Python Transformers sample code

Example 1: Using Transformers for Code Generation

python
# Import required libraries
import torch
from transformers import pipeline

# Load the model
pipe = pipeline("text-generation", model="GuillenLuis03/PyCodeGPT")

# Define the prompt
prompt = "function to reverse a string"

# Generate the code
generated_code = pipe(prompt, max_length=28, temperature=0.7, num_return_sequences=1)['generated_text']

# Print the generated code
print("Generated Python code-->")
print(generated_code)

Example 2: Building a Python Code Generator Using Transformers

python
# Import required libraries
import torch
from transformers import pipeline

# Load the model
pipe = pipeline("text-generation", model="GuillenLuis03/PyCodeGPT")

# Define the prompt
prompt = "short function to reverse a string"

# Generate the code
generated_code = pipe(prompt, max_length=28, temperature=0.7, num_return_sequences=1)['generated_text']

# Print the generated code
print("Generated Python code-->")
print(generated_code)

Example 3: Using Transformers for Code Generation with Custom Data

python
# Import required libraries
import torch
from transformers import pipeline

# Load the model
pipe = pipeline("text-generation", model="GuillenLuis03/PyCodeGPT")

# Define the prompt
prompt = "function to reverse a string"

# Generate the code
generated_code = pipe(prompt, max_length=28, temperature=0.7, num_return_sequences=1)['generated_text']

# Print the generated code
print("Generated Python code-->")
print(generated_code)

Example 4: Using Transformers for Code Generation with Custom Data

python
# Import required libraries
import torch
from transformers import pipeline

# Load the model
pipe = pipeline("text-generation", model="GuillenLuis03/PyCodeGPT")

# Define the prompt
prompt = "function to reverse a string"

# Generate the code
generated_code = pipe(prompt, max_length=28, temperature=0.7, num_return_sequences=1)['generated_text']

# Print the generated code
print("Generated Python code-->")
print(generated_code)

These examples demonstrate how to use Transformers to generate Python code from English problem statements. They involve loading a pre-trained model, defining a prompt, and generating the code using the `pipeline` function from the Hugging Face Transformers library. The generated code can be customized by adjusting parameters like `max_length` and `temperature` to control the complexity and randomness of the output[1][2][3].