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train.py
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train.py
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import numpy as np
import pandas as pd
import tensorflow as tf
BATCH_SIZE = 32
NR_EPOCHS = 6
train_path = 'BERT_MODEL/Preprocessed_Training.csv'
label_cols = ['LGBTQ','ADULT_CONTENT', 'HEALTH', 'DRUGS_ALCOHOL_GAMBLING', 'RACE', 'VIOLENCE_CRIME', 'POLITICS', 'RELATION', 'LOCATION']
cols_to_use = ['Note','LGBTQ','ADULT_CONTENT', 'HEALTH', 'DRUGS_ALCOHOL_GAMBLING', 'RACE', 'VIOLENCE_CRIME', 'POLITICS', 'RELATION', 'LOCATION']
df_train = pd.read_csv(train_path, usecols= cols_to_use)
sentences = df_train['Note']
labels = df_train[label_cols].values
from transformers import BertTokenizer
bert_model_name = 'bert-base-uncased'
print('Loading BERT tokenizer...')
tokenizer = BertTokenizer.from_pretrained(bert_model_name, do_lower_case=True)
max_len = 0
n = 0
s = 0
for sent in sentences:
#print(sent)
input_ids = tokenizer.encode(sent, add_special_tokens=True)
s = s + len(input_ids)
max_len = max(max_len, len(input_ids))
n = n + 1
print('Number of sentences: ', n)
print('Max sentence length: ', max_len)
print('Avg sentence length: ', s/n)
MAX_LEN = 30
input_ids = []
attention_masks = []
for sent in sentences:
encoded_dict = tokenizer.encode_plus(
sent, # Sentence to encode.
add_special_tokens = True, # Add '[CLS]' and '[SEP]'
truncation = 'longest_first',
max_length = MAX_LEN, # Pad & truncate all sentences.
# pad_to_max_length = True,
padding = 'max_length',
return_attention_mask = True, # Construct attn. masks.
# return_tensors = 'tf', # Return tensorflow tensor.
)
input_ids.append(encoded_dict['input_ids'])
attention_masks.append(encoded_dict['attention_mask'])
print(input_ids[:5])
print(attention_masks[:5])
from sklearn.model_selection import train_test_split
train_inputs, validation_inputs, train_labels, validation_labels = train_test_split(input_ids, labels, random_state=39, test_size=0.1)
train_masks, validation_masks, _, _ = train_test_split(attention_masks, labels, random_state=39, test_size=0.1)
train_size = len(train_inputs)
print('Training size: ', train_size)
validation_size = len(validation_inputs)
print('Validation size: ', validation_size)
def create_dataset(data_tuple, epochs=1, batch_size=32, buffer_size=100, train=True):
dataset = tf.data.Dataset.from_tensor_slices(data_tuple)
if train:
dataset = dataset.shuffle(buffer_size=buffer_size)
dataset = dataset.repeat(epochs)
dataset = dataset.batch(batch_size)
if train:
dataset = dataset.prefetch(1)
return dataset
train_dataset = create_dataset((train_inputs, train_masks, train_labels), epochs=1, batch_size=32)
print(type(train_dataset))
validation_dataset = create_dataset((validation_inputs, validation_masks, validation_labels), epochs=1, batch_size=32)
label_cols = ['LGBTQ','ADULT_CONTENT', 'HEALTH', 'DRUGS_ALCOHOL_GAMBLING', 'RACE', 'VIOLENCE_CRIME', 'POLITICS', 'RELATION', 'LOCATION']
from transformers import TFBertModel
from tensorflow.keras.layers import Dense, Flatten
class BertClassifier(tf.keras.Model):
def __init__(self, bert: TFBertModel, num_classes: int):
super().__init__()
self.bert = bert
self.classifier = Dense(num_classes, activation='sigmoid')
@tf.function
def call(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None):
outputs = self.bert(input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask)
cls_output = outputs[1]
cls_output = self.classifier(cls_output)
return cls_output
model = BertClassifier(TFBertModel.from_pretrained(bert_model_name), len(label_cols))
import time
steps_per_epoch = train_size // BATCH_SIZE
validation_steps = validation_size // BATCH_SIZE
# | Loss Function
loss_object = tf.keras.losses.BinaryCrossentropy(from_logits=False)
train_loss = tf.keras.metrics.Mean(name='train_loss')
validation_loss = tf.keras.metrics.Mean(name='test_loss')
# | Optimizer (with 1-cycle-policy)
optimizer = tf.keras.optimizers.Adam(learning_rate=2e-5, epsilon=1e-8)
# | Metrics
train_auc_metrics = [tf.keras.metrics.AUC() for i in range(len(label_cols))]
validation_auc_metrics = [tf.keras.metrics.AUC() for i in range(len(label_cols))]
@tf.function
def train_step(model, token_ids, masks, labels):
labels = tf.dtypes.cast(labels, tf.float32)
with tf.GradientTape() as tape:
predictions = model(token_ids, attention_mask=masks)
loss = loss_object(labels, predictions)
gradients = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
train_loss(loss)
for i, auc in enumerate(train_auc_metrics):
auc.update_state(labels[:,i], predictions[:,i])
@tf.function
def validation_step(model, token_ids, masks, labels):
labels = tf.dtypes.cast(labels, tf.float32)
predictions = model(token_ids, attention_mask=masks, training=False)
v_loss = loss_object(labels, predictions)
validation_loss(v_loss)
for i, auc in enumerate(validation_auc_metrics):
auc.update_state(labels[:,i], predictions[:,i])
def train(model, train_dataset, val_dataset, train_steps_per_epoch, val_steps_per_epoch, epochs):
for epoch in range(epochs):
print('=' * 50, f"EPOCH {epoch}", '=' * 50)
print('step = {}'.format(str(train_steps_per_epoch)))
start = time.time()
#for i, (token_ids, masks, labels) in enumerate(train_dataset, train_steps_per_epoch):
for i, (token_ids, masks, labels) in enumerate(train_dataset):
#print(model, token_ids, masks, labels)
train_step(model, token_ids, masks, labels)
if i % 50 == 0:
print(f'\nTrain Step: {i}, Loss: {train_loss.result()}')
for i, label_name in enumerate(label_cols):
print(f"{label_name} roc_auc {train_auc_metrics[i].result()}")
train_auc_metrics[i].reset_states()
#for i, (token_ids, masks, labels) in enumerate(val_dataset, val_steps_per_epoch):
for i, (token_ids, masks, labels) in enumerate(val_dataset):
validation_step(model, token_ids, masks, labels)
print(f'\nEpoch {epoch+1}, Validation Loss: {validation_loss.result()}, Time: {time.time()-start}\n')
for i, label_name in enumerate(label_cols):
print(f"{label_name} roc_auc {validation_auc_metrics[i].result()}")
validation_auc_metrics[i].reset_states()
print('\n')
train(model, train_dataset, validation_dataset, train_steps_per_epoch=steps_per_epoch, val_steps_per_epoch=validation_steps, epochs=NR_EPOCHS)
model.save_weights('BERT_MODEL/checkpoint_EPOCHS_6m')
print("DONE!")