
# 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)
```
{"language": ["en"], "license": "mit", "tags": ["dmv", "fun"], "datasets": ["DarwinAnim8or/DMV-Plate-Review"], "pipeline_tag": "text-generation", "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"}}
transformers pytorch safetensors gpt_neo text-generation dmv fun en dataset:DarwinAnim8or/DMV-Plate-Review license:mit co2_eq_emissions autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #safetensors #gpt_neo #text-generation #dmv #fun #en #dataset-DarwinAnim8or/DMV-Plate-Review #license-mit #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 


# 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

# 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
TAGS
#transformers #pytorch #safetensors #gpt_neo #text-generation #dmv #fun #en #dataset-DarwinAnim8or/DMV-Plate-Review #license-mit #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # 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
TAGS
#transformers #pytorch #safetensors #gpt_neo #text-generation #dmv #fun #en #dataset-DarwinAnim8or/DMV-Plate-Review #license-mit #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 
# 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