TinyBERT based model
Fetching the model
import torch
from torch.utils.data import DataLoader, Dataset
from transformers import AutoTokenizer, AutoModelForSequenceClassification, AdamW
from sklearn.model_selection import train_test_split
import pandas as pd
from tqdm import tqdm
# Load the TinyBERT tokenizer and model
tokenizer = AutoTokenizer.from_pretrained('huawei-noah/TinyBERT_General_4L_312D')
model = AutoModelForSequenceClassification.from_pretrained('huawei-noah/TinyBERT_General_4L_312D', num_labels=2)
# fetch the statedict to apply the fine-tuned weights
state_dict = torch.hub.load_state_dict_from_url(f"https://huggingface.co/KameronB/SITCC-Incident-Request-Classifier/resolve/main/tiny_bert_model.bin")
# if running on cpu
# state_dict = torch.hub.load_state_dict_from_url(f"https://huggingface.co/KameronB/SITCC-Incident-Request-Classifier/resolve/main/tiny_bert_model.bin", map_location=torch.device('cpu'))
model.load_state_dict(state_dict)
model = model.to(torch.device('cuda' if torch.cuda.is_available() else 'cpu'))
Using the model
def predict_description(model, tokenizer, text, max_length=512):
model.eval() # Set the model to evaluation mode
# Ensure model is on the correct device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
# Encode the input text
inputs = tokenizer.encode_plus(
text,
None,
add_special_tokens=True,
max_length=max_length,
padding='max_length',
return_token_type_ids=False,
return_tensors='pt',
truncation=True
)
# Move tensors to the correct device
inputs = {key: value.to(device) for key, value in inputs.items()}
# Make prediction
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probabilities = torch.softmax(logits, dim=-1)
predicted_class_id = torch.argmax(probabilities, dim=-1).item()
return predicted_class_id, probabilities.cpu().tolist()
#Example usage
tickets = [
"""Inquiry about the possibility of customizing Docker to better meet department-specific needs.
Gathered requirements for desired customizations.""",
"""We've encountered a recurring problem with DEVEnv shutting down anytime we try to save documents.
I looked over the error logs for any clues about what's going wrong. I'm passing this on to the team responsible for software upkeep."""
]
for row in tickets:
prediction, probabilities = predict_description(model, tokenizer, row)
prediction = (['INCIDENT', 'TASK'])[prediction]
print(f"{prediction} ({probabilities}) <== {row['content']}")
Additional fine-tuning
# The dataset class
class TextDataset(Dataset):
def __init__(self, descriptions, labels, tokenizer, max_len):
self.descriptions = descriptions
self.labels = labels
self.tokenizer = tokenizer
self.max_len = max_len
def __len__(self):
return len(self.descriptions)
def __getitem__(self, idx):
text = self.descriptions[idx]
inputs = self.tokenizer.encode_plus(
text,
None,
add_special_tokens=True,
max_length=self.max_len,
padding='max_length',
return_token_type_ids=False,
truncation=True
)
return {
'input_ids': torch.tensor(inputs['input_ids'], dtype=torch.long),
'attention_mask': torch.tensor(inputs['attention_mask'], dtype=torch.long),
'labels': torch.tensor(self.labels[idx], dtype=torch.long)
}
# =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
# load the data
df = pd.read_csv('..\\data\\final_data.csv')
df['label'] = df['type'].astype('category').cat.codes # Convert labels to category codes if they aren't already
# =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
# create the training and validation sets and data loaders
print( "cuda is available" if torch.cuda.is_available() else "cuda is unavailable: running on cpu")
# Split the data into training and validation sets
train_df, val_df = train_test_split(df, test_size=0.15)
# Create PyTorch datasets
train_dataset = TextDataset(train_df['content'].tolist(), train_df['label'].tolist(), tokenizer, max_len=512)
