Description
Masked Language Modeling (MLM) is a technique where a model predicts a masked word in a sentence. BERT (Bidirectional Encoder Representations from Transformers) utilizes MLM as one of its core tasks. In this use case, you provide input text with certain words masked by
Differences and Unique Aspects of BERT vs. ChatGPT and Others:
Differences between Bert and chatGPT:
BERT: Designed for bidirectional understanding of text. It learns to represent each word based on both its left and right context in the sentence. BERT is pre-trained using tasks like MLM (predicting masked words) and Next Sentence Prediction (NSP).
ChatGPT (e.g., GPT-3): Designed for text generation. It generates coherent and contextually relevant text based on a given prompt. GPT models are autoregressive, meaning they predict one word at a time based on previously generated words.
Use Case Focus:
BERT: Takes input with masked tokens (
ChatGPT: Takes input prompts and generates output text based on its training on vast amounts of text data. It generates text sequentially and aims to produce fluent and contextually appropriate responses.
Masked Language Modeling (MLM) is a technique where a model predicts a masked word in a sentence. BERT (Bidirectional Encoder Representations from Transformers) utilizes MLM as one of its core tasks. In this use case, you provide input text with certain words masked by
[MASK], and BERT predicts the missing words based on the context provided by the rest of the sentence. This approach is valuable for tasks requiring understanding and completion of textual content.Differences and Unique Aspects of BERT vs. ChatGPT and Others:
Differences between Bert and chatGPT:
BERT: Designed for bidirectional understanding of text. It learns to represent each word based on both its left and right context in the sentence. BERT is pre-trained using tasks like MLM (predicting masked words) and Next Sentence Prediction (NSP).
ChatGPT (e.g., GPT-3): Designed for text generation. It generates coherent and contextually relevant text based on a given prompt. GPT models are autoregressive, meaning they predict one word at a time based on previously generated words.
Use Case Focus:
BERT: Takes input with masked tokens (
[MASK]) and outputs predictions for those tokens based on the surrounding context in the sentence. It focuses on understanding and manipulating existing text rather than generating new text.ChatGPT: Takes input prompts and generates output text based on its training on vast amounts of text data. It generates text sequentially and aims to produce fluent and contextually appropriate responses.
This template scrapes the following data properties:
This template uses the following commands & functions:
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