akshay-3apr

## Store our loss and accuracy for plotting
train_loss_set = []
learning_rate = []

# Gradients gets accumulated by default
model.zero_grad()

# tnrange is a tqdm wrapper around the normal python range
for _ in tnrange(1,epochs+1,desc='Epoch'):
  print("<" + "="*22 + F" Epoch {_} "+ "="*22 + ">")

akshay-3apr

# Load BertForSequenceClassification, the pretrained BERT model with a single linear classification layer on top. 
model = BertForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=2).to(device)

# Parameters:
lr = 2e-5
adam_epsilon = 1e-8

# Number of training epochs (authors recommend between 2 and 4)
epochs = 3

akshay-3apr

# Split into a training set and a test set using a stratified k fold
train_inputs,validation_inputs,train_labels,validation_labels = train_test_split(input_ids,labels,random_state=SEED,test_size=0.1)
train_masks,validation_masks,_,_ = train_test_split(attention_masks,input_ids,random_state=SEED,test_size=0.1)

# convert all our data into torch tensors, required data type for our model
train_inputs = torch.tensor(train_inputs)
validation_inputs = torch.tensor(validation_inputs)
train_labels = torch.tensor(train_labels)
validation_labels = torch.tensor(validation_labels)
train_masks = torch.tensor(train_masks)

akshay-3apr

df = pd.read_csv("raw/in_domain_train.tsv", delimiter='\t', header=None, names=['sentence_source', 'label', 'label_notes', 'sentence'])
print(df.sample(5))

## create label and sentence list
sentences = df.sentence.values

#check distribution of data based on labels
print("Distribution of data based on labels: ",df.label.value_counts())

# Set the maximum sequence length. The longest sequence in our training set is 47, but we'll leave room on the end anyway. 

akshay-3apr

# To upload data from local disk at run time, uncomment below code
#from google.colab import files
#uploaded = files.upload()

# The below code is when we integrate Google drive to current Colab session.
# This is helpful when we want to store the trained model, and later download it to local.
from google.colab import drive
drive.mount('/content/gdrive')

os.chdir('/content/gdrive/My Drive')

akshay-3apr

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