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import numpy as np
import random
import torch
import time
import datetime
from transformers.file_utils import is_tf_available, is_torch_available, is_torch_tpu_available
def get_device():
# If there's a GPU available...
if torch.cuda.is_available():
# Tell PyTorch to use the GPU.
device = torch.device("cuda")
print('There are %d GPU(s) available.' % torch.cuda.device_count())
print('We will use the GPU:', torch.cuda.get_device_name(0))
# If not...
else:
print('No GPU available, using the CPU instead.')
device = torch.device("cpu")
return device
def compute_max_sent_length(tokenizer, sentences):
max_len = 0
avg_len = 0
min_len = 100000
# For every sentence...
for sent in sentences:
# Tokenize the text and add `[CLS]` and `[SEP]` tokens.
input_ids = tokenizer.encode(
sent,
truncation=True,
max_length=512,
add_special_tokens=True
)
# Update the maximum sentence length.
max_len = max(max_len, len(input_ids))
# Min length
min_len = min(min_len, len(input_ids))
# Average
avg_len += len(input_ids)
avg_len = avg_len / len(sentences)
print('Max sentence length: ', max_len)
print('Min sentence length: ', min_len)
print('Average sentence length: ', avg_len)
return max_len
def print_model(model):
# Get all of the model's parameters as a list of tuples.
params = list(model.named_parameters())
print('The BERT model has {:} different named parameters.\n'.format(len(params)))
print('==== Embedding Layer ====\n')
for p in params[0:5]:
print("{:<55} {:>12}".format(p[0], str(tuple(p[1].size()))))
print('\n==== First Transformer ====\n')
for p in params[5:21]:
print("{:<55} {:>12}".format(p[0], str(tuple(p[1].size()))))
print('\n==== Output Layer ====\n')
for p in params[-4:]:
print("{:<55} {:>12}".format(p[0], str(tuple(p[1].size()))))
# Function to calculate the accuracy of our predictions vs labels
def flat_accuracy(preds, labels):
pred_flat = np.argmax(preds, axis=1).flatten()
labels_flat = labels.flatten()
return np.sum(pred_flat == labels_flat) / len(labels_flat)
def format_time(elapsed):
'''
Takes a time in seconds and returns a string hh:mm:ss
'''
# Round to the nearest second.
elapsed_rounded = int(round((elapsed)))
# Format as hh:mm:ss
return str(datetime.timedelta(seconds=elapsed_rounded))
def set_seed(seed: int):
"""
Helper function for reproducible behavior to set the seed in ``random``, ``numpy``, ``torch`` and/or ``tf`` (if
installed).
Args:
seed (:obj:`int`): The seed to set.
"""
random.seed(seed)
np.random.seed(seed)
if is_torch_available():
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
# ^^ safe to call this function even if cuda is not available
if is_tf_available():
import tensorflow as tf
tf.random.set_seed(seed)
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