├── README.md
├── LICENSE
├── .gitignore
├── training_a_small_language_model.py
└── Training_a_Small_Language_Model.ipynb
/README.md:
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1 | # Training-Small-Language-Model
2 | Training Small Language Model
3 |
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/LICENSE:
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1 | MIT License
2 |
3 | Copyright (c) 2023 AI Anytime
4 |
5 | Permission is hereby granted, free of charge, to any person obtaining a copy
6 | of this software and associated documentation files (the "Software"), to deal
7 | in the Software without restriction, including without limitation the rights
8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 |
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 |
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 |
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/.gitignore:
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160 | #.idea/
161 |
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/training_a_small_language_model.py:
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1 | # -*- coding: utf-8 -*-
2 | """Training a Small Language Model.ipynb
3 |
4 | Automatically generated by Colaboratory.
5 |
6 | Original file is located at
7 | https://colab.research.google.com/drive/176cjp0TmFiv8rT96OuI51psRKq0gERxh
8 | """
9 |
10 | !pip install torch torchtext transformers sentencepiece pandas tqdm datasets
11 |
12 | from datasets import load_dataset, DatasetDict, Dataset
13 | import pandas as pd
14 | import ast
15 | import datasets
16 | from tqdm import tqdm
17 | import time
18 |
19 | # Load data set from huggingface
20 | data_sample = load_dataset("QuyenAnhDE/Diseases_Symptoms")
21 |
22 | data_sample
23 |
24 | # Convert to a pandas dataframe
25 | updated_data = [{'Name': item['Name'], 'Symptoms': item['Symptoms']} for item in data_sample['train']]
26 | df = pd.DataFrame(updated_data)
27 |
28 | df.head(5)
29 |
30 | # Just extract the Symptoms
31 | df['Symptoms'] = df['Symptoms'].apply(lambda x: ', '.join(x.split(', ')))
32 | display(df.head())
33 |
34 | from transformers import GPT2Tokenizer, GPT2LMHeadModel
35 | import torch
36 | import torch.nn as nn
37 | import torch.optim as optim
38 | from torch.utils.data import Dataset, DataLoader, random_split
39 |
40 | # If you have an NVIDIA GPU attached, use 'cuda'
41 | if torch.cuda.is_available():
42 | device = torch.device('cuda')
43 | else:
44 | # If Apple Silicon, set to 'mps' - otherwise 'cpu' (not advised)
45 | try:
46 | device = torch.device('mps')
47 | except Exception:
48 | device = torch.device('cpu')
49 |
50 | device
51 |
52 | # The tokenizer turns texts to numbers (and vice-versa)
53 | tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2')
54 |
55 | # The transformer
56 | model = GPT2LMHeadModel.from_pretrained('distilgpt2').to(device)
57 |
58 | model
59 |
60 | # Model params
61 | BATCH_SIZE = 8
62 |
63 | df.describe()
64 |
65 | # Dataset Prep
66 | class LanguageDataset(Dataset):
67 | """
68 | An extension of the Dataset object to:
69 | - Make training loop cleaner
70 | - Make ingestion easier from pandas df's
71 | """
72 | def __init__(self, df, tokenizer):
73 | self.labels = df.columns
74 | self.data = df.to_dict(orient='records')
75 | self.tokenizer = tokenizer
76 | x = self.fittest_max_length(df) # Fix here
77 | self.max_length = x
78 |
79 | def __len__(self):
80 | return len(self.data)
81 |
82 | def __getitem__(self, idx):
83 | x = self.data[idx][self.labels[0]]
84 | y = self.data[idx][self.labels[1]]
85 | text = f"{x} | {y}"
86 | tokens = self.tokenizer.encode_plus(text, return_tensors='pt', max_length=128, padding='max_length', truncation=True)
87 | return tokens
88 |
89 | def fittest_max_length(self, df): # Fix here
90 | """
91 | Smallest power of two larger than the longest term in the data set.
92 | Important to set up max length to speed training time.
