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"cells": [
{
"cell_type": "code",
"metadata": {
"id": "4BcQp1Kuz97y",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "c8ab244e-fb5c-4bcb-a353-3e46e36013fd"
},
"source": [
"!pip install transformers"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting transformers\n",
" Downloading transformers-4.10.2-py3-none-any.whl (2.8 MB)\n",
"\u001b[K |████████████████████████████████| 2.8 MB 5.2 MB/s \n",
"\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.0.12)\n",
"Collecting tokenizers<0.11,>=0.10.1\n",
" Downloading tokenizers-0.10.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (3.3 MB)\n",
"\u001b[K |████████████████████████████████| 3.3 MB 32.8 MB/s \n",
"\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from transformers) (21.0)\n",
"Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.19.5)\n",
"Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
"Collecting pyyaml>=5.1\n",
" Downloading PyYAML-5.4.1-cp37-cp37m-manylinux1_x86_64.whl (636 kB)\n",
"\u001b[K |████████████████████████████████| 636 kB 42.2 MB/s \n",
"\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.62.0)\n",
"Collecting huggingface-hub>=0.0.12\n",
" Downloading huggingface_hub-0.0.16-py3-none-any.whl (50 kB)\n",
"\u001b[K |████████████████████████████████| 50 kB 3.4 MB/s \n",
"\u001b[?25hRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2019.12.20)\n",
"Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.6.4)\n",
"Collecting sacremoses\n",
" Downloading sacremoses-0.0.45-py3-none-any.whl (895 kB)\n",
"\u001b[K |████████████████████████████████| 895 kB 56.9 MB/s \n",
"\u001b[?25hRequirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from huggingface-hub>=0.0.12->transformers) (3.7.4.3)\n",
"Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->transformers) (2.4.7)\n",
"Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.5.0)\n",
"Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.24.3)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2021.5.30)\n",
"Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n",
"Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n",
"Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (7.1.2)\n",
"Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.0.1)\n",
"Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.15.0)\n",
"Installing collected packages: tokenizers, sacremoses, pyyaml, huggingface-hub, transformers\n",
" Attempting uninstall: pyyaml\n",
" Found existing installation: PyYAML 3.13\n",
" Uninstalling PyYAML-3.13:\n",
" Successfully uninstalled PyYAML-3.13\n",
"Successfully installed huggingface-hub-0.0.16 pyyaml-5.4.1 sacremoses-0.0.45 tokenizers-0.10.3 transformers-4.10.2\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "RuClZ2PJ0qX9",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "eee139b3-ab7a-4107-87a0-a8865310ea74"
},
"source": [
"!python -m spacy download en_core_web_lg"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting en_core_web_lg==2.2.5\n",
" Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_lg-2.2.5/en_core_web_lg-2.2.5.tar.gz (827.9 MB)\n",
"\u001b[K |████████████████████████████████| 827.9 MB 1.2 MB/s \n",
"\u001b[?25hRequirement already satisfied: spacy>=2.2.2 in /usr/local/lib/python3.7/dist-packages (from en_core_web_lg==2.2.5) (2.2.4)\n",
"Requirement already satisfied: catalogue<1.1.0,>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (1.0.0)\n",
"Requirement already satisfied: plac<1.2.0,>=0.9.6 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (1.1.3)\n",
"Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (4.62.0)\n",
"Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (1.0.5)\n",
"Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (57.4.0)\n",
"Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (1.19.5)\n",
"Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (3.0.5)\n",
"Requirement already satisfied: srsly<1.1.0,>=1.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (1.0.5)\n",
"Requirement already satisfied: thinc==7.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (7.4.0)\n",
"Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (2.0.5)\n",
"Requirement already satisfied: blis<0.5.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (0.4.1)\n",
"Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (0.8.2)\n",
"Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (2.23.0)\n",
"Requirement already satisfied: importlib-metadata>=0.20 in /usr/local/lib/python3.7/dist-packages (from catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->en_core_web_lg==2.2.5) (4.6.4)\n",
"Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.20->catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->en_core_web_lg==2.2.5) (3.5.0)\n",
"Requirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.20->catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->en_core_web_lg==2.2.5) (3.7.4.3)\n",
"Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_lg==2.2.5) (3.0.4)\n",
"Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_lg==2.2.5) (1.24.3)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_lg==2.2.5) (2021.5.30)\n",
"Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_lg==2.2.5) (2.10)\n",
"Building wheels for collected packages: en-core-web-lg\n",
" Building wheel for en-core-web-lg (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for en-core-web-lg: filename=en_core_web_lg-2.2.5-py3-none-any.whl size=829180942 sha256=09c6caef4e69024bd9bc086805d04481b7b1fa2b3bf262501c5948465893857e\n",
" Stored in directory: /tmp/pip-ephem-wheel-cache-9fmv3ul8/wheels/11/95/ba/2c36cc368c0bd339b44a791c2c1881a1fb714b78c29a4cb8f5\n",
"Successfully built en-core-web-lg\n",
"Installing collected packages: en-core-web-lg\n",
"Successfully installed en-core-web-lg-2.2.5\n",
"\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
"You can now load the model via spacy.load('en_core_web_lg')\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "0N6BY70tyg3U",
"outputId": "3d3cf3f3-efd7-4a1b-b2c7-7c0e3647e31f"
},
"source": [
"!ls drive/MyDrive/model/bert_med/checkpoint/checkpoint-final"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"ls: cannot access 'drive/MyDrive/model/bert_med/checkpoint/checkpoint-final': No such file or directory\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "nUIQ5hmvzRaa"
},
