LLM Tutorial 9 — RoBERTa: Robustly Optimized BERT Pretraining

Learn how RoBERTa is an optimized version of BERT with better performance.

Table of Contents 1. Introduction 2. What is BERT and why is it important? 3. How RoBERTa improves upon BERT 4. How to use RoBERTa for various NLP tasks 5. Conclusion

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1. Introduction

In this blog, you will learn about RoBERTa, a robustly optimized version of BERT, one of the most popular and powerful language models in natural language processing (NLP). You will learn how RoBERTa improves upon BERT by using more data, longer training time, larger batch size, and some other tweaks. You will also learn how to use RoBERTa for various NLP tasks, such as text classification, sentiment analysis, question answering, and more.

But before we dive into RoBERTa, let’s first review what BERT is and why it is important for NLP.

2. What is BERT and why is it important?

BERT stands for Bidirectional Encoder Representations from Transformers, and it is a language model that was introduced by Google researchers in 2018. BERT is a powerful and versatile model that can be used for various NLP tasks, such as text classification, sentiment analysis, question answering, named entity recognition, and more.

But what makes BERT so special and important for NLP? Here are some of the key features and advantages of BERT:

Bidirectional: BERT is able to learn from both the left and the right context of a word, meaning that it can capture the meaning of a word based on its surrounding words. This is different from traditional language models that only learn from either the left or the right context, which limits their ability to understand the semantics of a sentence.
Encoder: BERT is based on the encoder part of the Transformer architecture, which is a neural network that consists of multiple layers of self-attention and feed-forward sub-layers. The encoder takes a sequence of tokens (words or subwords) as input and outputs a sequence of hidden representations that capture the syntactic and semantic information of the input. The encoder can process long sequences of tokens efficiently and effectively, thanks to the self-attention mechanism that allows the model to focus on the relevant parts of the input.
Representations: BERT is able to learn general-purpose representations of natural language that can be used for various downstream tasks. BERT is pre-trained on a large corpus of text using two unsupervised objectives: masked language modeling and next sentence prediction. Masked language modeling is a task where some of the tokens in the input are randomly masked (replaced with a special token), and the model has to predict the original tokens based on the context. Next sentence prediction is a task where the model has to predict whether two sentences are consecutive or not in the original text. By pre-training on these objectives, BERT learns to understand the structure and meaning of natural language at a deep level.
Transformers: BERT is built on the Transformer architecture, which is a novel and powerful neural network that uses attention mechanisms to model the relationships between tokens in a sequence. Transformers have several advantages over traditional recurrent or convolutional neural networks, such as parallelization, scalability, and interpretability. Transformers have been shown to achieve state-of-the-art results on various NLP tasks, such as machine translation, text summarization, and natural language generation.

As you can see, BERT is a remarkable and influential model that has revolutionized the field of NLP. However, BERT is not perfect, and there is still room for improvement. That’s where RoBERTa comes in.

3. How RoBERTa improves upon BERT

RoBERTa is a robustly optimized version of BERT that was introduced by Facebook researchers in 2019. RoBERTa is based on the same architecture and objectives as BERT, but it uses more data, longer training time, larger batch size, and some other tweaks to improve the performance and generalization of the model. RoBERTa has been shown to outperform BERT on several NLP benchmarks, such as GLUE, SQuAD, and RACE.

But how exactly does RoBERTa improve upon BERT? Here are some of the main differences and improvements that RoBERTa introduces:

More data: RoBERTa uses more data for pre-training than BERT. BERT was pre-trained on two corpora: BooksCorpus (800M words) and English Wikipedia (2,500M words). RoBERTa adds more data from various sources, such as CommonCrawl News, OpenWebText, and Stories, resulting in a total of 160GB of text, which is about 10 times more than BERT.
Longer training time: RoBERTa trains for longer than BERT. BERT was trained for 1M steps with a batch size of 256, which corresponds to 40 epochs over the data. RoBERTa trains for 500K steps with a batch size of 8K, which corresponds to 40 epochs over 10 times more data. RoBERTa also uses a larger learning rate of 0.0006, compared to BERT’s 0.0001.
Larger batch size: RoBERTa uses a larger batch size than BERT. BERT used a batch size of 256, which means that it processed 256 sequences of tokens at a time. RoBERTa uses a batch size of 8K, which means that it processes 8K sequences of tokens at a time. This allows RoBERTa to learn from more data in each iteration and achieve a higher level of parallelism.
Some other tweaks: RoBERTa also makes some other changes to the BERT model and training process, such as: — Removing the next sentence prediction objective, which was found to be unnecessary and even harmful for downstream tasks. — Using more dynamic masking patterns, which means that the masked tokens are chosen differently for each epoch, rather than being fixed throughout the training. — Using byte-pair encoding (BPE) as the subword tokenizer, rather than WordPiece, which reduces the vocabulary size and the number of out-of-vocabulary tokens.

