readme: ---
  license: mit
  datasets:
  - DarwinAnim8or/DMV-Plate-Review
  language:
  - en
  pipeline_tag: text-generation
  tags:
  - dmv
  - fun
  widget:
  - text: "PLATE: LCDR"
    example_title: "Plate LCDR"
  - text: "PLATE: LUCH"
    example_title: "Plate LUCH"
  - text: "PLATE: JJ BINKS"
    example_title: "Plate JJ BINKS"
  co2_eq_emissions:
    emissions: 20
    source: "https://mlco2.github.io/impact/#compute"
    training_type: "fine-tuning"
    geographical_location: "Oregon, USA"
    hardware_used: "1 T4, Google Colab"
  ---
  
  # GPT-DMV-125m
  A finetuned version of [GPT-Neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125M) on the 'DMV' dataset. (Linked above)
  A demo is available [here](https://huggingface.co/spaces/DarwinAnim8or/GPT-DMV-Playground)
  
  (I recommend using the demo playground rather than the Inference window on the right here)
  
  # Training Procedure
  This was trained on the 'DMV' dataset, using the "HappyTransformers" library on Google Colab.
  This model was trained for 5 epochs with learning rate 1e-2.
  
  # Biases & Limitations
  This likely contains the same biases and limitations as the original GPT-Neo-125M that it is based on, and additionally heavy biases from the DMV dataset.
  
  # Intended Use
  This model is meant for fun, nothing else.
  
  # Sample Use
  ```python
  #Import model:
  from happytransformer import HappyGeneration
  happy_gen = HappyGeneration("GPT-NEO", "DarwinAnim8or/GPT-DMV-125m")
  
  #Set generation settings:
  from happytransformer import GENSettings
  args_top_k = GENSettings(no_repeat_ngram_size=3, do_sample=True,top_k=80, temperature=0.4, max_length=50, early_stopping=False)
  
  #Generate a response:
  result = happy_gen.generate_text("""PLATE: LUCH
  REVIEW REASON CODE: """, args=args_top_k)
  
  print(result)
  print(result.text)
  ```