"source": [
"question = \"Can you give dosage information acetaminophen for children?\"\n",
"\n",
"reply = \"\"\"Use acetaminophen exactly as directed on the label, or as prescribed by your doctor. Do not use in larger or smaller amounts or for longer than recommended.\n",
"Do not take more of this medication than is recommended. An overdose of acetaminophen can damage your liver or cause death.\n",
"Adults and teenagers who weigh at least 110 pounds (50 kilograms): Do not take more than 1000 milligrams (mg) at one time. Do not take more than 4000 mg in 24 hours.\n",
"Children younger than 12 years old: Do not take more than 5 doses of acetaminophen in 24 hours. Use only the number of milligrams per dose that is recommended for the child's weight and age. Use exactly as directed on the label.\n",
"Avoid also using other medicines that contain acetaminophen, or you could have a fatal overdose.\n",
"If you are treating a child, use a pediatric form of acetaminophen. Use only the special dose-measuring dropper or oral syringe that comes with the specific pediatric form you are using. Carefully follow the dosing directions on the medicine label.\n",
"Measure liquid medicinewith the dosing syringe provided, or with a special dose-measuring spoon or medicine cup. If you do not have a dose-measuring device, ask your pharmacist for one.\n",
"Acetaminophen made for infants is available in two different dose concentrations, and each concentration comes with its own medicine dropper or oral syringe. These dosing devices are not equal between the different concentrations. Using the wrong device may cause you to give your child an overdose of acetaminophen. Never mix and match dosing devices between infant formulations of acetaminophen.\n",
"You may need to shake the liquid before each use. Follow the directions on the medicine label.\n",
"The chewable tablet must be chewed thoroughly before you swallow it.\n",
"Make sure your hands are dry when handling the acetaminophen disintegrating tablet. Place the tablet on your tongue. It will begin to dissolve right away. Do not swallow the tablet whole. Allow it to dissolve in your mouth without chewing.\n",
"To use the acetaminophen effervescent granules, dissolve one packet of the granules in at least 4 ounces of water. Stir this mixture and drink all of it right away. To make sure you get the entire dose, add a little more water to the same glass, swirl gently and drink right away.\n",
"The oral powder should be placed directly on the tongue and swallowed.\n",
"Stop taking acetaminophen and call your doctor if:\n",
"you still have a sore throat after 2 days of use;\n",
"you still have a fever after 3 days of use;\n",
"you still have pain after 7 days of use (or 5 days if treating a child);\n",
"you have a skin rash, ongoing headache, nausea, vomiting, or any redness or swelling; or\n",
"if your symptoms get worse, or if you have any new symptoms.\n",
"This medication can cause unusual results with certain lab tests for glucose (sugar) in the urine. Tell any doctor who treats you that you are using acetaminophen.\n",
"Store at room temperature away from heat and moisture.\n",
"Detailed Acetaminophen dosage information\n",
"What happens if I miss a dose?\n",
"Since acetaminophen is taken as needed, you may not be on a dosing schedule. If you are taking the medication regularly, take the missed dose as soon as you remember. Skip the missed dose if it is almost time for your next scheduled dose. Do not take extra medicine to make up the missed dose.\n",
"What happens if I overdose?\n",
"Seek emergency medical attention or call the Poison Help line at 1-800-222-1222. An overdose of acetaminophen can be fatal.\n",
"The first signs of an acetaminophen overdose include loss of appetite, nausea, vomiting, stomach pain, sweating, and confusion or weakness. Later symptoms may include pain in your upper stomach, dark urine, and yellowing of your skin or the whites of your eyes.\n",
"\"\"\""
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "_LUf0zi0ziZQ"
},
"source": [
"class SentenceSimilarity():\n",
" def __init__(self, model_name=None, device='cuda'):\n",
" self.tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
" self.model = AutoModel.from_pretrained(model_name).eval()\n",
"\n",
" def __call__(self, text):\n",
" tokens = self.tokenizer(text, padding=True, return_tensors='pt', truncation=True)\n",
" with torch.no_grad():\n",
" embeddings = self.model(**tokens).last_hidden_state\n",
"\n",
" mask = tokens['attention_mask'].unsqueeze(-1).expand(embeddings.shape).float()\n",
" sentence_embeddings = torch.sum(embeddings * mask, dim=1) / torch.clamp(mask.sum(1), min=1e-9) \n",
" return sentence_embeddings\n",
"\n",
" def compute_label_embedding(self, labels):\n",
" self.label_embeds = self(labels)\n",
" \n",
" def similarity(self, example):\n",
" return torch.cosine_similarity(self(example), self.label_embeds).tolist()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "_Wa2u2WAzzVV"
},
"source": [
"from transformers import AutoTokenizer, AutoModel\n",
"import en_core_web_lg\n",
"import torch\n",
"import pandas as pd\n",
"import numpy as np\n",
"from sklearn.decomposition import PCA\n",
"import matplotlib.pyplot as plt"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "O11MAh5Z0gGk"
},
"source": [
"nlp = en_core_web_lg.load()\n",
"doc = nlp(reply)\n",
"labels = [str(sentence) for sentence in list(doc.sents)]\n",
"examples = [question]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "xr9eQQkegqhT",
"outputId": "29892f73-258d-4d68-c19a-b46c59b52c9b"
},
"source": [
"from google.colab import drive\n",
"drive.mount('/content/drive')"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Mounted at /content/drive\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "UcLcTsqNzp68",
"outputId": "580de1d3-3241-4e46-fb0c-246a6ad6b21d"
},
"source": [
"model_base_name = 'drive/MyDrive/bert-base-finetuned'\n",
"model_base = SentenceSimilarity(model_base_name)\n",
"model_base.compute_label_embedding(labels)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Some weights of the model checkpoint at drive/MyDrive/bert-base-finetuned were not used when initializing BertModel: ['cls.predictions.transform.dense.bias', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.decoder.bias', 'cls.predictions.transform.LayerNorm.bias']\n",