val_dataset = TextDataset(val_df['content'].tolist(), val_df['label'].tolist(), tokenizer, max_len=512)
# Create data loaders
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=32)
# =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
# Train the model
# only these layers will be trained, customize this to your liking to freeze the ones you dont want to retrain
training_layers = [
"bert.encoder.layer.3.output.dense.weight",
"bert.encoder.layer.3.output.dense.bias",
"bert.encoder.layer.3.output.LayerNorm.weight",
"bert.encoder.layer.3.output.LayerNorm.bias",
"bert.pooler.dense.weight",
"bert.pooler.dense.bias",
"classifier.weight",
"classifier.bias",
]
for name, param in model.named_parameters():
if name not in training_layers: # Freeze layers that are not part of the classifier
param.requires_grad = False
# Training setup
optimizer = AdamW(model.parameters(), lr=5e-5)
epochs = 2
for epoch in range(epochs):
model.train()
loss_item = float('+inf')
for batch in tqdm(train_loader, desc=f"Training Loss: {loss_item}"):
batch = {k: v.to(model.device) for k, v in batch.items()}
outputs = model(**batch)
loss = outputs.loss
loss.backward()
optimizer.step()
optimizer.zero_grad()
loss_item = loss.item()
model.eval()
total_eval_accuracy = 0
for batch in tqdm(val_loader, desc=f"Validation Accuracy: {total_eval_accuracy}"):
batch = {k: v.to(model.device) for k, v in batch.items()}
with torch.no_grad():
outputs = model(**batch)
logits = outputs.logits
predictions = torch.argmax(logits, dim=-1)
accuracy = (predictions == batch['labels']).cpu().numpy().mean()
total_eval_accuracy += accuracy
print(f"Validation Accuracy: {total_eval_accuracy / len(val_loader)}")
DistilBERT based model
Fetching the model
import torch
from torch.utils.data import DataLoader, Dataset
from transformers import AutoTokenizer, AutoModelForSequenceClassification, AdamW
from sklearn.model_selection import train_test_split
import pandas as pd
from tqdm import tqdm
# Load the TinyBERT tokenizer and model
tokenizer = AutoTokenizer.from_pretrained('distilbert/distilbert-base-uncased')
model = AutoModelForSequenceClassification.from_pretrained('distilbert/distilbert-base-uncased', num_labels=2)
# fetch the statedict to apply the fine-tuned weights
state_dict = torch.hub.load_state_dict_from_url(f"https://huggingface.co/KameronB/SITCC-Incident-Request-Classifier/resolve/main/distilbert_1.bin")
# if running on cpu
# state_dict = torch.hub.load_state_dict_from_url(f"https://huggingface.co/KameronB/SITCC-Incident-Request-Classifier/resolve/main/distilbert_1.bin", map_location=torch.device('cpu'))
model.load_state_dict(state_dict)
model = model.to(torch.device('cuda' if torch.cuda.is_available() else 'cpu'))
Using the model
def predict_description(model, tokenizer, text, max_length=512):
model.eval() # Set the model to evaluation mode
# Ensure model is on the correct device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
# Encode the input text
inputs = tokenizer.encode_plus(
text,
None,
add_special_tokens=True,
max_length=max_length,
padding='max_length',
return_token_type_ids=False,
return_tensors='pt',
truncation=True
)
# Move tensors to the correct device
inputs = {key: value.to(device) for key, value in inputs.items()}
# Make prediction
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probabilities = torch.softmax(logits, dim=-1)
predicted_class_id = torch.argmax(probabilities, dim=-1).item()
return predicted_class_id, probabilities.cpu().tolist()
#Example usage
tickets = [
"""Inquiry about the possibility of customizing Docker to better meet department-specific needs.
Gathered requirements for desired customizations.""",
"""We've encountered a recurring problem with DEVEnv shutting down anytime we try to save documents.
I looked over the error logs for any clues about what's going wrong. I'm passing this on to the team responsible for software upkeep."""
