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Chapter 1: Advanced NLP Applications

1.4 Practical Exercises

This section introduces you to using pretrained transformer models for generating coherent and contextually relevant text. You will work with Hugging Face's Transformers library, focusing on GPT models for text generation.

Exercise 1: Generating Text with GPT-2

Use the pretrained GPT-2 model to generate text based on a custom prompt. This exercise demonstrates how to set up a text generation pipeline using Hugging Face.

Instructions:

Install the transformers library if you haven't already.
Use the GPT-2 model to generate a continuation of the provided prompt.
Experiment with different values for max_length and temperature.

Code Solution:

from transformers import pipeline

# Step 1: Initialize the text generation pipeline
generator = pipeline("text-generation", model="gpt2")

# Step 2: Define a prompt
prompt = "The future of artificial intelligence is"

# Step 3: Generate text
generated_text = generator(prompt, max_length=50, num_return_sequences=1, temperature=0.7)

# Step 4: Print the result
for idx, text in enumerate(generated_text):
    print(f"Generated Text {idx + 1}:\n{text['generated_text']}")

Discussion:

max_length controls the length of the generated text.
temperature adjusts randomness in text generation (lower values produce more deterministic results).

Exercise 2: Fine-Tuned GPT-2 for Domain-Specific Text Generation

In this exercise, you will use a fine-tuned version of GPT-2 (e.g., for generating product descriptions or medical texts). If no fine-tuned model is available, use Hugging Face's Model Hub to explore pretrained versions.

Instructions:

Load a fine-tuned GPT-2 model from Hugging Face.
Generate text related to the chosen domain (e.g., healthcare, marketing).

Code Solution:

from transformers import pipeline

# Load a fine-tuned GPT-2 model from Hugging Face
generator = pipeline("text-generation", model="EleutherAI/gpt-neo-125M")

# Define a domain-specific prompt
prompt = "In modern healthcare, artificial intelligence can be used to"

# Generate domain-specific text
domain_text = generator(prompt, max_length=60, temperature=0.6, num_return_sequences=1)

# Print the result
for idx, text in enumerate(domain_text):
    print(f"Generated Text {idx + 1}:\n{text['generated_text']}")

Discussion:

Explore the Hugging Face Model Hub to find fine-tuned models relevant to your domain.
Experiment with hyperparameters like top_k and top_p for controlling sampling methods.

Exercise 3: Comparing GPT-2 and GPT-4

This exercise involves generating text using GPT-2 and comparing it with GPT-4 via OpenAI's API. Observe the differences in coherence, creativity, and contextual relevance.

Instructions:

Use the OpenAI API to generate text with GPT-4.
Compare the output from GPT-4 with that of GPT-2 for the same prompt.
Evaluate differences in fluency, logical flow, and contextual understanding.

Code Solution (GPT-4 via OpenAI API):

import openai

# OpenAI API key setup
openai.api_key = "your-api-key-here"

# Define a prompt
prompt = "The future of artificial intelligence is"

# Generate text with GPT-4
response = openai.ChatCompletion.create(
    model="gpt-4",
    messages=[
        {"role": "system", "content": "You are an advanced text generation assistant."},
        {"role": "user", "content": prompt}
    ],
    max_tokens=50,
    temperature=0.7
)

# Print GPT-4 response
print(f"GPT-4 Generated Text:\n{response['choices'][0]['message']['content'].strip()}")

Discussion:

GPT-4 offers significant improvements in logical reasoning and contextual understanding compared to GPT-2.
Evaluate how GPT-4 performs in specific tasks, such as maintaining coherence across longer outputs.
Consider the practical implications of GPT-4’s advanced capabilities in terms of application use cases and cost.

1.4 Practical Exercises

Exercise 1: Generating Text with GPT-2

Use the pretrained GPT-2 model to generate text based on a custom prompt. This exercise demonstrates how to set up a text generation pipeline using Hugging Face.

Instructions:

Install the transformers library if you haven't already.
Use the GPT-2 model to generate a continuation of the provided prompt.
Experiment with different values for max_length and temperature.

Code Solution:

from transformers import pipeline

# Step 1: Initialize the text generation pipeline
generator = pipeline("text-generation", model="gpt2")

# Step 2: Define a prompt
prompt = "The future of artificial intelligence is"

# Step 3: Generate text
generated_text = generator(prompt, max_length=50, num_return_sequences=1, temperature=0.7)

# Step 4: Print the result
for idx, text in enumerate(generated_text):
    print(f"Generated Text {idx + 1}:\n{text['generated_text']}")

Discussion:

max_length controls the length of the generated text.
temperature adjusts randomness in text generation (lower values produce more deterministic results).

Exercise 2: Fine-Tuned GPT-2 for Domain-Specific Text Generation

Instructions:

Load a fine-tuned GPT-2 model from Hugging Face.
Generate text related to the chosen domain (e.g., healthcare, marketing).

