What is the BERT Algorithm?

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The BERT algorithm, which stands for Bidirectional Encoder Representations from Transformers, is a method for pre-training natural language processing (NLP) models that has revolutionised the field since its presentation in 2018.

In this article, we take a look at 10 key points to help you better understand this approach and its implications for the development of applications based on the understanding of human language, in particular through Google.


1. The two-way context

Unlike traditional methods, which analyse words in a text in a single direction (left to right or right to left), the BERT algorithm simultaneously takes into account the contexts to the left and right of each word. This provides a richer, more accurate representation of the semantic relationships between words.

What does this mean?

Thanks to this approach, BERT is able to handle complex ambiguities and nuances of meaning that often escape other NLP methods. However, this bidirectional analysis also requires greater computing power and memory capacity, which can make training and using BERT models more costly in terms of time and hardware resources.

2. Transformers and self-learning

BERT is based on a modelling architecture called transformerswhich enables learning not only on the basis of the training data provided, but also on the basis of the semantic relationships within the model itself. In this way, BERT can generate new representations of words based on their global context in the text and gradually improve its performance thanks to this additional information.

3. The Masked Language Model (MLM) learning technique

When training BERT models, the "Masked Language Model consists of randomly masking certain words in the training sentences and asking the model to predict these words on the basis of the other unmasked words in the context. This step helps BERT to develop a detailed understanding of each word and its relationship with the other words in the sentence.

4. Multiple tasks to assess and train BERT

In addition to the MLM technique, BERT is also subjected to other assessment tasks during its training, such as predicting the relationship between two sentences or classifying named entities. This combination of tasks contributes to the model's ability to be generalised and adapted to various NLP applications.

5. A transferable approach for different languages and fields

The BERT algorithm was initially developed for English, but it was found that this approach could be successfully transferred to other languages and fields of knowledge. BERT variants are now available pre-trained on corpora in French, Spanish, Chinese, Russian, etc., as well as on documents specific to sectors such as health or law.

6. Models of different sizes for different needs

BERT models are available in a range of sizes, generally expressed in terms of the number of layers (or 'transformers') and word representation dimensions. These variations in size make it possible toadapt the model to the specific requirements of each applicationWhether the focus is on performance, speed of execution or consumption of hardware resources.

What are the most common sizes of BERT models?

Examples include BERT Base, which has 12 layers and 768 representation dimensions, BERT Large with 24 layers and 1024 dimensions, and BERT-Tiny and BERT-Mini, which offer interesting trade-offs between size and performance for less resource-hungry applications.

7. An Open Source algorithm thanks to Google

The original development of BERT was led by Google AI researchers, who generously contributed published their work under a free and open source licence. This has enabled the scientific community and developers from all over the world to access this revolutionary algorithm, adapt it to their specific needs and contribute to its constant improvement.

8. A wide variety of natural language processing applications

Thanks to its advances in context understanding and generalization, BERT has found numerous applications in the field of NLP, such as :

  • Machine translation
  • Text generation
  • Information and knowledge extraction
  • Document classification
  • Question and answer systems
  • Chatbots and virtual assistants

9. BERT derivatives and extensions for specific use cases

The popularity of BERT and its availability as open source have also given rise to a number of numerous derivatives and extensionsvariants, which seek to improve or adapt the algorithm to specific scenarios. These variants include RoBERTa, ALBERT, SpanBERT, BioBERT, LegalBERT, etc.

10. Future challenges for BERT and its successors

Despite its undeniable successes, BERT still presents challenges and limitations that NLP research is striving to overcome. These challenges include:

  • Reduced energy consumption and hardware resources required to train and run models
  • Improving the robustness and reliability of predictions in the face of new or adverse situations
  • Taking account of wider and deeper cultural and linguistic diversity
  • Understanding and controlling the biases and discriminations potentially contained in the training data and model outputs

In short, the BERT algorithm represents a major advance in the field of natural language processing. Its unique features, such as bidirectional contextual analysis, the use of transformers and the MLM technique, enable it to achieve superior performance on a wide variety of text-related tasks.

However, challenges remain as we continue to improve and develop this promising technology.

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