Can BERT be used for sentence generating tasks? (2024)

Can BERT be used for sentence generating tasks?

No. Sentence generating is directly related to language modelling (given the previous words in the sentence, what is the next word). Because of bi-directionality of BERT, BERT cannot be used as a language model.

Why BERT is not good for text generation?

Lacking decoders, BERT may not be suitable for text generation. Therefore, the model requires adding extra task-specific architecture to adapt to generative tasks.

Can BERT be used for generation?

BERT can also be used for text generation, but its primary focus is on language understanding rather than text generation.

What are the advantages of BERT?

Advantages Of Using BERT NLP Model Over Other Models

Metrics can be fine-tuned and be used immediately. The accuracy of the model is outstanding because it is frequently updated. You can achieve this with successful fine-tuning training. The BERT model is available and pre-trained in more than 100 languages.

What is the difference between BERT and ChatGPT?

ChatGPT and BERT are applicable AI models used to perform various NLP tasks. Both are built on top of transformers, but ChatGPT uses generative transformers, while BERT uses bidirectional transformers, making them different in their applications.

What are the limitations of Bert language model?

Disadvantages of the BERT Language Model

The BERT Language Model is expensive and requires more computation because of its size. BERT is designed to be the input to other systems, and BERT is fine-tuned for downstream tasks, which are fussy. The model is enormous because of the corpus and the training structure.

What are the pros and cons of BERT?

The advantages of using BERT for question answering include its effectiveness with more training data, while the disadvantages include the need for fine-tuning and its competitiveness with non-pretrained models. Advantages: BERT and RoBERTa achieve impressive performance on many NLP tasks.

What is the difference between BERT and GPT for text generation?

Differences between GPT-3 and BERT

ChatGPT-3 generates text based on the context and is designed for conversational AI and chatbot applications. In contrast, BERT is primarily designed for tasks that require understanding of the meaning and context of words.

Which language model is better than BERT?

GPT wins over BERT for the embedding quality provided by the higher embedding size. However, GPT required a paid API, while BERT is free. In addition, the BERT model is open-source, and not black-box so you can make further analysis to understand it better. The GPT models from OpenAI are black-box.

Is BERT considered generative AI?

machine learning - Bert Used for generative AI - Cross Validated.

What are the downsides of BERT?

Requires vast amounts of training data. Minor support for non-English languages. Fine-tuning can be time-consuming.

Why is BERT good for question answering?

The model takes a passage and a question as input, then returns a segment of the passage that most likely answers the question. It requires semi-complex pre-processing including tokenization and post-processing steps that are described in the BERT paper and implemented in the sample app.

What problems does BERT solve?

BERT effectively addresses ambiguity, which is the greatest challenge to natural language understanding according to research scientists in the field. It is capable of parsing language with a relatively human-like "common sense".

What is the difference between BERT and sentence BERT?

Unlike BERT, SBERT is fine-tuned on sentence pairs using a siamese architecture. We can think of this as having two identical BERTs in parallel that share the exact same network weights. An SBERT model applied to a sentence pair sentence A and sentence B.

Is Google BERT free to use?

BERT is a free and open-source deep learning structure for dealing with Natural Language Processing (NLP).

Is T5 better than BERT?

The main difference between Bert and T5 is in the size of tokens (words) used in prediction. Bert predicts a target composed of a single word (single token masking), on the other hand, T5 can predict multiple words as you see in the figure above. It gives the model flexibility in terms of learning the model structure.

Can BERT take more than 2 sentences?

If you enter more than 2 sentences in a batch, you will get an error. Here is an example for two batches adding the special token manually. As you can see, the manually added special tokens are treated the same when encoding and decoding the sentence.

How many words can BERT handle?

The Problem with BERT

It's proved incredibly useful at a diverse array of tasks, including Q&A and classification. However, BERT can only take input sequences up to 512 tokens in length. This is quite a large limitation, since many common document types are much longer than 512 words.

How is BERT better than LSTM?

1. With the self-attention mechanism, BERT has no locality bias, which means long-distance context has “equal opportunity” to short-distance context. 2. Single multiplication per layer improves efficiency on TPU, which means the effective batch size is the number of words and not sequences.

What learning rate does BERT recommend?

The BERT authors recommend fine-tuning for 4 epochs over the following hyperparameter options: batch sizes: 8, 16, 32, 64, 128. learning rates: 3e-4, 1e-4, 5e-5, 3e-5.

Why BERT is better than other?

Additionally, BERT's bidirectional context enables it to handle ambiguous and complex expressions more effectively than unidirectional models. Furthermore, BERT can adapt to different tasks and domains by fine-tuning its model on specific data sets, thus increasing its flexibility and applicability.

What does BERT actually learn?

In contrast to deep learning neural networks which require very large amounts of data, BERT has already been pre-trained which means that it has learnt the representations of the words and sentences as well as the underlying semantic relations that they are connected with.

Is GPT-4 better than BERT?

GPT is ideal for tasks such as summarization or translation, while BERT is more advantageous for sentiment analysis or NLU. When deciding which model to use, it's essential to consider your specific needs and the task at hand.

Why is BERT better than GPT?

While GPT-3 only considers the left context when making predictions, BERT takes into account both left and right context. This makes BERT better suited for tasks such as sentiment analysis or NLU, where understanding the full context of a sentence or phrase is essential.

What is better than BERT for NLP?

ChatGPT. ChatGPT is an OpenAI language model. It can generate human-like responses to a variety prompts, and has been trained on a wide range of internet texts. ChatGPT can be used to perform natural language processing tasks such as conversation, question answering, and text generation.


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