2024-12-19 18:44:46 -05:00
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import collections
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import datasets
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import matplotlib.pyplot as plt
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.optim as optim
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import nltk
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nltk.download('punkt')
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nltk.download('averaged_perceptron_tagger')
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import tqdm
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seed = 1234
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validate_size = 0.25
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np.random.seed(seed)
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torch.manual_seed(seed)
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torch.cuda.manual_seed(seed)
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torch.backends.cudnn.deterministic = True
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train_data, test_data = datasets.load_dataset("imdb", split=["train", "test"])
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print(nltk.pos_tag(nltk.word_tokenize("Hello there you stupid fucking whore mr parker")))
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def tokenize(input):
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return {"tokens": nltk.word_tokenize(input["text"])}
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train_data = train_data.map(tokenize)
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test_data = test_data.map(tokenize)
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train_valid_data = train_data.train_test_split(test_size=validate_size)
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train_data = train_valid_data["train"]
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valid_data = train_valid_data["test"]
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print(train_data)
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print(test_data)
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print(valid_data)
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