Code Solution:

from transformers import pipeline

# Load a fine-tuned GPT-2 model from Hugging Face
generator = pipeline("text-generation", model="EleutherAI/gpt-neo-125M")

# Define a domain-specific prompt
prompt = "In modern healthcare, artificial intelligence can be used to"

# Generate domain-specific text
domain_text = generator(prompt, max_length=60, temperature=0.6, num_return_sequences=1)

# Print the result
for idx, text in enumerate(domain_text):
    print(f"Generated Text {idx + 1}:\n{text['generated_text']}")

Discussion:

Explore the Hugging Face Model Hub to find fine-tuned models relevant to your domain.
Experiment with hyperparameters like top_k and top_p for controlling sampling methods.

Exercise 3: Comparing GPT-2 and GPT-4

This exercise involves generating text using GPT-2 and comparing it with GPT-4 via OpenAI's API. Observe the differences in coherence, creativity, and contextual relevance.

Instructions:

Use the OpenAI API to generate text with GPT-4.
Compare the output from GPT-4 with that of GPT-2 for the same prompt.
Evaluate differences in fluency, logical flow, and contextual understanding.

Code Solution (GPT-4 via OpenAI API):

import openai

# OpenAI API key setup
openai.api_key = "your-api-key-here"

# Define a prompt
prompt = "The future of artificial intelligence is"

# Generate text with GPT-4
response = openai.ChatCompletion.create(
    model="gpt-4",
    messages=[
        {"role": "system", "content": "You are an advanced text generation assistant."},
        {"role": "user", "content": prompt}
    ],
    max_tokens=50,
    temperature=0.7
)

# Print GPT-4 response
print(f"GPT-4 Generated Text:\n{response['choices'][0]['message']['content'].strip()}")

Discussion:

GPT-4 offers significant improvements in logical reasoning and contextual understanding compared to GPT-2.
Evaluate how GPT-4 performs in specific tasks, such as maintaining coherence across longer outputs.
Consider the practical implications of GPT-4’s advanced capabilities in terms of application use cases and cost.

1.4 Practical Exercises

Exercise 1: Generating Text with GPT-2

Use the pretrained GPT-2 model to generate text based on a custom prompt. This exercise demonstrates how to set up a text generation pipeline using Hugging Face.

Instructions:

Install the transformers library if you haven't already.
Use the GPT-2 model to generate a continuation of the provided prompt.
Experiment with different values for max_length and temperature.

Code Solution:

from transformers import pipeline

# Step 1: Initialize the text generation pipeline
generator = pipeline("text-generation", model="gpt2")

# Step 2: Define a prompt
prompt = "The future of artificial intelligence is"

# Step 3: Generate text
generated_text = generator(prompt, max_length=50, num_return_sequences=1, temperature=0.7)

# Step 4: Print the result
for idx, text in enumerate(generated_text):
    print(f"Generated Text {idx + 1}:\n{text['generated_text']}")

Discussion:

max_length controls the length of the generated text.
temperature adjusts randomness in text generation (lower values produce more deterministic results).

Exercise 2: Fine-Tuned GPT-2 for Domain-Specific Text Generation

Instructions:

Load a fine-tuned GPT-2 model from Hugging Face.
Generate text related to the chosen domain (e.g., healthcare, marketing).

Code Solution:

from transformers import pipeline

# Load a fine-tuned GPT-2 model from Hugging Face
generator = pipeline("text-generation", model="EleutherAI/gpt-neo-125M")

# Define a domain-specific prompt
prompt = "In modern healthcare, artificial intelligence can be used to"

# Generate domain-specific text
domain_text = generator(prompt, max_length=60, temperature=0.6, num_return_sequences=1)

# Print the result
for idx, text in enumerate(domain_text):
    print(f"Generated Text {idx + 1}:\n{text['generated_text']}")

Discussion:

Explore the Hugging Face Model Hub to find fine-tuned models relevant to your domain.
Experiment with hyperparameters like top_k and top_p for controlling sampling methods.

Exercise 3: Comparing GPT-2 and GPT-4

This exercise involves generating text using GPT-2 and comparing it with GPT-4 via OpenAI's API. Observe the differences in coherence, creativity, and contextual relevance.

Instructions:

Use the OpenAI API to generate text with GPT-4.
Compare the output from GPT-4 with that of GPT-2 for the same prompt.
Evaluate differences in fluency, logical flow, and contextual understanding.

