---
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)
```