393 lines
11 KiB
Python
393 lines
11 KiB
Python
# %%
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# from datasets import load_from_disk
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import os
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os.environ['NCCL_P2P_DISABLE'] = '1'
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os.environ['NCCL_IB_DISABLE'] = '1'
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os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
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os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3"
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import re
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import random
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import torch
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from transformers import (
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AutoTokenizer,
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AutoModelForSequenceClassification,
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DataCollatorWithPadding,
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Trainer,
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EarlyStoppingCallback,
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TrainingArguments
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)
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import evaluate
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import numpy as np
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import pandas as pd
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# import matplotlib.pyplot as plt
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from datasets import Dataset, DatasetDict
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torch.set_float32_matmul_precision('high')
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# %%
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def set_seed(seed):
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"""
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Set the random seed for reproducibility.
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"""
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random.seed(seed) # Python random module
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np.random.seed(seed) # NumPy random
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torch.manual_seed(seed) # PyTorch CPU
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torch.cuda.manual_seed(seed) # PyTorch GPU
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torch.cuda.manual_seed_all(seed) # If using multiple GPUs
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torch.backends.cudnn.deterministic = True # Ensure deterministic behavior
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torch.backends.cudnn.benchmark = False # Disable optimization for reproducibility
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set_seed(42)
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SHUFFLES=2
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# %%
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# import training file
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data_path = '../../esAppMod_data_import/train.csv'
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train_df = pd.read_csv(data_path, skipinitialspace=True)
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# rather than use pattern, we use the real thing and property
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entity_ids = train_df['entity_id'].to_list()
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target_id_list = sorted(list(set(entity_ids)))
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def compute_normalized_class_weights(class_counts, max_resamples=SHUFFLES):
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"""
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Compute normalized class weights inversely proportional to class counts.
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The weights are normalized so that they sum to 1.
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Args:
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class_counts (array-like): An array or list where each element represents the count of samples for a class.
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Returns:
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numpy.ndarray: A normalized array of weights for each class.
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"""
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class_counts = np.array(class_counts)
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total_samples = np.sum(class_counts)
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class_weights = total_samples / class_counts
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# so that highest weight is 1
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normalized_weights = class_weights / np.max(class_weights)
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# Scale weights such that the highest weight corresponds to `max_resamples`
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resample_counts = normalized_weights * max_resamples
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# Round resamples to nearest integer
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resample_counts = np.round(resample_counts).astype(int)
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return resample_counts
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# %%
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id_counts = train_df['entity_id'].value_counts()
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id_weights = compute_normalized_class_weights(id_counts, max_resamples=SHUFFLES)
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id_index = id_counts.index
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label2weight = {}
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for idx, label in enumerate(id_index):
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label2weight[label] = id_weights[idx]
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# %%
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id2label = {}
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label2id = {}
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for idx, val in enumerate(target_id_list):
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id2label[idx] = val
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label2id[val] = idx
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# %%
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# introduce pre-processing functions
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def preprocess_text(text):
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# 1. Make all uppercase
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text = text.lower()
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# Remove any non alphanumeric character
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# text = re.sub(r'[^\w\s]', ' ', text) # Retains only alphanumeric and spaces
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# replace dashes
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text = re.sub(r"[-;:]", " ", text)
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# Add space between digit followed by a letter
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text = re.sub(r"(\d)([A-Z])", r"\1 \2", text)
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# Add space between letter followed by a digit
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text = re.sub(r"([A-Z])(\d)", r"\1 \2", text)
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# Substitute digits with 'x'
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text = re.sub(r'\d+', 'x', text)
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# standardize spacing
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text = re.sub(r'\s+', ' ', text).strip()
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return text
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def generate_random_shuffles(text, n):
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"""
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Generate n strings with randomly shuffled words from the input text.
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Args:
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text (str): The input text.
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n (int): The number of random variations to generate.
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Returns:
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list: A list of strings with shuffled words.
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"""
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words = text.split() # Split the input into words
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shuffled_variations = []
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for _ in range(n):
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shuffled = words[:] # Copy the word list to avoid in-place modification
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random.shuffle(shuffled) # Randomly shuffle the words
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shuffled_variations.append(" ".join(shuffled)) # Join the words back into a string
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return shuffled_variations
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# generate n more shuffled examples
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def shuffle_text(text, n_shuffles=SHUFFLES):
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"""
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Preprocess a list of texts and add n random shuffles for each string.
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Args:
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texts (list): An input strings.
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n_shuffles (int): Number of random shuffles to generate for each string.
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Returns:
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list: A list of preprocessed and shuffled strings.
