BERT (Bidirectional Encoder Representations from Transformers) is a transformer-based neural network model introduced by Google in 2018. It is designed to improve the understanding of natural language in various language processing tasks, such as question answering, sentiment analysis, named entity recognition, and more.
BERT differs from previous language models in its ability to capture the context of a word by considering both the left and right context in a sentence. Traditional language models, like the ones based on recurrent neural networks, process text in a sequential manner, making it difficult to capture the full context.
BERT, on the other hand, is a "pre-trained" model that is trained on a large corpus of unlabeled text data. During pre-training, BERT learns to predict missing words in sentences by considering the surrounding words on both sides. This bidirectional training allows BERT to capture contextual information effectively.
Once pre-training is complete, BERT is fine-tuned on specific downstream tasks. This fine-tuning involves training the model on labeled data from a particular task, such as sentiment analysis or named entity recognition. During fine-tuning, BERT adapts its pre-trained knowledge to the specific task, further improving its understanding and performance.
The key advantages of BERT include:
Contextual understanding: BERT can capture the contextual meaning of words by considering both the preceding and following words in a sentence, leading to better language understanding.
Transfer learning: BERT is pre-trained on a large corpus of unlabeled data, enabling it to learn general language representations. These pre-trained representations can then be fine-tuned for specific tasks, even with limited labeled data.
Versatility: BERT can be applied to a wide range of natural language processing tasks. By fine-tuning the model on specific tasks, it can achieve state-of-the-art performance in tasks such as question answering, text classification, and more.
Handling ambiguity: BERT's bidirectional nature helps it handle ambiguous language constructs more effectively. It can make more informed predictions by considering the context from both directions. 0_ViwaI3Vvbnd-CJSQ
