Astrology enthusiasts and data science fans alike will find the GPT2-Horoscopes tool intriguing. This nifty model generates horoscopes based on user-defined categories. In this article, we will walk you through the process of using GPT2-Horoscopes effectively and troubleshoot common issues.

Model Description

The GPT2-Horoscopes model is a fine-tuned version of the GPT2 architecture, specifically designed to generate horoscopes from a large dataset scraped from Horoscopes.com. It can create horoscopes based on various categories, including general, career, love, wellness, and birthday.

How to Use GPT2-Horoscopes

Utilizing the GPT2-Horoscopes model is straightforward, especially when harnessing the HuggingFace transformer pipeline. Here’s a step-by-step guide:

1. Begin by installing the necessary HuggingFace transformers library if you haven’t already.
2. Import the required modules in your Python environment:

from transformers import AutoTokenizer, AutoModelWithLMHead

3. Load the tokenizer and the model using the following commands:

tokenizer = AutoTokenizer.from_pretrained('shahp7575/gpt2-horoscopes')
model = AutoModelWithLMHead.from_pretrained('shahp7575/gpt2-horoscopes')

4. Create an input text prompt in the format of category category_type horoscope. Here are the supported categories: general, career, love, wellness, and birthday.
5. For example, your prompt could look like this:

prompt = 'category career horoscope'

6. Encode the prompt and generate the horoscope using the model:

prompt_encoded = torch.tensor(tokenizer.encode(prompt)).unsqueeze(0)
sample_outputs = model.generate(prompt_encoded,
                                 do_sample=True,
                                 top_k=40,
                                 max_length=300,
                                 top_p=0.95,
                                 temperature=0.95,
                                 num_return_sequences=1)

Training Data

The model is trained on a dataset that consists of approximately 12,000 horoscopes across the five categories mentioned earlier. You can explore this dataset on Kaggle.

Limitations

While the GPT2-Horoscopes model is exciting, it’s essential to remember that it doesn’t aim to produce actual horoscopes. Instead, it’s designed for educational and learning initiatives. Here are some things to keep in mind:

• The model might not always generate coherent or contextually accurate horoscopes.
• It should not be used for serious astrological advice.

Troubleshooting

While using the GPT2-Horoscopes model, you may encounter some challenges. Here are a few troubleshooting tips to help you get back on track:

• If you receive errors while loading the model or tokenizer, ensure your environment has the right version of HuggingFace Transformers installed.
• In case the generated output doesn’t resemble a horoscope, try tweaking the input prompt or parameters like top_k, top_p, and temperature.
• If all else fails, consider checking out the reference generation script available on GitHub for further insights.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

References

• Rey Farhans – Fine-tuning GPT2 Notebook
• Jonathan Bgn – Building a Slogan Generator with GPT-2