"- This IS expected if you are initializing BertModel 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 BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
"Some weights of BertModel were not initialized from the model checkpoint at drive/MyDrive/bert-base-finetuned and are newly initialized: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight']\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "n5jlNAfG10W8",
"outputId": "cc26c811-d816-42b7-889c-cf802c2fcc76"
},
"source": [
"scores = model_base.similarity(examples[0])\n",
"dic = {}\n",
"\n",
"print(question)\n",
"\n",
"for i, ex in enumerate(labels):\n",
" dic[ex] = scores[i]\n",
"\n",
"s = pd.Series(dic, name='Similarity')\n",
"s.index.name = 'Sentences'\n",
"df_finetune_bert = s.reset_index()\n",
"df_finetune_bert.sort_values(by='Similarity', ascending=False, inplace=True)\n",
"df_finetune_bert.reset_index(inplace=True)\n",
"df_finetune_bert"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Can you give dosage information acetaminophen for children?\n"
]
},
{
"output_type": "execute_result",
"data": {
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" <td>18</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>35</td>\n",
" <td>This medication can cause unusual results with...</td>\n",
" <td>0.679667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>12</td>\n",
" <td>Carefully follow the dosing directions on the ...</td>\n",
" <td>0.678807</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>0</td>\n",
" <td>Use acetaminophen exactly as directed on the l...</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>33</td>\n",
" <td>you still have a sore throat after 2 days of u...</td>\n",
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" </tr>\n",
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" </tr>\n",
" <tr>\n",
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" </tr>\n",
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],
"text/plain": [
" index Sentences Similarity\n",
"0 38 Detailed Acetaminophen dosage information\\n 0.853427\n",
"1 19 Never mix and match dosing devices between inf... 0.833955\n",
"2 40 Since acetaminophen is taken as needed, you ma... 0.812427\n",
"3 10 If you are treating a child, use a pediatric f... 0.797016\n",
"4 16 Acetaminophen made for infants is available in... 0.787604\n",
"5 32 Stop taking acetaminophen and call your doctor... 0.777802\n",
"6 23 Make sure your hands are dry when handling the... 0.772180\n",
"7 36 Tell any doctor who treats you that you are us... 0.744137\n",
"8 6 Children younger than 12 years old: Do not tak... 0.731766\n",
"9 28 To use the acetaminophen effervescent granules... 0.729823\n",
"10 9 Avoid also using other medicines that contain ... 0.712440\n",
"11 11 Use only the special dose-measuring dropper or... 0.712209\n",
"12 7 Use only the number of milligrams per dose tha... 0.698351\n",
"13 3 An overdose of acetaminophen can damage your l... 0.695948\n",
"14 18 Using the wrong device may cause you to give y... 0.695873\n",
"15 35 This medication can cause unusual results with... 0.679667\n",
"16 12 Carefully follow the dosing directions on the ... 0.678807\n",
"17 0 Use acetaminophen exactly as directed on the l... 0.677227\n",
"18 33 you still have a sore throat after 2 days of u... 0.675472\n",
"19 46 An overdose of acetaminophen can be fatal.\\n 0.675087\n",
"20 22 The chewable tablet must be chewed thoroughly ... 0.673205\n",
"21 5 Do not take more than 4000 mg in 24 hours.\\n 0.672365\n",
"22 17 These dosing devices are not equal between the... 0.668638\n",
"23 14 liquid medicinewith the dosing syringe provide... 0.667967\n",
"24 4 Adults and teenagers who weigh at least 110 po... 0.650907\n",
"25 31 The oral powder should be placed directly on t... 0.645979\n",
"26 20 You may need to shake the liquid before each use. 0.643904\n",
"27 30 To make sure you get the entire dose, add a li... 0.641648\n",
"28 29 Stir this mixture and drink all of it right away. 0.629004\n",
"29 15 If you do not have a dose-measuring device, as... 0.625567\n",
"30 2 Do not take more of this medication than is re... 0.621627\n",
"31 26 Do not swallow the tablet whole. 0.621161\n",
"32 41 If you are taking the medication regularly, ta... 0.615762\n",
"33 1 Do not use in larger or smaller amounts or for... 0.615096\n",
"34 47 The first signs of an acetaminophen overdose i... 0.611996\n",
"35 21 Follow the directions on the medicine label.\\n 0.608199\n",
"36 43 Do not take extra medicine to make up the miss... 0.606794\n",
"37 27 Allow it to dissolve in your mouth without che... 0.603124\n",
"38 42 Skip the missed dose if it is almost time for ... 0.592967\n",
"39 24 Place the tablet on your tongue. 0.585152\n",
"40 39 What happens if I miss a dose?\\n 0.575800\n",
"41 25 It will begin to dissolve right away. 0.573656\n",
"42 34 you have a skin rash, ongoing headache, nausea... 0.569242\n",
"43 48 Later symptoms may include pain in your upper ... 0.551067\n",
"44 44 What happens if I overdose?\\n 0.549442\n",
"45 8 Use exactly as directed on the label.\\n 0.532089\n",
"46 37 Store at room temperature away from heat and m... 0.524711\n",
"47 45 Seek emergency medical attention or call the P... 0.472104\n",
"48 13 Measure 0.372533"
]
},
"metadata": {},
"execution_count": 10
}
]
},
{
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},
"id": "7IhGI2F3nsdt",
"outputId": "bdc4d7c8-39d6-4b2c-fd5c-2397f08653fd"
},
"source": [
"model_base_name = 'bert-base-uncased'\n",
"model_base = SentenceSimilarity(model_base_name)\n",
"model_base.compute_label_embedding(labels)"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
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"model_id": "ef36d5e9f5ff45a58f52aa7da91c1f9b",
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{
"output_type": "stream",
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"text": [
"Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.dense.bias', 'cls.predictions.bias', 'cls.seq_relationship.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.bias']\n",
"- This IS expected if you are initializing BertModel 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 BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "0SNu8mGFnxZo",
"outputId": "c6ed0f25-f4b8-48f3-adfd-fed31e375fec"
},
"source": [
"scores = model_base.similarity(examples[0])\n",
"dic = {}\n",