93 | """
94 | max_length = max(len(max(df[self.labels[0]], key=len)), len(max(df[self.labels[1]], key=len)))
95 | x = 2
96 | while x < max_length: x = x * 2
97 | return x
98 |
99 | # Cast the Huggingface data set as a LanguageDataset we defined above
100 | data_sample = LanguageDataset(df, tokenizer)
101 |
102 | data_sample
103 |
104 | # Create train, valid
105 | train_size = int(0.8 * len(data_sample))
106 | valid_size = len(data_sample) - train_size
107 | train_data, valid_data = random_split(data_sample, [train_size, valid_size])
108 |
109 | # Make the iterators
110 | train_loader = DataLoader(train_data, batch_size=BATCH_SIZE, shuffle=True)
111 | valid_loader = DataLoader(valid_data, batch_size=BATCH_SIZE)
112 |
113 | # Set the number of epochs
114 | num_epochs = 10
115 |
116 | # Training parameters
117 | batch_size = BATCH_SIZE
118 | model_name = 'distilgpt2'
119 | gpu = 0
120 |
121 | # Set the learning rate and loss function
122 | ## CrossEntropyLoss measures how close answers to the truth.
123 | ## More punishing for high confidence wrong answers
124 | criterion = nn.CrossEntropyLoss(ignore_index = tokenizer.pad_token_id)
125 | optimizer = optim.Adam(model.parameters(), lr=5e-4)
126 | tokenizer.pad_token = tokenizer.eos_token
127 |
128 | # Init a results dataframe
129 | results = pd.DataFrame(columns=['epoch', 'transformer', 'batch_size', 'gpu',
130 | 'training_loss', 'validation_loss', 'epoch_duration_sec'])
131 |
132 | # The training loop
133 | for epoch in range(num_epochs):
134 | start_time = time.time() # Start the timer for the epoch
135 |
136 | # Training
137 | ## This line tells the model we're in 'learning mode'
138 | model.train()
139 | epoch_training_loss = 0
140 | train_iterator = tqdm(train_loader, desc=f"Training Epoch {epoch+1}/{num_epochs} Batch Size: {batch_size}, Transformer: {model_name}")
141 | for batch in train_iterator:
142 | optimizer.zero_grad()
143 | inputs = batch['input_ids'].squeeze(1).to(device)
144 | targets = inputs.clone()
145 | outputs = model(input_ids=inputs, labels=targets)
146 | loss = outputs.loss
147 | loss.backward()
148 | optimizer.step()
149 | train_iterator.set_postfix({'Training Loss': loss.item()})
150 | epoch_training_loss += loss.item()
151 | avg_epoch_training_loss = epoch_training_loss / len(train_iterator)
152 |
153 | # Validation
154 | ## This line below tells the model to 'stop learning'
155 | model.eval()
156 | epoch_validation_loss = 0
157 | total_loss = 0
158 | valid_iterator = tqdm(valid_loader, desc=f"Validation Epoch {epoch+1}/{num_epochs}")
159 | with torch.no_grad():
160 | for batch in valid_iterator:
161 | inputs = batch['input_ids'].squeeze(1).to(device)
162 | targets = inputs.clone()
163 | outputs = model(input_ids=inputs, labels=targets)
164 | loss = outputs.loss
165 | total_loss += loss
166 | valid_iterator.set_postfix({'Validation Loss': loss.item()})
167 | epoch_validation_loss += loss.item()
168 |
169 | avg_epoch_validation_loss = epoch_validation_loss / len(valid_loader)
170 |
171 | end_time = time.time() # End the timer for the epoch
172 | epoch_duration_sec = end_time - start_time # Calculate the duration in seconds
173 |
174 | new_row = {'transformer': model_name,
175 | 'batch_size': batch_size,
176 | 'gpu': gpu,
177 | 'epoch': epoch+1,
178 | 'training_loss': avg_epoch_training_loss,
179 | 'validation_loss': avg_epoch_validation_loss,
180 | 'epoch_duration_sec': epoch_duration_sec} # Add epoch_duration to the dataframe
181 |
