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https://api-inference.huggingface.co/models/stevhliu/astroGPT
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stevhliu/astroGPT stevhliu/astroGPT
192 downloads
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pytorch

tf

Contributed by

stevhliu Steven Liu
1 model

How to use this model directly from the πŸ€—/transformers library:

			
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from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("stevhliu/astroGPT") model = AutoModelWithLMHead.from_pretrained("stevhliu/astroGPT")

astroGPT πŸͺ

Model description

This is a GPT-2 model fine-tuned on Western zodiac signs. For more information about GPT-2, take a look at πŸ€— Hugging Face's GPT-2 model card. You can use astroGPT to generate a daily horoscope by entering the current date.

How to use

To use this model, simply enter the current date like so Mon DD, YEAR:

from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("stevhliu/astroGPT")
model = AutoModelWithLMHead.from_pretrained("stevhliu/astroGPT")

input_ids = tokenizer.encode('Sep 03, 2020', return_tensors='pt').to('cuda')

sample_output = model.generate(input_ids,
                               do_sample=True, 
                               max_length=75,
                               top_k=20, 
                               top_p=0.97)

print(sample_output)

Limitations and bias

astroGPT inherits the same biases that affect GPT-2 as a result of training on a lot of non-neutral content on the internet. The model does not currently support zodiac sign-specific generation and only returns a general horoscope. While the generated text may occasionally mention a specific zodiac sign, this is due to how the horoscopes were originally written by it's human authors.

Data

The data was scraped from Horoscope.com and trained on 4.7MB of text. The text was collected from four categories (daily, love, wellness, career) and span from 09/01/19 to 08/01/2020. The archives only store horoscopes dating a year back from the current date.

Training and results

The text was tokenized using the fast GPT-2 BPE tokenizer. It has a vocabulary size of 50,257 and sequence length of 1024 tokens. The model was trained with on one of Google Colaboratory's GPU's for approximately 2.5 hrs with fastai's learning rate finder, discriminative learning rates and 1cycle policy. See table below for a quick summary of the training procedure and results.

dataset size epochs lr training time train_loss valid_loss perplexity
5.9MB 32 slice(1e-7,1e-5) 2.5 hrs 2.657170 2.642387 14.046692