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# -*- coding: utf-8 -*-
"""Finetuning Language Models - Can I Patent This?.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1x9XfLKvGNBsajOK8rztsZnCoD2ucGfO6
# Finetuning Language Models - Can I Patent This?
Using the [Harvard USPTO patent dataset](https://github.com/suzgunmirac/hupd), we will fine-tune a DistilBERT model
obtained from Hugging Face that can predict whether a patent is accepted or rejected based off of its abstract and claims.
"""
import gc
import argparse
import numpy as np
import torch
from torch.utils.data import DataLoader
from torch.optim import AdamW
from datasets import load_dataset, load_from_disk
from transformers import DistilBertForSequenceClassification, DistilBertTokenizer, DistilBertConfig
# Initializing global variables
file_path = '/app/models/content/'
decision_to_str = {'REJECTED': 0, 'ACCEPTED': 1, 'PENDING': 2, 'CONT-REJECTED': 3, 'CONT-ACCEPTED': 4, 'CONT-PENDING': 5}
criterion = torch.nn.CrossEntropyLoss()
def create_dataloaders(dataset_dict, section):
# Initializing the tokenizer
model_name = 'distilbert-base-uncased'
tokenizer = DistilBertTokenizer.from_pretrained(model_name, do_lower_case=True)
train_set, val_set = dataset_dict['train'], dataset_dict['validation']
# Training set
train_set = train_set.map(
lambda e: tokenizer((e[section]), truncation=True, padding='max_length'),
batched=True)
# Validation set
val_set = val_set.map(
lambda e: tokenizer((e[section]), truncation=True, padding='max_length'),
batched=True)
train_set.set_format(type='torch',
columns=['input_ids', 'attention_mask', 'decision'])
val_set.set_format(type='torch',
columns=['input_ids', 'attention_mask', 'decision'])
train_loader = DataLoader(train_set, batch_size=8, shuffle=True)
val_loader = DataLoader(val_set, batch_size=8, shuffle=False)
return train_loader, val_loader, tokenizer
def measure_accuracy(outputs, labels):
# This function will accept a model's outputs and the actual decisions
# and return test accuracy and number of samples.
preds = np.argmax(outputs, axis=1).flatten()
labels = labels.flatten()
correct = np.sum(preds == labels)
return correct, len(labels)
def validation(model, val_loader):
# This function accepts a model and a validation set DataLoader as its parameters
# and returns the test accuracy.
model.eval()
total_correct = 0
total_samples = 0
for batch in val_loader:
input_ids = batch['input_ids'].to(device)
labels = batch['decision'].to(device)
with torch.no_grad():
outputs = model(input_ids=input_ids, labels=labels)
logits = outputs.logits
num_correct, num_samples = measure_accuracy(logits.cpu().numpy(), labels.cpu().numpy())
total_correct += num_correct
total_samples += num_samples
del input_ids, labels, logits
gc.collect()
torch.cuda.empty_cache()
return (total_correct/total_samples) * 100
def train(device, model, tokenizer, train_loader, val_loader, section):
# This function will accept a model, the training set DataLoader, validation set
# DataLoader, and section as its parameters and return the trained model.
model.train()
# Define optimizer.
optim = AdamW(model.parameters(), lr=5e-5)
num_epochs = 5
best_val_acc = 0
for epoch in range(num_epochs):
for batch in train_loader:
optim.zero_grad()
input_ids = batch['input_ids'].to(device, non_blocking=True)
attention_mask = batch['attention_mask'].to(device, non_blocking=True)
labels = batch['decision'].to(device, non_blocking=True)
outputs = model(input_ids, attention_mask=attention_mask, labels=labels).logits
loss = criterion(outputs, labels)
loss.backward()
optim.step()
del input_ids, attention_mask, labels
gc.collect()
torch.cuda.empty_cache()
# Calculate test accuracy.
val_acc = validation(model, val_loader)
# Save the model that yields the best test accuracy
if best_val_acc < val_acc:
best_val_acc = val_acc
model.save_pretrained(file_path + section + '/')
tokenizer.save_pretrained(file_path + section + '_model_tokenizer/')
model.train()
return model
if __name__ == '__main__':
device = 'cuda' if torch.cuda.is_available() else 'cpu'
parser = argparse.ArgumentParser()
parser.add_argument('--section', type=str)
args = parser.parse_args()
section = args.section
dataset_dict = load_from_disk(file_path + 'dataset_dict')
train_loader, val_loader, tokenizer = create_dataloaders(dataset_dict, section)
del dataset_dict
gc.collect()
torch.cuda.empty_cache()
# Defining the models.
config = DistilBertConfig(num_classes=2, output_hidden_states=False)
model = DistilBertForSequenceClassification(config=config)
model.to(device)
# Train the model.
model = train(device, model, tokenizer, train_loader, val_loader, section)
val_acc = validation(model, val_loader)
print(f'*** Accuracy on the validation set ({section}): {val_acc}')
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