"\n",
"print(question)\n",
"\n",
"for i, ex in enumerate(labels):\n",
" dic[ex] = scores[i]\n",
"\n",
"s = pd.Series(dic, name='Similarity')\n",
"s.index.name = 'Sentences'\n",
"df_finetune_bert = s.reset_index()\n",
"df_finetune_bert.sort_values(by='Similarity', ascending=False, inplace=True)\n",
"df_finetune_bert.reset_index(inplace=True)\n",
"df_finetune_bert"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Can you give dosage information acetaminophen for children?\n"
]
},
{
"output_type": "execute_result",
"data": {
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"text/plain": [
" index Sentences Similarity\n",
"0 19 Never mix and match dosing devices between inf... 0.880267\n",
"1 40 Since acetaminophen is taken as needed, you ma... 0.873089\n",
"2 38 Detailed Acetaminophen dosage information\\n 0.855957\n",
"3 10 If you are treating a child, use a pediatric f... 0.830490\n",
"4 23 Make sure your hands are dry when handling the... 0.818485\n",
"5 16 Acetaminophen made for infants is available in... 0.813781\n",
"6 0 Use acetaminophen exactly as directed on the l... 0.812985\n",
"7 6 Children younger than 12 years old: Do not tak... 0.808529\n",
"8 32 Stop taking acetaminophen and call your doctor... 0.808096\n",
"9 9 Avoid also using other medicines that contain ... 0.806899\n",
"10 36 Tell any doctor who treats you that you are us... 0.797769\n",
"11 12 Carefully follow the dosing directions on the ... 0.772176\n",
"12 3 An overdose of acetaminophen can damage your l... 0.763895\n",
"13 18 Using the wrong device may cause you to give y... 0.763837\n",
"14 46 An overdose of acetaminophen can be fatal.\\n 0.762662\n",
"15 28 To use the acetaminophen effervescent granules... 0.759485\n",
"16 7 Use only the number of milligrams per dose tha... 0.759076\n",
"17 2 Do not take more of this medication than is re... 0.754334\n",
"18 11 Use only the special dose-measuring dropper or... 0.746475\n",
"19 35 This medication can cause unusual results with... 0.743332\n",
"20 33 you still have a sore throat after 2 days of u... 0.733555\n",
"21 41 If you are taking the medication regularly, ta... 0.726520\n",
"22 15 If you do not have a dose-measuring device, as... 0.725144\n",
"23 1 Do not use in larger or smaller amounts or for... 0.720082\n",
"24 42 Skip the missed dose if it is almost time for ... 0.717582\n",
"25 43 Do not take extra medicine to make up the miss... 0.706198\n",
"26 5 Do not take more than 4000 mg in 24 hours.\\n 0.702651\n",
"27 47 The first signs of an acetaminophen overdose i... 0.699613\n",
"28 14 liquid medicinewith the dosing syringe provide... 0.690246\n",
"29 21 Follow the directions on the medicine label.\\n 0.678231\n",
"30 30 To make sure you get the entire dose, add a li... 0.677095\n",
"31 22 The chewable tablet must be chewed thoroughly ... 0.667250\n",
"32 31 The oral powder should be placed directly on t... 0.653675\n",
"33 17 These dosing devices are not equal between the... 0.652507\n",
"34 4 Adults and teenagers who weigh at least 110 po... 0.637722\n",
"35 34 you have a skin rash, ongoing headache, nausea... 0.627184\n",
"36 20 You may need to shake the liquid before each use. 0.609635\n",
"37 29 Stir this mixture and drink all of it right away. 0.603456\n",
"38 27 Allow it to dissolve in your mouth without che... 0.600359\n",
"39 48 Later symptoms may include pain in your upper ... 0.592718\n",
"40 45 Seek emergency medical attention or call the P... 0.592356\n",
"41 24 Place the tablet on your tongue. 0.564837\n",
"42 26 Do not swallow the tablet whole. 0.556474\n",
"43 37 Store at room temperature away from heat and m... 0.556075\n",
"44 39 What happens if I miss a dose?\\n 0.553039\n",
"45 8 Use exactly as directed on the label.\\n 0.548044\n",
"46 44 What happens if I overdose?\\n 0.517920\n",
"47 25 It will begin to dissolve right away. 0.506700\n",
"48 13 Measure 0.250616"
]
},
"metadata": {},
"execution_count": 14
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "B2DkojVfzx_V",
"outputId": "f17a690c-3270-4e88-b9f8-d569059abee5"
},
"source": [
"model_large_name = 'drive/MyDrive/bert-large-finetuned'\n",
"model_large = SentenceSimilarity(model_large_name)\n",
"model_large.compute_label_embedding(labels)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Some weights of the model checkpoint at drive/MyDrive/bert-large-finetuned were not used when initializing BertModel: ['cls.predictions.transform.dense.bias', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.decoder.bias', 'cls.predictions.transform.LayerNorm.bias']\n",
"- This IS expected if you are initializing BertModel 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 BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
"Some weights of BertModel were not initialized from the model checkpoint at drive/MyDrive/bert-large-finetuned and are newly initialized: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight']\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "LR9Yf_J21tVV",
"outputId": "b6e1f9a1-617b-4eb6-f61a-bc4bcbd83902"
},
"source": [
"scores = model_large.similarity(examples[0])\n",
"dic = {}\n",
"\n",
"print(question)\n",
"\n",
"for i, ex in enumerate(labels):\n",
" dic[ex] = scores[i]\n",
"\n",
"s = pd.Series(dic, name='Similarity')\n",
"s.index.name = 'Sentences'\n",
"df_finetune_bert = s.reset_index()\n",
"df_finetune_bert.sort_values(by='Similarity', ascending=False, inplace=True)\n",
"df_finetune_bert.reset_index(inplace=True)\n",
"df_finetune_bert"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Can you give dosage information acetaminophen for children?\n"
]
},
{
"output_type": "execute_result",
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"</div>"
],
"text/plain": [
" index Sentences Similarity\n",
"0 10 If you are treating a child, use a pediatric f... 0.830899\n",
"1 38 Detailed Acetaminophen dosage information\\n 0.830435\n",
"2 18 Using the wrong device may cause you to give y... 0.795456\n",
"3 16 Acetaminophen made for infants is available in... 0.791079\n",
"4 28 To use the acetaminophen effervescent granules... 0.761752\n",
"5 32 Stop taking acetaminophen and call your doctor... 0.756600\n",
"6 0 Use acetaminophen exactly as directed on the l... 0.755415\n",
"7 23 Make sure your hands are dry when handling the... 0.751867\n",
"8 6 Children younger than 12 years old: Do not tak... 0.749787\n",
