. Model is loaded during initialization.\"\"\"\n",
" pass\n",
"\n",
" def get_supported_entities(self) -> List[str]:\n",
" \"\"\"\n",
" Return supported entities by this model.\n",
" :return: List of the supported entities.\n",
" \"\"\"\n",
" return self.supported_entities\n",
"\n",
" # Class to use Flair with Presidio as an external recognizer.\n",
" def analyze(\n",
" self, text: str, entities: List[str], nlp_artifacts: NlpArtifacts = None\n",
" ) -> List[RecognizerResult]:\n",
" \"\"\"\n",
" Analyze text using Text Analytics.\n",
" :param text: The text for analysis.\n",
" :param entities: Not working properly for this recognizer.\n",
" :param nlp_artifacts: Not used by this recognizer.\n",
" :param language: Text language. Supported languages in MODEL_LANGUAGES\n",
" :return: The list of Presidio RecognizerResult constructed from the recognized\n",
" Flair detections.\n",
" \"\"\"\n",
"\n",
" results = []\n",
"\n",
" sentences = Sentence(text)\n",
" self.model.predict(sentences)\n",
"\n",
" # If there are no specific list of entities, we will look for all of it.\n",
" if not entities:\n",
" entities = self.supported_entities\n",
"\n",
" for entity in entities:\n",
" if entity not in self.supported_entities:\n",
" continue\n",
"\n",
" for ent in sentences.get_spans(\"ner\"):\n",
" if not self.__check_label(\n",
" entity, ent.labels[0].value, self.check_label_groups\n",
" ):\n",
" continue\n",
" textual_explanation = self.DEFAULT_EXPLANATION.format(\n",
" ent.labels[0].value\n",
" )\n",
" explanation = self.build_flair_explanation(\n",
" round(ent.score, 2), textual_explanation\n",
" )\n",
" flair_result = self._convert_to_recognizer_result(ent, explanation)\n",
"\n",
" results.append(flair_result)\n",
"\n",
" return results\n",
"\n",
" def _convert_to_recognizer_result(self, entity, explanation) -> RecognizerResult:\n",
" entity_type = self.PRESIDIO_EQUIVALENCES.get(entity.tag, entity.tag)\n",
" flair_score = round(entity.score, 2)\n",
"\n",
" flair_results = RecognizerResult(\n",
" entity_type=entity_type,\n",
" start=entity.start_position,\n",
" end=entity.end_position,\n",
" score=flair_score,\n",
" analysis_explanation=explanation,\n",
" )\n",
"\n",
" return flair_results\n",
"\n",
" def build_flair_explanation(\n",
" self, original_score: float, explanation: str\n",
" ) -> AnalysisExplanation:\n",
" \"\"\"\n",
" Create explanation for why this result was detected.\n",
" :param original_score: Score given by this recognizer\n",
" :param explanation: Explanation string\n",
" :return:\n",
" \"\"\"\n",
" explanation = AnalysisExplanation(\n",
" recognizer=self.__class__.__name__,\n",
" original_score=original_score,\n",
" textual_explanation=explanation,\n",
" )\n",
" return explanation\n",
"\n",
" @staticmethod\n",
" def __check_label(\n",
" entity: str, label: str, check_label_groups: Tuple[Set, Set]\n",
" ) -> bool:\n",
" return any(\n",
" [entity in egrp and label in lgrp for egrp, lgrp in check_label_groups]\n",
" )"
],
"metadata": {
"id": "OWwGi143lCVF"
},
"execution_count": 20,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from transformers import AutoModel, AutoTokenizer\n",
"\n",
"\n",
"if __name__ == \"__main__\":\n",
" from flair.data import Sentence\n",
" from flair.models import SequenceTagger\n",
"\n",
" # load tagger\n",
" tagger = SequenceTagger.load(\"flair/ner-english-large\")\n",
"\n",
" # make example sentence\n",
" sentence = Sentence(\"My name is Karishma Shirsath. I live in Toronto Canada.\")\n",
"\n",
" # predict NER tags\n",
" tagger.predict(sentence)\n",
"\n",
" # print sentence\n",
" print(sentence)\n",
"\n",
" # print predicted NER spans\n",
" print(\"The following NER tags are found:\")\n",
" # iterate over entities and print\n",
" for entity in sentence.get_spans(\"ner\"):\n",
" print(entity)\n",
"\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "LT92Kk44lgAV",
"outputId": "0fc28bdc-4a3a-4e68-8617-27cdcedbc3ce"
},
"execution_count": 21,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"2024-03-16 05:24:49,993 SequenceTagger predicts: Dictionary with 20 tags: <unk>, O, S-ORG, S-MISC, B-PER, E-PER, S-LOC, B-ORG, E-ORG, I-PER, S-PER, B-MISC, I-MISC, E-MISC, I-ORG, B-LOC, E-LOC, I-LOC, <START>, <STOP>\n",
"Sentence[12]: \"My name is Karishma Shirsath. I live in Toronto Canada.\" → [\"Karishma Shirsath\"/PER, \"Toronto\"/LOC, \"Canada\"/LOC]\n",
"The following NER tags are found:\n",
"Span[3:5]: \"Karishma Shirsath\" → PER (1.0)\n",
"Span[9:10]: \"Toronto\" → LOC (1.0)\n",
"Span[10:11]: \"Canada\" → LOC (1.0)\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"if __name__ == \"__main__\":\n",
" from flair.data import Sentence\n",
" from flair.models import SequenceTagger\n",
"\n",
" # load tagger\n",
" tagger = SequenceTagger.load(\"flair/ner-english-large\")\n",
"\n",
" # make example sentence\n",
" sentence = Sentence(\"My name is Karishma Shirsath. I live in Toronto Canada.\")\n",
"\n",
" # predict NER tags\n",
" tagger.predict(sentence)\n",
"\n",
" # print sentence\n",
" print(sentence)\n",
"\n",
" # Anonymize identified named entities\n",
" anonymized_sentence = str(sentence)\n",
" for entity in sentence.get_spans(\"ner\"):\n",
" entity_text = entity.text\n",
" anonymized_text = \"*\" * len(entity_text)\n",
" anonymized_sentence = anonymized_sentence.replace(entity_text, anonymized_text)\n",
"\n",
" # print anonymized sentence\n",
" print(\"Anonymized sentence:\")\n",
" print(anonymized_sentence)\n",
"\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "lgYJJVilwbVF",
"outputId": "20e52cfd-0e6e-4906-bcb0-3c403160293d"
},
"execution_count": 33,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"2024-03-16 05:39:00,757 SequenceTagger predicts: Dictionary with 20 tags: <unk>, O, S-ORG, S-MISC, B-PER, E-PER, S-LOC, B-ORG, E-ORG, I-PER, S-PER, B-MISC, I-MISC, E-MISC, I-ORG, B-LOC, E-LOC, I-LOC, <START>, <STOP>\n",
"Sentence[12]: \"My name is Karishma Shirsath. I live in Toronto Canada.\" → [\"Karishma Shirsath\"/PER, \"Toronto\"/LOC, \"Canada\"/LOC]\n",
"Anonymized sentence:\n",
"Sentence[12]: \"My name is *****************. I live in ******* ******.\" → [\"*****************\"/PER, \"*******\"/LOC, \"******\"/LOC]\n"
]
}
]
}
]
} |