]
for row in tickets:
prediction, probabilities = predict_description(model, tokenizer, row)
prediction = (['INCIDENT', 'TASK'])[prediction]
print(f"{prediction} ({probabilities}) <== {row['content']}")
Additional fine-tuning
# The dataset class
class TextDataset(Dataset):
def __init__(self, descriptions, labels, tokenizer, max_len):
self.descriptions = descriptions
self.labels = labels
self.tokenizer = tokenizer
self.max_len = max_len
def __len__(self):
return len(self.descriptions)
def __getitem__(self, idx):
text = self.descriptions[idx]
inputs = self.tokenizer.encode_plus(
text,
None,
add_special_tokens=True,
max_length=self.max_len,
padding='max_length',
return_token_type_ids=False,
truncation=True
)
return {
'input_ids': torch.tensor(inputs['input_ids'], dtype=torch.long),
'attention_mask': torch.tensor(inputs['attention_mask'], dtype=torch.long),
'labels': torch.tensor(self.labels[idx], dtype=torch.long)
}
# =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
# load the data
df = pd.read_csv('..\\data\\final_data.csv')
df['label'] = df['type'].astype('category').cat.codes # Convert labels to category codes if they aren't already
# =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
# create the training and validation sets and data loaders
print( "cuda is available" if torch.cuda.is_available() else "cuda is unavailable: running on cpu")
# Split the data into training and validation sets
train_df, val_df = train_test_split(df, test_size=0.15)
# Create PyTorch datasets
train_dataset = TextDataset(train_df['content'].tolist(), train_df['label'].tolist(), tokenizer, max_len=512)
val_dataset = TextDataset(val_df['content'].tolist(), val_df['label'].tolist(), tokenizer, max_len=512)
# Create data loaders
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=32)
# =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
# Train the model
# only these layers will be trained, customize this to your liking to freeze the ones you dont want to retrain
training_layers = [
"distilbert.transformer.layer.5.ffn.lin2.weight",
"distilbert.transformer.layer.5.ffn.lin2.bias",
"distilbert.transformer.layer.5.output_layer_norm.weight",
"distilbert.transformer.layer.5.output_layer_norm.bias",
"pre_classifier.weight",
"pre_classifier.bias",
"classifier.weight",
"classifier.bias"
]
for name, param in model.named_parameters():
if name not in training_layers: # Freeze layers that are not part of the classifier
param.requires_grad = False
# if the model is not already on gpu, make sure to train it on gpu if available
# model = model.to(torch.device('cuda' if torch.cuda.is_available() else 'cpu'))
# Training setup
optimizer = AdamW(model.parameters(), lr=5e-5)
epochs = 2
for epoch in range(epochs):
model.train()
loss_item = float('+inf')
for batch in tqdm(train_loader, desc=f"Training Loss: {loss_item}"):
batch = {k: v.to(model.device) for k, v in batch.items()}
outputs = model(**batch)
loss = outputs.loss
loss.backward()
optimizer.step()
optimizer.zero_grad()
loss_item = loss.item()
model.eval()
total_eval_accuracy = 0
for batch in tqdm(val_loader, desc=f"Validation Accuracy: {total_eval_accuracy}"):
batch = {k: v.to(model.device) for k, v in batch.items()}
with torch.no_grad():
outputs = model(**batch)
logits = outputs.logits
predictions = torch.argmax(logits, dim=-1)
accuracy = (predictions == batch['labels']).cpu().numpy().mean()
total_eval_accuracy += accuracy
print(f"Validation Accuracy: {total_eval_accuracy / len(val_loader)}")
RoBERT based model
Base model
import torch
from torch.utils.data import DataLoader, Dataset
from transformers import RobertaTokenizer, RobertaForSequenceClassification, AdamW
from sklearn.model_selection import train_test_split
import pandas as pd
# Load the tokenizer
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
# Load RoBERTa pre-trained model
model = RobertaForSequenceClassification.from_pretrained('roberta-base', num_labels=2)
# fetch the statedict to apply the fine-tuned weights
state_dict = torch.hub.load_state_dict_from_url(f"https://huggingface.co/KameronB/SITCC-Incident-Request-Classifier/resolve/main/pytorch_model.bin")
# if running on cpu
# state_dict = torch.hub.load_state_dict_from_url(f"https://huggingface.co/KameronB/SITCC-Incident-Request-Classifier/resolve/main/pytorch_model.bin", map_location=torch.device('cpu'))
model.load_state_dict(state_dict)
model = model.to(torch.device('cuda' if torch.cuda.is_available() else 'cpu'))
Use model to make predictions
def predict_description(model, tokenizer, text, max_length=512):
model.eval() # Set the model to evaluation mode
# Ensure model is on the correct device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
# Encode the input text
inputs = tokenizer.encode_plus(
text,
None,
add_special_tokens=True,
max_length=max_length,
padding='max_length',
return_token_type_ids=False,
return_tensors='pt',
truncation=True
)
# Move tensors to the correct device
inputs = {key: value.to(device) for key, value in inputs.items()}
# Make prediction
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probabilities = torch.softmax(logits, dim=-1)
predicted_class_id = torch.argmax(probabilities, dim=-1).item()
return predicted_class_id
(['INCIDENT', 'REQUEST'])[predict_description(model, tokenizer, """My ID card is not being detected.""")]
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