"9 11 Use only the special dose-measuring dropper or... 0.749476\n",
"10 12 Carefully follow the dosing directions on the ... 0.732956\n",
"11 9 Avoid also using other medicines that contain ... 0.732284\n",
"12 7 Use only the number of milligrams per dose tha... 0.729259\n",
"13 46 An overdose of acetaminophen can be fatal.\\n 0.727064\n",
"14 15 If you do not have a dose-measuring device, as... 0.699796\n",
"15 17 These dosing devices are not equal between the... 0.698050\n",
"16 14 liquid medicinewith the dosing syringe provide... 0.697277\n",
"17 4 Adults and teenagers who weigh at least 110 po... 0.693215\n",
"18 35 This medication can cause unusual results with... 0.685208\n",
"19 33 you still have a sore throat after 2 days of u... 0.685091\n",
"20 20 You may need to shake the liquid before each use. 0.683204\n",
"21 47 The first signs of an acetaminophen overdose i... 0.677560\n",
"22 36 Tell any doctor who treats you that you are us... 0.668998\n",
"23 39 What happens if I miss a dose?\\n 0.662117\n",
"24 19 Never mix and match dosing devices between inf... 0.661677\n",
"25 41 If you are taking the medication regularly, ta... 0.654314\n",
"26 8 Use exactly as directed on the label.\\n 0.650494\n",
"27 22 The chewable tablet must be chewed thoroughly ... 0.648614\n",
"28 42 Skip the missed dose if it is almost time for ... 0.646594\n",
"29 44 What happens if I overdose?\\n 0.642188\n",
"30 48 Later symptoms may include pain in your upper ... 0.639400\n",
"31 30 To make sure you get the entire dose, add a li... 0.631776\n",
"32 37 Store at room temperature away from heat and m... 0.623742\n",
"33 31 The oral powder should be placed directly on t... 0.621894\n",
"34 29 Stir this mixture and drink all of it right away. 0.617748\n",
"35 34 you have a skin rash, ongoing headache, nausea... 0.612240\n",
"36 2 Do not take more of this medication than is re... 0.608741\n",
"37 40 Since acetaminophen is taken as needed, you ma... 0.586397\n",
"38 43 Do not take extra medicine to make up the miss... 0.558599\n",
"39 5 Do not take more than 4000 mg in 24 hours.\\n 0.549838\n",
"40 24 Place the tablet on your tongue. 0.543047\n",
"41 3 An overdose of acetaminophen can damage your l... 0.502245\n",
"42 45 Seek emergency medical attention or call the P... 0.497212\n",
"43 25 It will begin to dissolve right away. 0.493721\n",
"44 1 Do not use in larger or smaller amounts or for... 0.491455\n",
"45 27 Allow it to dissolve in your mouth without che... 0.486398\n",
"46 21 Follow the directions on the medicine label.\\n 0.443755\n",
"47 26 Do not swallow the tablet whole. 0.441074\n",
"48 13 Measure 0.436105"
]
},
"metadata": {},
"execution_count": 12
}
]
},
{
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"id": "pL9uPsjnn779",
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},
"source": [
"model_large_name = 'bert-large-uncased'\n",
"model_large = SentenceSimilarity(model_large_name)\n",
"model_large.compute_label_embedding(labels)"
],
"execution_count": null,
"outputs": [
{
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{
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{
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"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Some weights of the model checkpoint at bert-large-uncased were not used when initializing BertModel: ['cls.predictions.transform.dense.bias', 'cls.predictions.bias', 'cls.seq_relationship.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.bias']\n",
"- This IS expected if you are initializing BertModel 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 BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "bsofPCZOn-mI",
"outputId": "4b821159-885b-4c2e-cc9a-1b3247289912"
},
"source": [
"scores = model_large.similarity(examples[0])\n",
"dic = {}\n",
"\n",
"print(question)\n",
"\n",
"for i, ex in enumerate(labels):\n",
" dic[ex] = scores[i]\n",
"\n",
"s = pd.Series(dic, name='Similarity')\n",
"s.index.name = 'Sentences'\n",
"df_finetune_bert = s.reset_index()\n",
"df_finetune_bert.sort_values(by='Similarity', ascending=False, inplace=True)\n",
"df_finetune_bert.reset_index(inplace=True)\n",
"df_finetune_bert"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Can you give dosage information acetaminophen for children?\n"
]
},
{
"output_type": "execute_result",
"data": {
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" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>15</td>\n",
" <td>If you do not have a dose-measuring device, as...</td>\n",
" <td>0.829827</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>18</td>\n",
" <td>Using the wrong device may cause you to give y...</td>\n",
" <td>0.829579</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>35</td>\n",
" <td>This medication can cause unusual results with...</td>\n",
" <td>0.823431</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>3</td>\n",
" <td>An overdose of acetaminophen can damage your l...</td>\n",
" <td>0.818224</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>46</td>\n",
" <td>An overdose of acetaminophen can be fatal.\\n</td>\n",
" <td>0.815892</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>11</td>\n",
" <td>Use only the special dose-measuring dropper or...</td>\n",
" <td>0.813490</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>23</td>\n",
" <td>Make sure your hands are dry when handling the...</td>\n",
" <td>0.803463</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>21</td>\n",
" <td>Follow the directions on the medicine label.\\n</td>\n",
" <td>0.794302</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>43</td>\n",
" <td>Do not take extra medicine to make up the miss...</td>\n",
" <td>0.790150</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>7</td>\n",
" <td>Use only the number of milligrams per dose tha...</td>\n",
" <td>0.789142</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>0</td>\n",
" <td>Use acetaminophen exactly as directed on the l...</td>\n",
" <td>0.787985</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>1</td>\n",
" <td>Do not use in larger or smaller amounts or for...</td>\n",
" <td>0.786247</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>22</td>\n",
" <td>The chewable tablet must be chewed thoroughly ...</td>\n",
" <td>0.783559</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>17</td>\n",