182 | results.loc[len(results)] = new_row
183 | print(f"Epoch: {epoch+1}, Validation Loss: {total_loss/len(valid_loader)}")
184 |
185 | input_str = "Kidney Failure"
186 | input_ids = tokenizer.encode(input_str, return_tensors='pt').to(device)
187 |
188 | output = model.generate(
189 | input_ids,
190 | max_length=20,
191 | num_return_sequences=1,
192 | do_sample=True,
193 | top_k=8,
194 | top_p=0.95,
195 | temperature=0.5,
196 | repetition_penalty=1.2
197 | )
198 |
199 | decoded_output = tokenizer.decode(output[0], skip_special_tokens=True)
200 | print(decoded_output)
201 |
202 | torch.save(model, 'SmallMedLM.pt')
203 |
204 | torch.save(model, 'drive/My Drive/SmallMedLM.pt')
205 |
206 |
207 |
208 |
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/Training_a_Small_Language_Model.ipynb:
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3116 | "text": [
3117 | "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.1.0+cu121)\n",
3118 | "Requirement already satisfied: torchtext in /usr/local/lib/python3.10/dist-packages (0.16.0)\n",
3119 | "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.35.2)\n",
3120 | "Requirement already satisfied: sentencepiece in /usr/local/lib/python3.10/dist-packages (0.1.99)\n",
3121 | "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (1.5.3)\n",
3122 | "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (4.66.1)\n",
3123 | "Collecting datasets\n",
3124 | " Downloading datasets-2.16.0-py3-none-any.whl (507 kB)\n",
3125 | "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m507.1/507.1 kB\u001b[0m \u001b[31m8.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
3126 | "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.13.1)\n",
3127 | "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch) (4.5.0)\n",
3128 | "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch) (1.12)\n",
3129 | "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.2.1)\n",
3130 | "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.2)\n",
3131 | "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch) (2023.6.0)\n",
3132 | "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch) (2.1.0)\n",
3133 | "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from torchtext) (2.31.0)\n",
3134 | "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from torchtext) (1.23.5)\n",
3135 | "Requirement already satisfied: torchdata==0.7.0 in /usr/local/lib/python3.10/dist-packages (from torchtext) (0.7.0)\n",
3136 | "Requirement already satisfied: urllib3>=1.25 in /usr/local/lib/python3.10/dist-packages (from torchdata==0.7.0->torchtext) (2.0.7)\n",
3137 | "Requirement already satisfied: huggingface-hub<1.0,>=0.16.4 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.19.4)\n",
3138 | "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (23.2)\n",
3139 | "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.1)\n",
3140 | "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2023.6.3)\n",
3141 | "Requirement already satisfied: tokenizers<0.19,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.15.0)\n",
3142 | "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.1)\n",
3143 | "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n",
3144 | "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2023.3.post1)\n",
3145 | "Requirement already satisfied: pyarrow>=8.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (10.0.1)\n",
3146 | "Collecting pyarrow-hotfix (from datasets)\n",
