This model is part of the Deepstory project, which combines Natural Language Generation, Text-to-Speech, and animation technologies to create interactive storytelling experiences.

The model has been fine-tuned on the complete text of "Waiting for Godot" to capture the distinctive absurdist dialogue style, circular conversations, and existential themes of Beckett's masterpiece.

Model Architecture

This model is based on GPT-2 Medium architecture.

Parameter	Value
Architecture	GPT2LMHeadModel
Model Size	GPT-2 Medium
Number of Layers (n_layer)	24
Hidden Size (n_embd)	1024
Attention Heads (n_head)	16
Context Length (n_ctx)	1024
Max Positions (n_positions)	1024
Vocabulary Size	50,257
Activation Function	GELU (new)
Attention Dropout	0.1
Embedding Dropout	0.1
Residual Dropout	0.1
Layer Norm Epsilon	1e-05

Usage

With Transformers Library

python

from transformers import GPT2Tokenizer, GPT2LMHeadModel
import torch

# Load model and tokenizer
tokenizer = GPT2Tokenizer.from_pretrained("thetobysiu/gpt2-waiting-for-godot")
model = GPT2LMHeadModel.from_pretrained("thetobysiu/gpt2-waiting-for-godot")

# Generate dialogue
prompt = "VLADIMIR: What do we do now?\nESTRAGON:"
input_ids = tokenizer.encode(prompt, return_tensors='pt')

output = model.generate(
    input_ids=input_ids,
    max_length=200,
    temperature=0.9,
    top_p=0.95,
    top_k=50,
    do_sample=True,
    num_return_sequences=1
)

generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Generation Parameters

For best results when generating Beckett-style dialogue:

Parameter	Recommended Value	Description
temperature	0.8 - 1.0	Higher for more absurd outputs
top_p	0.9 - 0.95	Nucleus sampling threshold
top_k	40 - 50	Top-k sampling
max_length	100 - 300	Maximum tokens to generate
do_sample	True	Enable sampling

Character Format

The model works best when prompted with the character name format used in the play:

VLADIMIR: or DIDI:
ESTRAGON: or GOGO:
POZZO:
LUCKY:
BOY:

Training Data

The model was fine-tuned on the complete text of "Waiting for Godot" (En attendant Godot) by Samuel Beckett, including both Act I and Act II.

Training Procedure

The model was fine-tuned using the Hugging Face Transformers library starting from the pre-trained GPT-2 Medium checkpoint.

Intended Use

This model is intended for:

Experimental theater and creative writing
Generating absurdist dialogue
Exploring AI-generated dramatic text
Research in language models and literary style
Educational purposes in studying Beckett's style

Characteristics of Generated Text

The model tends to generate text with:

Circular, repetitive dialogue patterns
Existential themes and philosophical musings
Short, punchy exchanges between characters
References to waiting, time, and uncertainty
The distinctive rhythm of Beckett's prose

Limitations

The model is trained on a relatively small corpus (single play)
Generated dialogue may become repetitive
Context length is limited to 1024 tokens
May not capture all nuances of Beckett's theatrical timing
The training data is based on copyrighted material

Ethical Considerations

This model is trained on copyrighted material and should be used for personal/research purposes
Generated content should not be presented as original work by Samuel Beckett
The model may generate text touching on existential and nihilistic themes

Citation

If you use this model, please cite the Deepstory project:

bibtex

@misc{deepstory,
  author = {Siu King Wai},
  title = {Deepstory},
  year = {2020},
  publisher = {GitHub},
  url = {https://github.com/thetobysiu/deepstory}
}

License

This model is released under the MIT License. Please note that "Waiting for Godot" is the intellectual property of the Samuel Beckett estate.

Acknowledgments

OpenAI for the original GPT-2 model
Hugging Face for the Transformers library
Samuel Beckett for the original play "Waiting for Godot"