" <td>These dosing devices are not equal between the...</td>\n",
" <td>0.777908</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>33</td>\n",
" <td>you still have a sore throat after 2 days of u...</td>\n",
" <td>0.776968</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>12</td>\n",
" <td>Carefully follow the dosing directions on the ...</td>\n",
" <td>0.773562</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>41</td>\n",
" <td>If you are taking the medication regularly, ta...</td>\n",
" <td>0.772783</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>5</td>\n",
" <td>Do not take more than 4000 mg in 24 hours.\\n</td>\n",
" <td>0.771272</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>2</td>\n",
" <td>Do not take more of this medication than is re...</td>\n",
" <td>0.769992</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>14</td>\n",
" <td>liquid medicinewith the dosing syringe provide...</td>\n",
" <td>0.769487</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>42</td>\n",
" <td>Skip the missed dose if it is almost time for ...</td>\n",
" <td>0.768442</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>47</td>\n",
" <td>The first signs of an acetaminophen overdose i...</td>\n",
" <td>0.765844</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>30</td>\n",
" <td>To make sure you get the entire dose, add a li...</td>\n",
" <td>0.764426</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>4</td>\n",
" <td>Adults and teenagers who weigh at least 110 po...</td>\n",
" <td>0.760032</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>34</td>\n",
" <td>you have a skin rash, ongoing headache, nausea...</td>\n",
" <td>0.749295</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>20</td>\n",
" <td>You may need to shake the liquid before each use.</td>\n",
" <td>0.742221</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>28</td>\n",
" <td>To use the acetaminophen effervescent granules...</td>\n",
" <td>0.733613</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>37</td>\n",
" <td>Store at room temperature away from heat and m...</td>\n",
" <td>0.733249</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>48</td>\n",
" <td>Later symptoms may include pain in your upper ...</td>\n",
" <td>0.733133</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>31</td>\n",
" <td>The oral powder should be placed directly on t...</td>\n",
" <td>0.725665</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>27</td>\n",
" <td>Allow it to dissolve in your mouth without che...</td>\n",
" <td>0.720810</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>39</td>\n",
" <td>What happens if I miss a dose?\\n</td>\n",
" <td>0.717329</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>45</td>\n",
" <td>Seek emergency medical attention or call the P...</td>\n",
" <td>0.713387</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>44</td>\n",
" <td>What happens if I overdose?\\n</td>\n",
" <td>0.710678</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>29</td>\n",
" <td>Stir this mixture and drink all of it right away.</td>\n",
" <td>0.699790</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>26</td>\n",
" <td>Do not swallow the tablet whole.</td>\n",
" <td>0.694884</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>24</td>\n",
" <td>Place the tablet on your tongue.</td>\n",
" <td>0.654303</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>25</td>\n",
" <td>It will begin to dissolve right away.</td>\n",
" <td>0.594295</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>8</td>\n",
" <td>Use exactly as directed on the label.\\n</td>\n",
" <td>0.567564</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>13</td>\n",
" <td>Measure</td>\n",
" <td>0.427649</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" index Sentences Similarity\n",
"0 38 Detailed Acetaminophen dosage information\\n 0.898420\n",
"1 19 Never mix and match dosing devices between inf... 0.893630\n",
"2 40 Since acetaminophen is taken as needed, you ma... 0.877569\n",
"3 16 Acetaminophen made for infants is available in... 0.872395\n",
"4 10 If you are treating a child, use a pediatric f... 0.853832\n",
"5 32 Stop taking acetaminophen and call your doctor... 0.852571\n",
"6 9 Avoid also using other medicines that contain ... 0.851332\n",
"7 36 Tell any doctor who treats you that you are us... 0.844329\n",
"8 6 Children younger than 12 years old: Do not tak... 0.841645\n",
"9 15 If you do not have a dose-measuring device, as... 0.829827\n",
"10 18 Using the wrong device may cause you to give y... 0.829579\n",
"11 35 This medication can cause unusual results with... 0.823431\n",
"12 3 An overdose of acetaminophen can damage your l... 0.818224\n",
"13 46 An overdose of acetaminophen can be fatal.\\n 0.815892\n",
"14 11 Use only the special dose-measuring dropper or... 0.813490\n",
"15 23 Make sure your hands are dry when handling the... 0.803463\n",
"16 21 Follow the directions on the medicine label.\\n 0.794302\n",
"17 43 Do not take extra medicine to make up the miss... 0.790150\n",
"18 7 Use only the number of milligrams per dose tha... 0.789142\n",
"19 0 Use acetaminophen exactly as directed on the l... 0.787985\n",
"20 1 Do not use in larger or smaller amounts or for... 0.786247\n",
"21 22 The chewable tablet must be chewed thoroughly ... 0.783559\n",
"22 17 These dosing devices are not equal between the... 0.777908\n",
"23 33 you still have a sore throat after 2 days of u... 0.776968\n",
"24 12 Carefully follow the dosing directions on the ... 0.773562\n",
"25 41 If you are taking the medication regularly, ta... 0.772783\n",
"26 5 Do not take more than 4000 mg in 24 hours.\\n 0.771272\n",
"27 2 Do not take more of this medication than is re... 0.769992\n",
"28 14 liquid medicinewith the dosing syringe provide... 0.769487\n",
"29 42 Skip the missed dose if it is almost time for ... 0.768442\n",
"30 47 The first signs of an acetaminophen overdose i... 0.765844\n",
"31 30 To make sure you get the entire dose, add a li... 0.764426\n",
"32 4 Adults and teenagers who weigh at least 110 po... 0.760032\n",
"33 34 you have a skin rash, ongoing headache, nausea... 0.749295\n",
"34 20 You may need to shake the liquid before each use. 0.742221\n",
"35 28 To use the acetaminophen effervescent granules... 0.733613\n",
"36 37 Store at room temperature away from heat and m... 0.733249\n",
