Here are five techniques or algorithms for data augmentation on text data:
- Synonym replacement: This involves replacing certain words in the text with synonyms to create new examples. This can be done manually or using a synonym generation tool.
- Paraphrasing: This involves creating new examples by paraphrasing the original sentences. This can be done manually or using a paraphrasing tool.
- Backtranslation: This involves translating the text to another language and then back to the original language, creating new examples in the process. This can be done using machine translation tools.
- Text style transfer: This involves transferring the style of one text to another, creating new examples in the process. This can be done using text style transfer models.
- Generative models: This involves using generative models, such as language models or generative adversarial networks, to generate new examples based on the original text. This can be done using pre-trained models or by training a model on the original text.
Synonym Replacement
Here is some sample code to demonstrate synonym replacement
import torch
import transformers
# Load the pre-trained model
model = transformers.BertForMaskedLM.from_pretrained('bert-base-cased')
# Define the device and set the model to evaluation mode
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)
model.eval()
def replace_synonym(model, text, tokenizer, word_idx, temperature=1.0):
"""
Replaces the word at the given index in the text with a synonym generated by the model.
"""
# Split the text into tokens and convert to token IDs
tokens = tokenizer.tokenize(text)
token_ids = tokenizer.convert_tokens_to_ids(tokens)
# Replace the target word with the mask token
token_ids[word_idx] = tokenizer.mask_token_id
tokens[word_idx] = tokenizer.mask_token
# Convert the token IDs and tokens back to a string
masked_text = tokenizer.convert_ids_to_tokens(token_ids)
masked_text = ' '.join(masked_text)
# Encode the text
input_ids = torch.tensor([token_ids], device=device)
token_type_ids = torch.tensor([[0] * len(token_ids)], device=device)
# Generate the synonym
with torch.no_grad():
outputs = model(input_ids, token_type_ids=token_type_ids)
predictions = outputs[0]
# Get the logits for the masked word
masked_word_logits = predictions[0, word_idx]
# Apply temperature
masked_word_logits = masked_word_logits / temperature
# Get the top-k indices of the logits
top_k_indices = torch.topk(masked_word_logits, k=1).indices[0]
# Get the synonym
Above is a function for replacing a synonym with a pre trained language model, here is code on how to use this for data augmentation of text dataset.
import random
# Load the original dataset
dataset = SomeLanguageModelDataset()
# Define the tokenizer
tokenizer = transformers.BertTokenizer.from_pretrained('bert-base-cased')
# Augment the dataset by replacing a random word in each example with a synonym
augmented_dataset = []
for example, target in dataset:
# Select a random word to replace
word_idx = random.randint(0, len(example.split(' '))-1)
# Replace the word with a synonym
augmented_example = replace_synonym(model, example, tokenizer, word_idx)
# Add the augmented example to the dataset
augmented_dataset.append((augmented_example, target))
# Use the augmented dataset as the training set
train_dataloader = torch.utils.data.DataLoader(augmented_dataset, batch_size=batch_size, shuffle=True)
Paraphrasing for data augmentation
Here is an example of how to perform paraphrasing for data augmentation using the latest techniques in PyTorch:
import torch
import transformers
# Load the pre-trained model
model = transformers.T5ForConditionalGeneration.from_pretrained('t5-base')
# Define the device and set the model to evaluation mode
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)
model.eval()
def paraphrase(model, text, temperature=1.0):
"""
Paraphrases the given text using the model.
"""
# Encode the text
input_ids = torch.tensor(model.encode(text, max_length=1024), device=device).unsqueeze(0)
# Generate the paraphrased text
with torch.no_grad():
outputs = model(input_ids, max_length=1024, temperature=temperature)
paraphrased_text = model.decode(outputs[0], skip_special_tokens=True)
return paraphrased_text
# Example usage
text = "The cat sat on the mat."
paraphrased_text = paraphrase(model, text)
print(paraphrased_text)
To use this function for data augmentation, you can apply it to the original training examples to generate new, augmented examples. Here is an example of how to do this:
# Load the original dataset
dataset = SomeLanguageModelDataset()
# Augment the dataset by paraphrasing each example
augmented_dataset = []
for example, target in dataset:
# Paraphrase the example
augmented_example = paraphrase(model, example)
# Add the augmented example to the dataset
augmented_dataset.append((augmented_example, target))
# Use the augmented dataset as the training set
train_dataloader = torch.utils.data.DataLoader(augmented_dataset, batch_size=batch_size, shuffle=True)
Backtranslation for data augmentation
Here is an example of how to perform backtranslation for data augmentation using the latest techniques in PyTorch:
import torch
import transformers
# Load the pre-trained models
source_model = transformers.T5ForConditionalGeneration.from_pretrained('t5-base')
target_model = transformers.T5ForConditionalGeneration.from_pretrained('t5-base')
# Define the device and set the models to evaluation mode
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
source_model.to(device)
source_model.eval()
target_model.to(device)
target_model.eval()
def translate(model, text, source_lang, target_lang, temperature=1.0):
"""
Translates the given text from the source language to the target language using the model.
