GPT-DMV-125m
A finetuned version of GPT-Neo-125M on the 'DMV' dataset. (Linked above) A demo is available here
(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
#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)
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