---
license: apache-2.0
language:
- en
base_model: openai-community/gpt2
pipeline_tag: text-generation
tags:
- art
---


## Model Card: GPT2_Shakespeare

### Model Description
This model is a fine-tuned version of the GPT-2 base model, fine-tuned on a dataset consisting of works by William Shakespeare to generate text in his tone and style. The model is designed to generate coherent and contextually relevant text, mimicking the unique style and phrasing found in the dataset.

### Model Details
- **Model Type:** GPT-2 ([Base](https://huggingface.co/openai-community/gpt2))
- **Training Dataset:** Works by William Shakespeare [Github](https://gist.githubusercontent.com/blakesanie/dde3a2b7e698f52f389532b4b52bc254/raw/76fe1b5e9efcf0d2afdfd78b0bfaa737ad0a67d3/shakespeare.txt)

- **Intended Use Cases:** 
  - Creative writing assistance
  - Educational purposes for studying literary styles
  - Text generation in the style of William Shakespeare

### Usage
You can easily use this model to generate text in Python using the Hugging Face `transformers` library.

#### Installation
Ensure you have the `transformers` library installed:
```bash
pip install transformers
```
#### Inference

```python
from transformers import GPT2LMHeadModel, GPT2Tokenizer

# Load the fine-tuned model and tokenizer
model_name = "sartajbhuvaji/gpt2_B_Shakespeare"
model = GPT2LMHeadModel.from_pretrained(model_name)
tokenizer = GPT2Tokenizer.from_pretrained(model_name)

# Prepare input text
input_text = "To be, or not to be, that is the question:"
input_ids = tokenizer.encode(input_text, return_tensors="pt")

# Generate text
output = model.generate(
    input_ids,
    max_length=200,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    do_sample=True,
    top_k=50,
    top_p=0.95
)

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

```

#### Limitations and Biases
This model has been trained on a specific dataset, and its responses will reflect the content and style of that dataset.
The model may generate text that reflects the biases present in the original data.
This model is not suitable for generating factual information or for use cases requiring highly accurate and unbiased outputs.

#### Ethical Considerations
Use this model responsibly. The text generated by the model should not be used for misleading or harmful purposes.
Note that this model might reflect historical biases inherent in the original text sources.
Acknowledgments
This model is based on the GPT-2 architecture by OpenAI and has been fine-tuned using the Hugging Face transformers library.