"37 48 Later symptoms may include pain in your upper ... 0.733133\n",
"38 31 The oral powder should be placed directly on t... 0.725665\n",
"39 27 Allow it to dissolve in your mouth without che... 0.720810\n",
"40 39 What happens if I miss a dose?\\n 0.717329\n",
"41 45 Seek emergency medical attention or call the P... 0.713387\n",
"42 44 What happens if I overdose?\\n 0.710678\n",
"43 29 Stir this mixture and drink all of it right away. 0.699790\n",
"44 26 Do not swallow the tablet whole. 0.694884\n",
"45 24 Place the tablet on your tongue. 0.654303\n",
"46 25 It will begin to dissolve right away. 0.594295\n",
"47 8 Use exactly as directed on the label.\\n 0.567564\n",
"48 13 Measure 0.427649"
]
},
"metadata": {},
"execution_count": 16
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "S3ld_5vfcvuz"
},
"source": [
"labels_vector = np.array([nlp(label).vector for label in labels])"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Sao-KQ0YdBiT",
"outputId": "9df32a95-f6cc-4e2f-d850-e44f98ebee66"
},
"source": [
"labels_vector.shape"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(49, 300)"
]
},
"metadata": {},
"execution_count": 38
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "o_fMUo_hdfG6",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "ddfd7bd0-b270-4702-b347-7cb62fd4ef37"
},
"source": [
"pca = PCA(n_components=2)\n",
"pca.fit(labels_vector)\n",
"labels_reducted = pca.transform(labels_vector)\n",
"print(pca.explained_variance_ratio_)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[0.20346883 0.13145208]\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 283
},
"id": "4bW8z8weevdq",
"outputId": "e354e163-480e-42d9-eda7-e284ce975a72"
},
"source": [
"plt.scatter(labels_reducted.T[0], labels_reducted.T[1])"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7fe2b74f1c10>"
]
},
"metadata": {},
"execution_count": 40
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "iJbXfzDWf2-0",
"outputId": "36d0a5a0-41b0-423d-b9b7-3ae4f88644ce"
},
"source": [
"model_base_name = 'drive/MyDrive/bert-base-finetuned'\n",
"model_base = SentenceSimilarity(model_base_name)\n",
"model_base.compute_label_embedding(labels)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Some weights of the model checkpoint at drive/MyDrive/bert-base-finetuned were not used when initializing BertModel: ['cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.decoder.bias']\n",
"- This IS expected if you are initializing BertModel 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 BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
"Some weights of BertModel were not initialized from the model checkpoint at drive/MyDrive/bert-base-finetuned and are newly initialized: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight']\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "JKFvSGw7hDin",
"outputId": "088ff946-1c4e-466a-d275-053eb95291af"
},
"source": [
"labels_vector = model_base.label_embeds\n",
"pca.fit(labels_vector)\n",
"labels_reducted = pca.transform(labels_vector)\n",
"print(pca.explained_variance_ratio_)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[0.13525368 0.11957026]\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 283
},
"id": "kJ7OjKVohLFk",
"outputId": "c6a0346c-b292-4f02-d4a2-7e94bc78697e"
},
"source": [
"plt.scatter(labels_reducted.T[0], labels_reducted.T[1])"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7fe2b71eea10>"
]
},
"metadata": {},
"execution_count": 50
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 251,
"referenced_widgets": [
"fdeccb82bc274bf28bc9838965f9949e",
"9f299bb4c6fe4265bab8161742606d4f",
"acc7611677184e3bb007ae16221567cb",
"d83ca64f9213414c95aa6e4f41cee1b6",
"521f59b60e164ee1ab0ddd8d2b41cc65",
"3c953a452341469cb943857472eb8354",
"0955ce4e6f584fa2ae796ee921f5f4cc",
"803e36a3647b461398835d57cb4b4cd3",
"fa5be4c9b7b7431c8f063bd5b82b5917",
"d333fbda2d1e42a0bc2c0e07be7d9cfa",
"92767c4ddb214d569cc4f98d97cfe51d",
"431016a9288c445282e680eccf22adb4",
"4a20298c92b54523af03c374cb3e8587",
"a99afa3f345e49b698d10cf12e54401c",
"04c161e2965846dfb2b4d02ec4962bb5",
"750169dbc2ff4d3584957e5d33b83823",
"19779724696447fda7848a408cc6fcc3",
"40268e850d9940e88ce8abe0f081ef31",
"77288c5fc65e4c1b930a12df74f76732",
"883c765c863f4bd5b25da77648d36d5e",
"a436314e2f264f1bae6dbf3ec9fdac00",
"027e3460916644549a22029a6ee4c180",
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"bf4dbcffaa1e4bc1bc5026fe5d382beb",
"cec8bf2bf0c24fddad0297d8c0bcfb8c",
"3e14c4497afb4f09be8a27c5c99a0aa7",
"3de3ebae4c60420cba957f1a5fafe18d",
"90e4535e37284869b84845883167183d",
"e4b9f03aa76b4bf7abbb0752e657bf28",
"9f21ed780198405ab71586924ec56967",
"4546ec4944a94af6b74649c36520d973",
"17add52f09094849805056ca1557d68b",
"d53781f81c374ee6a571e44623915380",
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"5b189a46179e4be291a5227fe67bdd1e",
"7ba316f8fe95421e8aa3d91059ca349d",
"56b0c978481d455b8938197c0f240f4d",
"caae34417b6645fb93c56139e859d444",
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"1fecd622983940019a989387f38505d6",
"761020d07c6645ec83e2b993c3467b04",
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"e72c255c802e4c739c350947b141156c",
"0fac02d7288944bc9950d5390cccd4b0",
"630841f6497c494a8257466fb36107d0",
"aae563273bdb4484a842da30a2ede3d4",
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"2c0b8fc3ac67452fa7bc1ba6a3f0698f"
]
},
"id": "btkVVuvzhj8i",
"outputId": "330395c2-b827-46cf-ee1b-82c7bbec822b"
},
"source": [
"model_base_name = 'bert-base-uncased'\n",
"model_base = SentenceSimilarity(model_base_name)\n",
"model_base.compute_label_embedding(labels)"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fdeccb82bc274bf28bc9838965f9949e",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"Downloading: 0%| | 0.00/28.0 [00:00<?, ?B/s]"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "431016a9288c445282e680eccf22adb4",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"Downloading: 0%| | 0.00/570 [00:00<?, ?B/s]"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5f3e0f15d5e64176878eecfd4eb9e944",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"Downloading: 0%| | 0.00/232k [00:00<?, ?B/s]"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "17add52f09094849805056ca1557d68b",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"Downloading: 0%| | 0.00/466k [00:00<?, ?B/s]"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "761020d07c6645ec83e2b993c3467b04",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"Downloading: 0%| | 0.00/440M [00:00<?, ?B/s]"
