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