내 텍스트 분류를 위해 GPT 3를 어떻게 사용할 수 있나요?

저는 텍스트 분류 문제에서 OpenAI GPT-3을 전이 학습에 사용할 수 있는지 궁금합니다. 그렇다면, Tensorflow, Keras를 사용하여 어떻게 시작할 수 있을까요?

답변 1

(다음 샘플에서 미움말은 ********로 대체했습니다)

1. I cannot stand ******** people who spread lies.

나는 거짓말을 퍼뜨리는 ******** 사람을 참을 수 없어.

2. It is unacceptable for anyone to use ******** language.

누구든 ******** 언어를 사용하는 것은 용납할 수 없다.

3. I don't want to be friends with ******** individuals who are disrespectful.

나는 무례한 ******** 개인들과 친구가 되고 싶지 않다.

4. Racism has no place in society, and we need to speak out against ******** behavior.

인종차별은 사회에서 자리 잡을 자격이 없으며, 우리는 ******** 행동에 대해 발언해야 한다.

주어진 샘플 예시:

(You look like ****** *** to me *******, true)

(**** you *********, true)

(**** my ****, true)

(hey my name is John can you help me?, false)

(hey my name is John, i think you ****** ***!, true)

(i have a problem with my network driver hpz-3332d, false)

GPT-3는 실제로 주어진 입력이 혐오적인지 여부를 결정할 수 있습니다. GPT-3는 임의의 댓글이 혐오적인지 아닌지를 매우 효과적으로 판단하는 필터를 구현하고 있습니다. 당신은 그저 메시지를 입력하고 GPT3이 끝에 있는 , true|false) 부분을 자동으로 완성하게끔 설정을 한 후, 토큰을 약 6개로 설정하고 온도를 90%로 설정하기만 하면 됩니다.

GPT3를 사용하여 더 복잡한 문맥을 고려하는 부울식 분류(욕하지 않고도 누군가를 모욕할 수 있음)도 가능하며, GPT2에서도 수행할 수 있습니다.

답변 2

GPT-3 (Generative Pre-trained Transformer 3) is a state-of-the-art language model that has become widely popular due to its ability to perform a variety of natural language processing tasks, including text classification. In this article, we will discuss how GPT-3 can be used for text classification and its benefits.

Text classification refers to the process of categorizing text into different classes or categories based on its content. It is a commonly used technique in natural language processing, where it is used for a range of applications, such as sentiment analysis, spam detection, and topic modeling. Text classification can be a time-consuming task, especially when dealing with large amounts of data. However, with the advent of GPT-3, the process has become more accessible and less time-consuming.

GPT-3 uses an unsupervised learning approach to understand the underlying patterns and context that exist within a given text. This means that the model can automatically classify text without being trained on a specific dataset. It can learn from the vast amount of text that has been fed into it, making it highly adaptable to any new data that it encounters.

To use GPT-3 for text classification, one must first train the model to recognize the different categories of text. This involves providing the model with a dataset that is labeled with the correct categories. The model will then use this dataset to learn the various features associated with each category, allowing it to classify new text accurately.

The benefits of using GPT-3 for text classification are numerous. Firstly, it allows users to classify large amounts of text within a short period, saving time and effort. Secondly, the model is highly accurate, as it has been trained on vast amounts of data, which makes it suitable for a range of applications. Finally, GPT-3 can be easily integrated into existing natural language processing workflows, making it accessible for many users.

In conclusion, using GPT-3 for text classification is a powerful tool for natural language processing. It has the ability to classify large amounts of text accurately, quickly and with very little human intervention. It could be used for a range of applications, including sentiment analysis, spam detection, and topic modeling, making it highly adaptable and suitable for a range of businesses.