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.decoder.weight', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight']\n",
"- This IS expected if you are initializing BertModel 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 BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "AC5B4_Ibh26p",
"outputId": "506b789a-87ad-4a96-b168-2c53a9686562"
},
"source": [
"labels_vector = model_base.label_embeds\n",
"pca.fit(labels_vector)\n",
"labels_reducted = pca.transform(labels_vector)\n",
"print(pca.explained_variance_ratio_)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[0.19288862 0.09613357]\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 283
},
"id": "xDrb-RhNiAa4",
"outputId": "fc741ff1-2ac2-4cd0-a3e5-f56d6bf273ce"
},
"source": [
"plt.scatter(labels_reducted.T[0], labels_reducted.T[1])"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7fe2c1e206d0>"
]
},
"metadata": {},
"execution_count": 53
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "kKCXrhk5iCF2",
"outputId": "036f1798-0cff-4737-dafa-64cc4f8aed58"
},
"source": [
"model_base_name = 'drive/MyDrive/bert-large-finetuned'\n",
"model_base = SentenceSimilarity(model_base_name)\n",
"model_base.compute_label_embedding(labels)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Some weights of the model checkpoint at drive/MyDrive/bert-large-finetuned were not used when initializing BertModel: ['cls.predictions.decoder.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.decoder.bias']\n",
"- This IS expected if you are initializing BertModel 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 BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
"Some weights of BertModel were not initialized from the model checkpoint at drive/MyDrive/bert-large-finetuned and are newly initialized: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight']\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "iFdaTVaIiGsP",
"outputId": "edd04a88-de80-4d29-ed5c-ba53bac5fec4"
},
"source": [
"labels_vector = model_base.label_embeds\n",
"pca.fit(labels_vector)\n",
"labels_reducted = pca.transform(labels_vector)\n",
"print(pca.explained_variance_ratio_)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[0.18953428 0.08279858]\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 283
},
"id": "DZfmHdmsiJvf",
"outputId": "c598ac24-b138-47ee-e09a-e3f02cfdca48"
},
"source": [
"plt.scatter(labels_reducted.T[0], labels_reducted.T[1])"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7fe2c1dfbc50>"
]
},
"metadata": {},
"execution_count": 56
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 251,
"referenced_widgets": [
"c9b744324b2c4facbd132380d7d28e1e",
"c013789b1563479dbbdb4d6df2955a78",
"8e82b8e9a2d148cab163f95ed892f90f",
"24f8570a329640e390e4fd899285a42f",
"883b4cc2caf84fdebd907dd306ea5056",
"ddaa45b0fc98477d96a44a22b588938e",
"30e6861b802247c484175dd40da00156",
"f91a02f05901416da8f319148d980f82",
"eb31f87b9f9945fd850912c24705ae28",
"e9e76318e63d47edad749bf79b463911",
"7bee9e39d3ac4e7694a41f9ca9b795f9",
"78ac045043da428a8ea22eb7d63f3b02",
"7b45dddd02cb49259e7d1ec8d56b4b48",
"4690731e66514885a311e05cea5e8e72",
"d70c7fba2d86437183a50eb385d5c3e7",
"116e50700a884a748d5837f02757d444",
"0c0b2f29480d47d8ac0a9117d8f324a0",
"cef44e615f86480499801161fb971ced",
"b8a385ac947242c39b7cde58fe8ac4c0",
"c4737344565a4228815e7c38dbc37bfd",
"3a08b94e43f5436097481262157e5cd8",
"df0df93bf0a644bebf545f537ae86c85",
"307a49f63aab4ba9bb8f76b254453513",
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"32dc14c1272f4703af3d3f55e65799f0",
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"cb642aca320c4e7486afb3b13fcaf6ae",
"ffad3f16cd094bd0b85e05bee9301efa",
"c0311d9639644c65ac9a9a9fc284d96d",
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"842b77cea307410c83d3dca6053d0f30"
]
},
"id": "bhZnMm5HiLWX",
"outputId": "210bf5c2-6e39-4e5b-8183-cdbb37d023e6"
},
"source": [
"model_base_name = 'bert-large-uncased'\n",
"model_base = SentenceSimilarity(model_base_name)\n",
"model_base.compute_label_embedding(labels)"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c9b744324b2c4facbd132380d7d28e1e",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"Downloading: 0%| | 0.00/28.0 [00:00<?, ?B/s]"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "78ac045043da428a8ea22eb7d63f3b02",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"Downloading: 0%| | 0.00/571 [00:00<?, ?B/s]"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "307a49f63aab4ba9bb8f76b254453513",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"Downloading: 0%| | 0.00/232k [00:00<?, ?B/s]"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "32dc14c1272f4703af3d3f55e65799f0",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"Downloading: 0%| | 0.00/466k [00:00<?, ?B/s]"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fd89605ac41e4f208269e51ec4be2bce",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"Downloading: 0%| | 0.00/1.34G [00:00<?, ?B/s]"
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Some weights of the model checkpoint at bert-large-uncased were not used when initializing BertModel: ['cls.predictions.decoder.weight', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias']\n",
"- This IS expected if you are initializing BertModel 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 BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "BIl0m3mwiZM3",
"outputId": "6c746c08-3475-476c-ba85-1b6f29109044"
},
"source": [
"labels_vector = model_base.label_embeds\n",
"pca.fit(labels_vector)\n",
"labels_reducted = pca.transform(labels_vector)\n",
"print(pca.explained_variance_ratio_)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[0.15568082 0.1241058 ]\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 283
},
"id": "u5tE9T9Uia0C",
"outputId": "23e44c98-5d67-4753-a309-8165b1129430"
},
"source": [
"plt.scatter(labels_reducted.T[0], labels_reducted.T[1])"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7fe2c5c54990>"
]
},
"metadata": {},
"execution_count": 59
},
{
"output_type": "display_data",
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "pqT32ERYicHu"
},
"source": [
""
],
"execution_count": null,
"outputs": []
}
]
}
Datasets
Models
