BERT（Bidirectional Encoder Representations from Transformers）
BERT (Bidirectional Encoder Representations from Transformers), BERT was published by Google researchers in 2018 and is a deep neural network model pre-trained with a large text corpus and is one of the very successful pre-training models in the field of natural language processing (NLP). The main features and overview of BERT are described below.
1. Bidirectional:
BERT differs from traditional NLP models in that it represents words in a bidirectional context. This is the reason for its superior performance on many NLP tasks, as it produces a richer contextual representation.
2. Transformer architecture:
BERT is based on an architecture called Transformer described in “Overview of Transformer Models, Algorithms, and Examples of Implementations“, which can efficiently capture contextual information using the Self-Attention mechanism described in “Attention in Deep Learning” and from which BERT BERT is benefiting from this.
3. large scale pre-training:
BERT is pre-trained on very large text data sets. This pre-training allows the model to acquire general language comprehension skills and to better adapt to task-specific data.
4. versatility:
BERT can be used for many NLP tasks, and by fine-tuning pre-trained models to task-specific data, it has demonstrated high performance on a variety of tasks, including text classification, document generation, question answering, and machine translation.
5. providing pre-trained models:.
BERT is available as a pre-trained model, which researchers and developers can download and use. It can also be easily integrated with many NLP libraries and frameworks (e.g., Hugging Face Transformers, spaCy, described in “Overview of Automatic Sentence Generation with Huggingface“).
6. context-sensitive:
BERT is a context-sensitive model, which generates different embeddings for the same word depending on the context. This makes it excellent for understanding word polysemy and context.
BERT has revolutionized the NLP community, achieving top performance on many NLP tasks. the success of BERT has contributed to the widespread acceptance of prior learning-based approaches as a means of solving key challenges in NLP, and many derivative models based on BERT have since been developed and have contributed to the evolution of NLP research and applications.
Specific steps for BERT
The specific steps of BERT (Bidirectional Encoder Representations from Transformers) are roughly as follows: BERT is pre-trained from a large corpus of text, so the process is very computationally and data dependent. Since BERT is pre-trained from a large text corpus, the process is very computationally and data-dependent, but the general flow is as follows:
1. pre-training data collection:
To train BERT, a very large text corpus is needed. This is collected from texts on the web, books, Wikipedia, etc.
2. text preprocessing:
Pre-process the collected text data. This step includes tokenizing the text (splitting it into words and subwords), segmenting sentences, and inserting special tokens.
3. architecture selection for the BERT model:
BERT is based on the Transformer architecture, but is achieved by choosing the parameters of the architecture, such as the depth and width of the model and the number of dimensions of the embedding. In general, larger models provide higher performance, but they are also computationally more expensive.
4. pre-training run:
Perform a pre-training run using the selected BERT model architecture. This involves the process of feeding textual data into the model and fine-tuning the model parameters, with the goal of pre-training being to obtain a contextual representation of the words and tokens.
5. model storage:
Once the pre-training is complete, the trained BERT model is saved. This model will later be used for task-specific fine tuning.
6 Fine Tuning:
BERT models have general language comprehension capabilities and can be applied to a variety of NLP tasks. Fine tuning uses task-specific data sets to fine tune the model and tailor its performance to a specific task.
7. application to tasks:
Fine-tuned BERT models can be applied to a variety of NLP tasks, including text classification, entity recognition, and machine translation. Input text is fed into the model to generate task-specific predictions.
8. evaluation and tuning:
Evaluate model performance and adjust hyperparameters and training process as needed. The fine-tuned model may be further adjusted to improve performance.
Examples of BERT implementations
BERTの実装例について述べる。BERTの実装には、主要な深層学習フレームワーク（PyTorch、TensorFlowなど）を使用することが一般的となる。以下に、Hugging Face Transformersライブラリを使用したPyTorchをベースとしたBERTの実装例を示す。このライブラリはBERTを含むさまざまなNLPモデルを提供し、使いやすさと柔軟性を提供している。
import torch
from transformers import BertTokenizer, BertForSequenceClassification
from transformers import AdamW
from transformers import get_linear_schedule_with_warmup
from torch.utils.data import DataLoader, TensorDataset, random_split
# BERT Model Preparation
model_name = 'bert-base-uncased' # Name of pre-trained BERT model
tokenizer = BertTokenizer.from_pretrained(model_name)
model = BertForSequenceClassification.from_pretrained(model_name, num_labels=2) # 2クラス分類の例
# Data preparation (example of text classification)
texts = ["This is a positive sentence.", "This is a negative sentence."]
labels = [1, 0]
# Tokenize text and convert to a format understood by the model
tokenized_texts = tokenizer(texts, padding=True, truncation=True, return_tensors='pt')
labels = torch.tensor(labels)
# Creating Data Sets
dataset = TensorDataset(tokenized_texts['input_ids'], tokenized_texts['attention_mask'], labels)
# Split dataset into training and validation
train_size = int(0.8 * len(dataset))
val_size = len(dataset) - train_size
train_dataset, val_dataset = random_split(dataset, [train_size, val_size])
# DataLoaderの作成
batch_size = 2
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=batch_size)
# Optimizer and Scheduler Settings
optimizer = AdamW(model.parameters(), lr=1e-5)
scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=0, num_training_steps=len(train_loader))
# training loop
epochs = 3
for epoch in range(epochs):
model.train()
total_loss = 0.0
for batch in train_loader:
input_ids, attention_mask, labels = batch
optimizer.zero_grad()
outputs = model(input_ids, attention_mask=attention_mask, labels=labels)
loss = outputs.loss
loss.backward()
optimizer.step()
scheduler.step()
total_loss += loss.item()
# Verification per training epoch
model.eval()
val_loss = 0.0
correct = 0
total = 0
with torch.no_grad():
for batch in val_loader:
input_ids, attention_mask, labels = batch
outputs = model(input_ids, attention_mask=attention_mask, labels=labels)
loss = outputs.loss
val_loss += loss.item()
_, predicted = torch.max(outputs.logits, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print(f'Epoch {epoch + 1}/{epochs}, Train Loss: {total_loss / len(train_loader)}, Validation Loss: {val_loss / len(val_loader)}, Validation Accuracy: {(correct / total) * 100}%')In this example, the BERT model is loaded, the text data is tokenized to create a dataset, and BERT is fine-tuned to solve the text classification task. It also provides a training loop to evaluate the performance of the model at each epoch.