Using larger sequence lengths of 512 tokens for all pre-training data, rather than using shorter sequences for some data as BERT did.

By making these improvements, RoBERTa is able to learn better representations of natural language that can be fine-tuned for various downstream tasks. In the next section, we will see how to use RoBERTa for some common NLP tasks, such as text classification, sentiment analysis, question answering, and more.

4. How to use RoBERTa for various NLP tasks

In this section, you will learn how to use RoBERTa for various NLP tasks, such as text classification, sentiment analysis, question answering, and more. You will use the Hugging Face Transformers library, which provides a high-level API for working with various pre-trained language models, including RoBERTa. You will also use PyTorch, a popular deep learning framework, to build and train your models.

The general steps for using RoBERTa for any NLP task are as follows:

Load the pre-trained RoBERTa model and tokenizer: You can use the AutoModel and AutoTokenizer classes from the Transformers library to load the pre-trained RoBERTa model and tokenizer. You can specify the model name as "roberta-base" for the base version of RoBERTa, or "roberta-large" for the large version of RoBERTa. For example, to load the base model and tokenizer, you can use the following code:

from transformers import AutoModel, AutoTokenizer

# Load the pre-trained RoBERTa model and tokenizer
model = AutoModel.from_pretrained("roberta-base")
tokenizer = AutoTokenizer.from_pretrained("roberta-base")

Prepare the input data: You need to prepare the input data for your specific task, such as text classification, sentiment analysis, question answering, etc. You need to convert the raw text into numerical tokens that can be fed into the RoBERTa model. You can use the tokenizer object to encode the text into tokens, and add the special tokens such as "[CLS]" and "[SEP]" that are required by the RoBERTa model. You also need to pad or truncate the tokens to a fixed length, and create attention masks to indicate which tokens are relevant and which are padding. For example, to encode a single sentence for text classification, you can use the following code:

# Encode a single sentence for text classification
sentence = "This is a positive sentence."
encoded = tokenizer.encode_plus(sentence, add_special_tokens=True, max_length=128, pad_to_max_length=True, return_attention_mask=True, return_tensors="pt")
input_ids = encoded["input_ids"] # The token ids
attention_mask = encoded["attention_mask"] # The attention mask

Fine-tune the RoBERTa model for your task: You need to fine-tune the RoBERTa model for your specific task, such as text classification, sentiment analysis, question answering, etc. You need to add a task-specific layer on top of the RoBERTa model, such as a linear layer for text classification, or a span prediction layer for question answering. You also need to define the loss function, the optimizer, and the learning rate for your task. You then need to train the model on your training data, and evaluate it on your validation data. For example, to fine-tune the RoBERTa model for text classification, you can use the following code:

import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from transformers import AdamW

