ilyasoulk/AigrisGPT

Model Card for Model ID

Model Details

Model Description

This model is a specialized version of the GPT-2 architecture, fine-tuned for generating negative movie reviews. It aims to produce text reflecting strong dissatisfaction, capturing nuances in negative sentiment and expressing them effectively in generated content.

• Model type: GPT-2 fine-tuned for negative movie reviews
• Language(s) (NLP): English

Uses

from transformers import GPT2LMHeadModel, GPT2Tokenizer

# Specify the model path
model_path = "AigrisGPT"

# Load the model and tokenizer
model = GPT2LMHeadModel.from_pretrained(model_path)
tokenizer = GPT2Tokenizer.from_pretrained(model_path)

input_sequence = "This movie"
max_length = 100

# Encode the input text
input_ids = tokenizer.encode(input_sequence, return_tensors='pt')

# Generate text using the model
output_ids = model.generate(
    input_ids,
    max_length=max_length,
    pad_token_id=model.config.eos_token_id,
    top_k=50,
    top_p=0.95,
    do_sample=True
)

# Decode and print the generated text
generated_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
print(generated_text)

Example of Model Output

Here is an example of text generated by this model with an input This movie:

’This movie tries too hard to be a thriller film and to say there are lots of people like me who like this kind of movies it falls apart at some points. But the thing is this: these people would probably be bored with the genre anyway. All the characters are a mix of stereotypical, racist, violent and sexist stereotypes which are supposed to fit into a mmon genre. One that I found myself thinking about after I watched it. I should have read the books first. If not, I’