Code Solution (GPT-4 via OpenAI API):

import openai

# OpenAI API key setup
openai.api_key = "your-api-key-here"

# Define a prompt
prompt = "The future of artificial intelligence is"

# Generate text with GPT-4
response = openai.ChatCompletion.create(
    model="gpt-4",
    messages=[
        {"role": "system", "content": "You are an advanced text generation assistant."},
        {"role": "user", "content": prompt}
    ],
    max_tokens=50,
    temperature=0.7
)

# Print GPT-4 response
print(f"GPT-4 Generated Text:\n{response['choices'][0]['message']['content'].strip()}")

Discussion:

GPT-4 offers significant improvements in logical reasoning and contextual understanding compared to GPT-2.
Evaluate how GPT-4 performs in specific tasks, such as maintaining coherence across longer outputs.
Consider the practical implications of GPT-4’s advanced capabilities in terms of application use cases and cost.

1.4 Practical Exercises

Exercise 1: Generating Text with GPT-2

Use the pretrained GPT-2 model to generate text based on a custom prompt. This exercise demonstrates how to set up a text generation pipeline using Hugging Face.

Instructions:

Install the transformers library if you haven't already.
Use the GPT-2 model to generate a continuation of the provided prompt.
Experiment with different values for max_length and temperature.

Code Solution:

from transformers import pipeline

# Step 1: Initialize the text generation pipeline
generator = pipeline("text-generation", model="gpt2")

# Step 2: Define a prompt
prompt = "The future of artificial intelligence is"

# Step 3: Generate text
generated_text = generator(prompt, max_length=50, num_return_sequences=1, temperature=0.7)

# Step 4: Print the result
for idx, text in enumerate(generated_text):
    print(f"Generated Text {idx + 1}:\n{text['generated_text']}")

Discussion:

max_length controls the length of the generated text.
temperature adjusts randomness in text generation (lower values produce more deterministic results).

Exercise 2: Fine-Tuned GPT-2 for Domain-Specific Text Generation

Instructions:

Load a fine-tuned GPT-2 model from Hugging Face.
Generate text related to the chosen domain (e.g., healthcare, marketing).

Code Solution:

from transformers import pipeline

# Load a fine-tuned GPT-2 model from Hugging Face
generator = pipeline("text-generation", model="EleutherAI/gpt-neo-125M")

# Define a domain-specific prompt
prompt = "In modern healthcare, artificial intelligence can be used to"

# Generate domain-specific text
domain_text = generator(prompt, max_length=60, temperature=0.6, num_return_sequences=1)

# Print the result
for idx, text in enumerate(domain_text):
    print(f"Generated Text {idx + 1}:\n{text['generated_text']}")

Discussion:

Explore the Hugging Face Model Hub to find fine-tuned models relevant to your domain.
Experiment with hyperparameters like top_k and top_p for controlling sampling methods.

Exercise 3: Comparing GPT-2 and GPT-4

This exercise involves generating text using GPT-2 and comparing it with GPT-4 via OpenAI's API. Observe the differences in coherence, creativity, and contextual relevance.

Instructions:

Use the OpenAI API to generate text with GPT-4.
Compare the output from GPT-4 with that of GPT-2 for the same prompt.
Evaluate differences in fluency, logical flow, and contextual understanding.

Code Solution (GPT-4 via OpenAI API):

import openai

# OpenAI API key setup
openai.api_key = "your-api-key-here"

# Define a prompt
prompt = "The future of artificial intelligence is"

# Generate text with GPT-4
response = openai.ChatCompletion.create(
    model="gpt-4",
    messages=[
        {"role": "system", "content": "You are an advanced text generation assistant."},
        {"role": "user", "content": prompt}
    ],
    max_tokens=50,
    temperature=0.7
)

# Print GPT-4 response
print(f"GPT-4 Generated Text:\n{response['choices'][0]['message']['content'].strip()}")

Discussion:

GPT-4 offers significant improvements in logical reasoning and contextual understanding compared to GPT-2.
Evaluate how GPT-4 performs in specific tasks, such as maintaining coherence across longer outputs.
Consider the practical implications of GPT-4’s advanced capabilities in terms of application use cases and cost.

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Chapter 1: Advanced NLP Applications

1.4 Practical Exercises

Exercise 1: Generating Text with GPT-2

Exercise 2: Fine-Tuned GPT-2 for Domain-Specific Text Generation

Exercise 3: Comparing GPT-2 and GPT-4

1.4 Practical Exercises

Exercise 1: Generating Text with GPT-2

Exercise 2: Fine-Tuned GPT-2 for Domain-Specific Text Generation

Exercise 3: Comparing GPT-2 and GPT-4

1.4 Practical Exercises

Exercise 1: Generating Text with GPT-2

Exercise 2: Fine-Tuned GPT-2 for Domain-Specific Text Generation

Exercise 3: Comparing GPT-2 and GPT-4

1.4 Practical Exercises

Exercise 1: Generating Text with GPT-2

Exercise 2: Fine-Tuned GPT-2 for Domain-Specific Text Generation

Exercise 3: Comparing GPT-2 and GPT-4