3147 | " Downloading pyarrow_hotfix-0.6-py3-none-any.whl (7.9 kB)\n",
3148 | "Collecting dill<0.3.8,>=0.3.0 (from datasets)\n",
3149 | " Downloading dill-0.3.7-py3-none-any.whl (115 kB)\n",
3150 | "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m15.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
3151 | "\u001b[?25hRequirement already satisfied: xxhash in /usr/local/lib/python3.10/dist-packages (from datasets) (3.4.1)\n",
3152 | "Collecting multiprocess (from datasets)\n",
3153 | " Downloading multiprocess-0.70.15-py310-none-any.whl (134 kB)\n",
3154 | "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m17.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
3155 | "\u001b[?25hRequirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets) (3.9.1)\n",
3156 | "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (23.1.0)\n",
3157 | "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (6.0.4)\n",
3158 | "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.9.4)\n",
3159 | "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.4.1)\n",
3160 | "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.1)\n",
3161 | "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (4.0.3)\n",
3162 | "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)\n",
3163 | "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->torchtext) (3.3.2)\n",
3164 | "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->torchtext) (3.6)\n",
3165 | "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->torchtext) (2023.11.17)\n",
3166 | "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (2.1.3)\n",
3167 | "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n",
3168 | "Installing collected packages: pyarrow-hotfix, dill, multiprocess, datasets\n",
3169 | "Successfully installed datasets-2.16.0 dill-0.3.7 multiprocess-0.70.15 pyarrow-hotfix-0.6\n"
3170 | ]
3171 | }
3172 | ],
3173 | "source": [
3174 | "!pip install torch torchtext transformers sentencepiece pandas tqdm datasets"
3175 | ]
3176 | },
3177 | {
3178 | "cell_type": "code",
3179 | "source": [
3180 | "from datasets import load_dataset, DatasetDict, Dataset\n",
3181 | "import pandas as pd\n",
3182 | "import ast\n",
3183 | "import datasets\n",
3184 | "from tqdm import tqdm\n",
3185 | "import time"
3186 | ],
3187 | "metadata": {
3188 | "id": "veBWZ4_6ZzNF"
3189 | },
3190 | "execution_count": null,
3191 | "outputs": []
3192 | },
3193 | {
3194 | "cell_type": "code",
3195 | "source": [
3196 | "# Load data set from huggingface\n",
3197 | "data_sample = load_dataset(\"QuyenAnhDE/Diseases_Symptoms\")"
3198 | ],
3199 | "metadata": {
3200 | "colab": {
3201 | "base_uri": "https://localhost:8080/",
3202 | "height": 168,
3203 | "referenced_widgets": [
3204 | "909582d0b6014056b113ef9a0a1db740",
3205 | "6b08d4679ac6461cbe254fe3478091b6",
3206 | "9a2502aacbd6489da0f8b6fbadcf079b",
3207 | "f6ff0e6cd04047babe24708d21937576",
3208 | "36349f3e29664f0bbdda5c8f7651304e",
3209 | "59945ccd1da44215b5eaa6226835968c",
3210 | "2aed084699344e14b687a38196f3a4dc",
3211 | "97040354aa6047f4b2adfd7d0a255d0e",
3212 | "4167d18aa99240f09d20b78bb447f302",
3213 | "7b8c3ec2b0d7466c89881890686152f3",
3214 | "a86181d8ced94aa49b4664f692937ca8",
3215 | "d11ae8b4126d42a8a72dd70b3d093185",
3216 | "6319e0859d1547f8a586c2409c263519",
3217 | "2ce9e1635d34488eb5deee6f1eab48fc",
3218 | "44af7e9762b647beae7100e10b0f188f",
3219 | "01062415cb564e54906fa8ad9ff4031b",
3220 | "3be9d4577dec410cad57443e239347ce",
3221 | "9f0b34c9152840ac8f1e853420964173",
