Text Generation using Language Models

This repository contains a code snippet that demonstrates text generation using a language model. The code focuses on the concepts of one-shot, few-shot, and zero-shot inferences, showcasing how to create prompts, generate text, and compare the generated output with human-written summaries.

Code Overview

The code snippet is designed to showcase the following concepts:

1. One-shot Inference: This section demonstrates how to create a prompt using a single example and generate text based on that prompt.

2. Few-shot Inference: This section showcases the process of creating prompts using multiple examples and generating text accordingly.

3. Zero-shot Inference: This section illustrates how to generate text without providing any explicit examples in the prompt. Instead, the model generates text based on a general instruction or context.

The dataset used for the examples includes dialogues and summaries, which are used to construct prompts for the language model.

Usage

1. One-shot Inference:

   • Set the example_indices_full and example_index_to_summarize variables to specify the indices of examples to use as prompts.
   • Construct a prompt using the specified example indices using the make_prompt function.
   • Generate text based on the prompt using the language model.
   • Compare the generated output with the human-written summary.

2. Few-shot Inference:

   • Similar to one-shot inference, set example_indices_full and example_index_to_summarize to define examples for prompts.
   • Use the make_prompt function to create a prompt with multiple examples.
   • Generate text using the language model and compare the output with the human-written summary.

3. Zero-shot Inference:

   • Create a prompt without providing specific examples.
   • Generate text based on the provided context or instruction.
   • Examine the generated output to see how the model responds to the context.

Requirements

• Python (>=3.6)
• Transformers library (Hugging Face)