Post by kmstfatema on Mar 5, 2024 4:03:43 GMT
I must admit, given my background as a linguist, that Google Bert is perhaps the Google update that has most stimulated my studies in this regard. We can define it as a sort of compromise between the semantic web and the semantics of the language , remembering and underlining that in reality the two things have little in common. This time like never before I had to study a lot because the topic is not at all simple, on the contrary it presents enormous difficulties in understanding. The main problem is that in most online sources you can only find out how Google uses this technology, but not what this technology actually is . I tried to dig deeper, I hope you will appreciate the effort. What exactly does BERT mean? BERT (Bidirectional Encoder Representations from Transformers) is basically a paper published recently by Google .
AI Language researchers. It has made waves in the Germany Telegram Number Data Machine Learning community by presenting cutting-edge results on a wide range of NLP tasks, including question answering (SQuAD v1.1), Natural Language Inference (MNLI), and several other types. BERT's main technical innovation is to apply bidirectional Transformer training, a fairly well-known model for understanding, to language modeling. This is in contrast to previous efforts that have looked at a left-to-right sequence of text or combined left-to-right and right-to-left alignment. The paper's findings show that a bidirectionally set language model can have a deeper sense of context and language flow than single-direction language models. In the paper, the researchers detail a new technique called Masked LM (MLM) that enables bidirectional alignment in models where it was previously impossible.
Its researchers have developed some computer processes that manage words in a sentence by relating them to each other , instead of managing them individually as was the case until now. How does Bert work? BERT makes use of Transformer, an attention mechanism that learns the contextual relationships between words (or sub-words) in a text. In its original form, Transformer includes two separate mechanisms: an encoder that reads the text input and a decoder that produces a prediction for the task . Since the goal of BERT is to generate a language model, only the encoder mechanism is needed. The detailed operation of Transformer is described in a Google doc . Unlike directional models, which read entered text sequentially (left to right or right to left), the Transformer encoder reads the entire sequence of words at once. Therefore it is considered bidirectional, although it would be more accurate to say that it is non-directional.
AI Language researchers. It has made waves in the Germany Telegram Number Data Machine Learning community by presenting cutting-edge results on a wide range of NLP tasks, including question answering (SQuAD v1.1), Natural Language Inference (MNLI), and several other types. BERT's main technical innovation is to apply bidirectional Transformer training, a fairly well-known model for understanding, to language modeling. This is in contrast to previous efforts that have looked at a left-to-right sequence of text or combined left-to-right and right-to-left alignment. The paper's findings show that a bidirectionally set language model can have a deeper sense of context and language flow than single-direction language models. In the paper, the researchers detail a new technique called Masked LM (MLM) that enables bidirectional alignment in models where it was previously impossible.
Its researchers have developed some computer processes that manage words in a sentence by relating them to each other , instead of managing them individually as was the case until now. How does Bert work? BERT makes use of Transformer, an attention mechanism that learns the contextual relationships between words (or sub-words) in a text. In its original form, Transformer includes two separate mechanisms: an encoder that reads the text input and a decoder that produces a prediction for the task . Since the goal of BERT is to generate a language model, only the encoder mechanism is needed. The detailed operation of Transformer is described in a Google doc . Unlike directional models, which read entered text sequentially (left to right or right to left), the Transformer encoder reads the entire sequence of words at once. Therefore it is considered bidirectional, although it would be more accurate to say that it is non-directional.