readme: ---
  license: mit
  datasets:
  - DarwinAnim8or/greentext
  language:
  - en
  tags:
  - fun
  - greentext
  widget:
  - text: ">be me"
    example_title: "be me"
  co2_eq_emissions:
    emissions: 60
    source: "https://mlco2.github.io/impact/#compute"
    training_type: "fine-tuning"
    geographical_location: "Oregon, USA"
    hardware_used: "1 T4, Google Colab"
  ---
  
  # GPT-Greentext-355m
  A finetuned version of [GPT2-Medium](https://huggingface.co/gpt2-medium) on the 'greentext' dataset. (Linked above)
  A demo is available [here](https://huggingface.co/spaces/DarwinAnim8or/GPT-Greentext-Playground)
  The demo playground is recommended over the inference box on the right. 
  
  # Training Procedure
  This was trained on the 'greentext' dataset, using the "HappyTransformers" library on Google Colab.
  This model was trained for 15 epochs with learning rate 1e-2.
  
  # Biases & Limitations
  This likely contains the same biases and limitations as the original GPT2 that it is based on, and additionally heavy biases from the greentext dataset.
  It likely will generate offensive output. 
  
  # Intended Use
  This model is meant for fun, nothing else.
  
  # Sample Use
  ```python
  #Import model:
  from happytransformer import HappyGeneration
  happy_gen = HappyGeneration("GPT2", "DarwinAnim8or/GPT-Greentext-355m")
  
  #Set generation settings:
  from happytransformer import GENSettings
  args_top_k = GENSettingsGENSettings(no_repeat_ngram_size=3, do_sample=True, top_k=80, temperature=0.8, max_length=150, early_stopping=False)
  
  #Generate a response:
  result = happy_gen.generate_text(""">be me
  >""", args=args_top_k)
  
  print(result)
  print(result.text)
  ```