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"""
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all_processed = []
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all_processed.append(text)
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# Generate random shuffles
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shuffled_variations = generate_random_shuffles(text, n_shuffles)
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all_processed.extend(shuffled_variations)
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return all_processed
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term_to_abbrev = {
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r'job entry system': 'jes',
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r'subversion': 'svn',
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r'borland database engine': 'bde',
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r'business intelligence and reporting tools': 'birt',
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r'lan management solution': 'lms',
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r'laboratory information management system': 'lims',
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r'ibm database 2': 'db/2',
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r'integrated development environment': 'ide',
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r'software development kit': 'sdk',
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r'hp operations orchestration': 'hpoo',
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r'hp server automation': 'hpsa',
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r'internet information server': 'iis',
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r'release 2': 'r2',
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r'red hat enterprise linux': 'rhel',
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r'oracle enterprise linux': 'oel',
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r'websphere application server': 'was',
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r'application development facility': 'adf',
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r'server analysis services': 'ssas'
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}
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abbrev_to_term = {rf'\b{value}\b': key for key, value in term_to_abbrev.items()}
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def replace_terms_with_abbreviations(text):
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for input, replacement in term_to_abbrev.items():
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text = re.sub(input, replacement, text)
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return text
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def replace_abbreivations_with_terms(text):
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for input, replacement in abbrev_to_term.items():
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text = re.sub(input, replacement, text)
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return text
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# outputs a list of dictionaries
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# processes dataframe into lists of dictionaries
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# each element maps input to output
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# input: tag_description
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# output: class label
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def process_df_to_dict(df):
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output_list = []
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for _, row in df.iterrows():
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# produce shuffling
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index = row['entity_id']
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parent_desc = row['mention']
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parent_desc = preprocess_text(parent_desc)
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# ensure at least 1 shuffle
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# no_of_shuffles = label2weight[index] + 1
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no_of_shuffles = SHUFFLES
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processed_descs = shuffle_text(parent_desc, n_shuffles=no_of_shuffles)
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for desc in processed_descs:
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element = {
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'text' : desc,
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'label': label2id[index], # ensure labels starts from 0
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}
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output_list.append(element)
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# perform abbrev_to_term
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desc = replace_terms_with_abbreviations(parent_desc)
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no_of_shuffles = SHUFFLES
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processed_descs = shuffle_text(desc, n_shuffles=no_of_shuffles)
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for desc in processed_descs:
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element = {
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'text' : desc,
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'label': label2id[index], # ensure labels starts from 0
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}
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output_list.append(element)
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# perform term to abbrev
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desc = replace_abbreivations_with_terms(parent_desc)
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no_of_shuffles = SHUFFLES
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processed_descs = shuffle_text(desc, n_shuffles=no_of_shuffles)
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for desc in processed_descs:
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element = {
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'text' : desc,
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'label': label2id[index], # ensure labels starts from 0
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}
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output_list.append(element)
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return output_list
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def create_dataset():
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# train
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data_path = '../../esAppMod_data_import/train.csv'
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train_df = pd.read_csv(data_path, skipinitialspace=True)
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combined_data = DatasetDict({
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'train': Dataset.from_list(process_df_to_dict(train_df)),
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})
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return combined_data
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# %%
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def train():
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save_path = f'checkpoint'
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split_datasets = create_dataset()
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# prepare tokenizer
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model_checkpoint = "distilbert/distilbert-base-uncased"
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# model_checkpoint = 'google-bert/bert-base-cased'
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# model_checkpoint = 'prajjwal1/bert-small'
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tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, return_tensors="pt", clean_up_tokenization_spaces=True)
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# Define additional special tokens
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# additional_special_tokens = ["<DESC>"]
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# Add the additional special tokens to the tokenizer
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# tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
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max_length = 120
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# given a dataset entry, run it through the tokenizer
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def preprocess_function(example):
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input = example['text']
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# text_target sets the corresponding label to inputs
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# there is no need to create a separate 'labels'
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model_inputs = tokenizer(
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input,
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max_length=max_length,
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truncation=True,
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padding=True
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)
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return model_inputs
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# map maps function to each "row" in the dataset
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# aka the data in the immediate nesting
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tokenized_datasets = split_datasets.map(
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preprocess_function,
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batched=True,
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num_proc=8,
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remove_columns="text",
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)
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# %% temp
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# tokenized_datasets['train'].rename_columns()
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# %%
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# create data collator
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data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
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# %%
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# compute metrics
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metric = evaluate.load("accuracy")
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def compute_metrics(eval_preds):
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preds, labels = eval_preds
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preds = np.argmax(preds, axis=1)
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return metric.compute(predictions=preds, references=labels)
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# %%
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# create id2label and label2id
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# %%
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model = AutoModelForSequenceClassification.from_pretrained(
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model_checkpoint,
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num_labels=len(target_id_list),
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id2label=id2label,
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label2id=label2id)
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# important! after extending tokens vocab
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model.resize_token_embeddings(len(tokenizer))
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# model = torch.compile(model, backend="inductor", dynamic=True)
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# %%
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# Trainer
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training_args = TrainingArguments(
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output_dir=f"{save_path}",
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# eval_strategy="epoch",
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eval_strategy="no",
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logging_dir="tensorboard-log",
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logging_strategy="epoch",
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# save_strategy="epoch",
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load_best_model_at_end=False,
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learning_rate=5e-5,
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per_device_train_batch_size=64,
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per_device_eval_batch_size=64,
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auto_find_batch_size=False,
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ddp_find_unused_parameters=False,
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weight_decay=0.01,
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save_total_limit=1,
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num_train_epochs=80,
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warmup_steps=400,
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bf16=True,
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push_to_hub=False,
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remove_unused_columns=False,
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)
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trainer = Trainer(
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model,
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training_args,
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train_dataset=tokenized_datasets["train"],
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tokenizer=tokenizer,
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data_collator=data_collator,
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compute_metrics=compute_metrics,
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# callbacks=[EarlyStoppingCallback(early_stopping_patience=3)],
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)
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# uncomment to load training from checkpoint
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# checkpoint_path = 'default_40_1/checkpoint-5600'
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# trainer.train(resume_from_checkpoint=checkpoint_path)
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trainer.train()
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# execute training
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train()
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# %%
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