"""
# Encode the text
input_text = f"translate {source_lang} to {target_lang}: {text}"
input_ids = torch.tensor(model.encode(input_text, max_length=1024), device=device).unsqueeze(0)
# Translate the text
with torch.no_grad():
outputs = model(input_ids, max_length=1024, temperature=temperature)
translated_text = model.decode(outputs[0], skip_special_tokens=True)
return translated_text
def backtranslate(source_model, target_model, text, source_lang, target_lang, temperature=1.0):
"""
Backtranslates the given text from the source language to the target language and then back to the source language.
"""
# Translate the text from the source language to the target language
translated_text = translate(target_model, text, source_lang, target_lang, temperature)
# Translate the text back to the source language
backtranslated_text = translate(source_model, translated_text, target_lang, source_lang, temperature)
return backtranslated_text
# Example usage
text = "The cat sat on the mat."
backtranslated_text = backtranslate(source_model, target_model, text, 'en', 'fr')
print(backtranslated_text)
# Load the original dataset
dataset = SomeLanguageModelDataset()
# Augment the dataset by backtranslating each example
augmented_dataset = []
for example, target in dataset:
# Backtranslate the example
augmented_example = backtranslate(source_model, target_model, example, 'en', 'fr')
# Add the augmented example to the dataset
augmented_dataset.append((augmented_example, target))
# Use the augmented dataset as the training set
train_dataloader = torch.utils.data.DataLoader(augmented_dataset, batch_size=batch_size, shuffle=True)
Style transfer using latest techniques for data augmentation of text dataset
Here is an example of how to perform text style transfer for data augmentation using the latest techniques in PyTorch:
import torch
import transformers
# Load the pre-trained model
model = transformers.GPT2LMHeadModel.from_pretrained('gpt2')
# Define the device and set the model to evaluation mode
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)
model.eval()
def transfer_style(model, text, style, temperature=1.0):
"""
Transfers the style of the given text to the specified style using the model.
"""
# Encode the text
input_text = f"{style}: {text}"
input_ids = torch.tensor(model.encode(input_text, max_length=1024), device=device).unsqueeze(0)
# Transfer the style
with torch.no_grad():
outputs = model(input_ids, max_length=1024, temperature=temperature)
transferred_text = model.decode(outputs[0], skip_special_tokens=True)
return transferred_text
# Example usage
text = "The cat sat on the mat."
transferred_text = transfer_style(model, text, 'poetry')
print(transferred_text)
import random
# Load the original dataset
dataset = SomeLanguageModelDataset()
# Augment the dataset by transferring the style of each example to a different style
augmented_dataset = []
for example, target in dataset:
# Choose a random style to transfer the example to
style = random.choice(['formal', 'informal', 'academic', 'contractions', 'colloquial'])
# Transfer the style of the example
augmented_example = transfer_style(model, example, style)
# Add the augmented example to the dataset
augmented_dataset.append((augmented_example, target))
# Use the augmented dataset as the training set
train_dataloader = torch.utils.data.DataLoader(augmented_dataset, batch_size=batch_size, shuffle=True)
Generative models for data augmentation of text datasets
Here is an example of how to use generative models for data augmentation of text datasets in PyTorch:
import torch
import transformers
# Load the pre-trained model
model = transformers.GPT2LMHeadModel.from_pretrained('gpt2')
# Define the device and set the model to evaluation mode
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)
model.eval()
def generate_text(model, prompt, temperature=1.0):
"""
Generates text based on the given prompt using the model.
"""
# Encode the prompt
input_ids = torch.tensor(model.encode(prompt, max_length=1024), device=device).unsqueeze(0)
# Generate the text
with torch.no_grad():
outputs = model(input_ids, max_length=1024, temperature=temperature)
generated_text = model.decode(outputs[0], skip_special_tokens=True)
return generated_text
# Example usage
prompt = "The cat sat on the mat."
generated_text = generate_text(model, prompt)
print(generated_text)
To use this function for data augmentation, you can apply it to generate new, augmented examples based on the original training examples. Here is an example of how to do this:
# Load the original dataset
dataset = SomeLanguageModelDataset()
# Augment the dataset by generating new examples based on the original examples
augmented_dataset = []
for example, target in dataset:
# Generate a new example based on the original example
augmented_example = generate_text(model, example)
# Add the augmented example to the dataset
augmented_dataset.append((augmented_example, target))
# Use the augmented dataset as the training set
train_dataloader = torch.utils.data.DataLoader(augmented_dataset, batch_size=batch_size, shuffle=True)