3222 | "8894905d0d0f4e4a995e8be50b214f39",
3223 | "c3b8c945c14e4adeb5031c68fe377ad6",
3224 | "8782c620fea449fd92ea1f8f7ef3c6ed",
3225 | "c04efab8b28346ea92e64a1599f1ee17",
3226 | "bd60373a16d647d9834a62c2624c9fc2",
3227 | "8e3fb5977e1c4ccab1181cf41959c132",
3228 | "d35466c751474dd688fe7073aec0a71f",
3229 | "82e084627ec14d57bf502decdc393b38",
3230 | "df0d6cc9ad9b4597802c6e98614a7f95",
3231 | "ef7a60fd80fe4ba8a8224b79f9659644",
3232 | "7e912d3befc4448ea114e1fa3aa93119",
3233 | "ba7aaec1107b40248eb6b76fb09f1fc9",
3234 | "393fcf7561da4434b07ac6022ee95eeb",
3235 | "8cc2390d790f46ce880f81da99c3b243",
3236 | "cb65ea1de35d439dbb0664329db64b25"
3237 | ]
3238 | },
3239 | "id": "vup_3Q1jZ60E",
3240 | "outputId": "d6611e5c-1fdf-43ba-b8c9-e9af9211b932"
3241 | },
3242 | "execution_count": null,
3243 | "outputs": [
3244 | {
3245 | "output_type": "display_data",
3246 | "data": {
3247 | "text/plain": [
3248 | "Downloading readme: 0%| | 0.00/381 [00:00\n",
3376 | " \n",
3377 | "\n",
3390 | "
\n",
3391 | " \n",
3392 | " \n",
3393 | " | \n",
3394 | " Name | \n",
3395 | " Symptoms | \n",
3396 | "
\n",
3397 | " \n",
3398 | " \n",
3399 | " \n",
3400 | " | 0 | \n",
3401 | " Panic disorder | \n",
3402 | " Palpitations, Sweating, Trembling, Shortness o... | \n",
3403 | "
\n",
3404 | " \n",
3405 | " | 1 | \n",
3406 | " Vocal cord polyp | \n",
3407 | " Hoarseness, Vocal Changes, Vocal Fatigue | \n",
3408 | "
\n",
3409 | " \n",
3410 | " | 2 | \n",
3411 | " Turner syndrome | \n",
3412 | " Short stature, Gonadal dysgenesis, Webbed neck... | \n",
3413 | "
\n",
3414 | " \n",
3415 | " | 3 | \n",
3416 | " Cryptorchidism | \n",
3417 | " Absence or undescended testicle(s), empty scro... | \n",
3418 | "
\n",
3419 | " \n",
3420 | " | 4 | \n",
3421 | " Ethylene glycol poisoning-1 | \n",
3422 | " Nausea, vomiting, abdominal pain, General mala... | \n",
3423 | "
\n",
3424 | " \n",
3425 | "
\n",
3426 | "
\n",
3681 | "
\n",
3682 | "\n",
3695 | "
\n",
3696 | " \n",
3697 | " \n",
3698 | " | \n",
3699 | " Name | \n",
3700 | " Symptoms | \n",
3701 | "
\n",
3702 | " \n",
3703 | " \n",
3704 | " \n",
3705 | " | 0 | \n",
3706 | " Panic disorder | \n",
3707 | " Palpitations, Sweating, Trembling, Shortness o... | \n",
3708 | "
\n",
3709 | " \n",
3710 | " | 1 | \n",
3711 | " Vocal cord polyp | \n",
3712 | " Hoarseness, Vocal Changes, Vocal Fatigue | \n",
3713 | "
\n",
3714 | " \n",
3715 | " | 2 | \n",
3716 | " Turner syndrome | \n",
3717 | " Short stature, Gonadal dysgenesis, Webbed neck... | \n",
3718 | "
\n",
3719 | " \n",
3720 | " | 3 | \n",
3721 | " Cryptorchidism | \n",
3722 | " Absence or undescended testicle(s), empty scro... | \n",
3723 | "
\n",
3724 | " \n",
3725 | " | 4 | \n",
3726 | " Ethylene glycol poisoning-1 | \n",
3727 | " Nausea, vomiting, abdominal pain, General mala... | \n",
3728 | "
\n",
3729 | " \n",
3730 | "
\n",
3731 | "
\n",
3732 | "
\n",
3940 | "
\n",
4273 | "\n",
4286 | "
\n",
4287 | " \n",
4288 | " \n",
4289 | " | \n",
4290 | " Name | \n",
4291 | " Symptoms | \n",
4292 | "
\n",
4293 | " \n",
4294 | " \n",
4295 | " \n",
4296 | " | count | \n",
4297 | " 400 | \n",
4298 | " 400 | \n",
4299 | "
\n",
4300 | " \n",
4301 | " | unique | \n",
4302 | " 392 | \n",
4303 | " 395 | \n",
4304 | "
\n",
4305 | " \n",
4306 | " | top | \n",
4307 | " Sciatica | \n",
4308 | " Swelling, pain, dry mouth, bad taste | \n",
4309 | "
\n",
4310 | " \n",
4311 | " | freq | \n",
4312 | " 3 | \n",
4313 | " 3 | \n",
4314 | "
\n",
4315 | " \n",
4316 | "
\n",
4317 | "