Challenges for BERT
BERT (Bidirectional Encoder Representations from Transformers) is a very powerful NLP model, but there are some challenges and limitations. The main challenges of BERT are described below.
1. computational resource requirements:
BERT is a very large model and requires a lot of computational resources for training and inference. Training and operating a large model is expensive, requiring high-performance hardware and distributed computing environments.
2. large data set dependencies:
BERT is pre-trained on large textual data sets, which requires high-quality data sets. It can be especially difficult to obtain datasets suitable for low-resource languages or specific domains.
3. constraints on handling long texts:
Since BERT processes text on a token-by-token basis, there are limitations in processing very long sentences and texts. Therefore, it is necessary to break up long sentences or truncate parts of them.
4. dealing with polysemous words:
BERT generates embeddings that take into account the context of words and tokens, but is limited to cases where the same word has different meanings in different contexts. Dealing with polysemous words is also a major challenge for NLP.
5. language generality:
BERT is effective for many languages, but its performance for certain low-resource languages may be poor. Therefore, data collection and research is underway for various languages in order to improve multilingualizability.
6. interpretability constraints:
BERT generates high-dimensional embeddings, making it difficult to interpret how the model is learning information. Increased interpretability is needed.
7. domain adaptation constraints:
BERT is a general pre-trained model and requires fine tuning to adapt it to a specific domain. Lack of task-specific data sets may constrain the effectiveness of fine tuning.
These challenges are factors to consider in the use and deployment of BERT, and while BERT performs well on many tasks in NLP, it needs to be tuned to the task and resources. In addition, research and improvements related to BERT are ongoing and solutions to these challenges are being sought.
Strategies for addressing BERT issues
Various countermeasures are being considered by researchers and developers to address BERT challenges. The following are examples of major BERT challenges and corresponding countermeasures.
1. addressing the demand for computational resources:
- Lightweight models: Instead of large BERT models, lightweight model architectures and model compression techniques can be used to reduce computational costs. For more information on lightweight models, see “Overview of Automatic Sentence Generation with Huggingface” which includes several models stored in Huggingface.
- Distributed computing: In order to effectively utilize computational resources, high-performance hardware such as GPUs, TPUs, and cloud computing can be used. See “Parallel and Distributed Processing in Machine Learning” for parallel and distributed processing in machine learning, “Cloud Technology” for the use of cloud computing, and “Hardware in Computing” for acceleration on GPUs and other hardware.
2. dealing with the dependencies of large data sets
- Transfer learning and fine tuning: By fine tuning a pre-trained BERT model to a target task, it is possible to use task-specific datasets. If datasets are insufficient, data augmentation and data synthesis techniques may be used to increase the diversity of the training data. For more information on transfer learning, For details, please refer to “Overview of Transfer Learning, Algorithms, and Examples of Implementations. and for more information on small data, such as data extension and data synthesis techniques, please refer to “Machine Learning Approaches for Small Data and Examples of Various Implementations.
3. dealing with restrictions in handling long sentences:
- Sentence segmentation: It is possible to process long sentences by dividing them into multiple segments and feeding them into the BERT model sequentially. A method that takes into account the relationship between segments is also proposed. For details, please refer to “NLP Processing of Long Sentences by Sentence Segmentation“.
4 Dealing with polysemy:
- Subword segmentation: A word can be segmented into subwords to make it easier to grasp the meaning of a polysemous word. For more information on other ways to deal with polysemy, please refer to “How to deal with polysemy in machine learning“.
5. language generality support:
- Multilingual support for pre-trained models: Pre-trained BERT models have been developed that support multiple languages and can be applied to multilingual tasks. For more information on multilingual support in machine learning, see also “Multilingual Support in Machine Learning.
6. addressing interpretability constraints:
- Combination with interpretable models: BERT predictions can be combined with interpretable models (e.g., LIME, SHAP) to make the model behavior easier to interpret. Attention visualization and other techniques are also being used to attempt to understand the inner workings of the model. See also “Explainable Machine Learning” for machine learning interpretations.
7. addressing domain adaptation constraints:
- Domain-specific fine tuning: By additionally training the BERT model with data appropriate to the target domain, it is possible to improve performance in a particular domain.
Reference Information and Reference Books
For more information on natural language processing in general, see “Natural Language Processing Technology” and “Overview of Natural Language Processing and Examples of Various Implementations.
Reference books include “Natural language processing (NLP): Unleashing the Power of Human Communication through Machine Intelligence“.
“Practical Natural Language Processing: A Comprehensive Guide to Building Real-World NLP Systems“
“Natural Language Processing With Transformers: Building Language Applications With Hugging Face“
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