Google BERT (Bidirectional Encoder Representations from Transformers) is a pre-trained natural language processing model that utilizes deep learning techniques to understand the meaning of words and sentences in a more human-like manner.
BERT uses a neural network architecture known as transformers, which allows it to consider the context of a word within a sentence, rather than just looking at individual words in isolation. This helps it to better understand the nuances and complexities of natural language.
One of the key features of BERT is that it is pre-trained on massive amounts of text data, which enables it to learn the relationships between words and sentences in a more general way. This pre-training allows BERT to be used for a wide variety of natural language processing tasks, such as sentiment analysis, question answering, and language translation.
When BERT is used for a specific task, such as sentiment analysis, it is fine-tuned on a smaller dataset of labeled examples related to that task. This fine-tuning process allows BERT to adapt to the specific language and terminology used in the task at hand, and to achieve high accuracy in its predictions.
Overall, BERT is used because it is a powerful tool for natural language processing tasks. Its ability to understand the context of words and sentences, coupled with its pre-training on massive amounts of text data, has made it a go-to solution for many organizations looking to analyze or manipulate natural language data.