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Using OpenAI API for Natural Language Processing in Python.png

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As consumers rely more and more on search engines and technical software programs to answer their questions, the demand for effective and scalable natural language processing has gone immensely up. OpenAI provides access to the GPT-3 model, which can perform several operations for NLP-related tasks such as summarization, classification, text completion, text insertion, and more. In this course, you’ll learn about the various endpoints of the OpenAI API and how they can be used to accomplish certain NLP tasks. We'll also look at examples of each endpoint to show how they work. By the time you’re done with this course, you’ll be able to work on your own projects using the OpenAI API.

Using OpenAI API for Natural Language Processing in Python

## Classification

**Classification** is the process of categorizing the data into groups. In this course, we’ll focus on the text classification problem. There are many applications of text classification, including sentiment analysis, natural language inference (NLI), and grammatical verification. We’ll use the completions endpoint of OpenAI to classify the text data, which can be used to label any text without fine-tuning the model. To classify the text, we provide the task description and some optional examples with labels.


The input parameters are the same as we saw in the previous lesson. Let’s see how to provide a prompt to the API for classification:

```text
Classify the Text's label as positive, neutral, or negative.

Text: I loved the new Spiderman movie!
Sentiment:
```

### Examples

Let's try the above prompt in the code widget below:

# Classification

**Classification** is the process of categorizing the data into groups. In this course, we’ll focus on the text classification problem. There are many applications of text classification, including sentiment analysis, natural language inference (NLI), and grammatical verification. We’ll use the completions endpoint of OpenAI to classify the text data, which can be used to label any text without fine-tuning the model. To classify the text, we provide the task description and some optional examples with labels.


The input parameters are the same as we saw in the previous lesson. Let’s see how to provide a prompt to the API for classification:

```text
Classify the Text's label as positive, neutral, or negative.

Text: I loved the new Spiderman movie!
Sentiment:
```

## Examples

Let's try the above prompt in the code widget below:

Learn how to perform classification tasks using the completions endpoint.

Introduction

The Completions Endpoint

Miscellaneous Endpoints

Conclusion

Appendix

Classification

Classification

Examples