# Define the text classification layer
class TextClassifier(nn.Module):
    def __init__(self, model, num_labels):
        super(TextClassifier, self).__init__()
        self.model = model
        self.linear = nn.Linear(model.config.hidden_size, num_labels)
    def forward(self, input_ids, attention_mask, labels=None):
        # Get the last hidden state from the RoBERTa model
        outputs = self.model(input_ids, attention_mask)
        last_hidden_state = outputs[0]
        # Get the [CLS] token representation
        cls_rep = last_hidden_state[:, 0, :]
        # Pass it through the linear layer
        logits = self.linear(cls_rep)
        # Compute the loss if labels are provided
        if labels is not None:
            loss_fn = nn.CrossEntropyLoss()
            loss = loss_fn(logits, labels)
            return loss, logits
        else:
            return logits
# Create an instance of the text classifier
num_labels = 2 # Number of labels for binary classification
classifier = TextClassifier(model, num_labels)
# Define the hyperparameters
batch_size = 32
num_epochs = 3
learning_rate = 2e-5
# Create the data loaders
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)
# Create the optimizer
optimizer = AdamW(classifier.parameters(), lr=learning_rate)
# Fine-tune the model
for epoch in range(num_epochs):
    # Train the model on the training data
    classifier.train()
    train_loss = 0
    train_acc = 0
    for batch in train_loader:
        # Get the input ids, attention mask, and labels
        input_ids = batch["input_ids"]
        attention_mask = batch["attention_mask"]
        labels = batch["labels"]
        # Forward pass
        loss, logits = classifier(input_ids, attention_mask, labels)
        # Backward pass and update the parameters
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        # Compute the accuracy
        preds = torch.argmax(logits, dim=1)
        acc = torch.sum(preds == labels) / batch_size
        # Accumulate the loss and accuracy
        train_loss += loss.item()
        train_acc += acc.item()
    # Compute the average loss and accuracy
    train_loss = train_loss / len(train_loader)
    train_acc = train_acc / len(train_loader)
    # Print the results
    print(f"Epoch {epoch+1}, Train Loss: {train_loss:.4f}, Train Acc: {train_acc:.4f}")
    # Evaluate the model on the validation data
    classifier.eval()
    val_loss = 0
    val_acc = 0
    for batch in val_loader:
        # Get the input ids, attention mask, and labels
        input_ids = batch["input_ids"]
        attention_mask = batch["attention_mask"]
        labels = batch["labels"]
        # Forward pass
        with torch.no_grad():
            loss, logits = classifier(input_ids, attention_mask, labels)
        # Compute the accuracy
        preds = torch.argmax(logits, dim=1)
        acc = torch.sum(preds == labels) / batch_size
        # Accumulate the loss and accuracy
        val_loss += loss.item()
        val_acc += acc.item()
    # Compute the average loss and accuracy
    val_loss = val_loss / len(val_loader)
    val_acc = val_acc / len(val_loader)
    # Print the results
    print(f"Epoch {epoch+1}, Val Loss: {val_loss:.4f}, Val Acc: {val_acc:.4f}")

Use the fine-tuned RoBERTa model for inference: Once you have fine-tuned the RoBERTa model for your task, you can use it to make predictions on new data. You need to prepare the input data in the same way as you did for training, and then feed it into the fine-tuned model. You can then get the output of the task-specific layer, such as the logits for text classification, or the start and end scores for question answering. You can then interpret the output according to your task, such as getting the label with the highest probability for text classification, or getting the answer span with the highest score for question answering. For example, to use the fine-tuned RoBERTa model for text classification, you can use the following code:

# Use the fine-tuned RoBERTa model for text classification
sentence = "This is a negative sentence."
# Encode the sentence
encoded = tokenizer.encode_plus(sentence, add_special_tokens=True, max_length=128, pad_to_max_length=True, return_attention_mask=True, return_tensors="pt")
input_ids = encoded["input_ids"]
attention_mask = encoded["attention_mask"]
# Forward pass
with torch.no_grad():
    logits = classifier(input_ids, attention_mask)
# Get the label with the highest probability
probs = torch.softmax(logits, dim=1)
label = torch.argmax(probs, dim=1).item()
# Print the result
print(f"Sentence: {sentence}")
print(f"Label: {label}")

By following these steps, you can use RoBERTa for various NLP tasks, and achieve state-of-the-art results. In the next section, we will conclude this blog and summarize the main points.

5. Conclusion

In this blog, you have learned about RoBERTa, a robustly optimized version of BERT, one of the most popular and powerful language models in natural language processing (NLP). You have learned how RoBERTa improves upon BERT by using more data, longer training time, larger batch size, and some other tweaks. You have also learned how to use RoBERTa for various NLP tasks, such as text classification, sentiment analysis, question answering, and more. You have used the Hugging Face Transformers library and PyTorch to load the pre-trained RoBERTa model and tokenizer, prepare the input data, fine-tune the RoBERTa model for your task, and use the fine-tuned RoBERTa model for inference.

By following this blog, you have gained a deeper understanding of RoBERTa and its applications in NLP. You have also acquired some practical skills and knowledge that can help you solve your own NLP problems using RoBERTa. We hope that you have enjoyed this blog and found it useful and informative. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading!

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Updated weekly — 05.12.2023

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