readme: ---
  language:
  - en
  tags:
  - text-generation
  - gpt2
  - gpt
  license: mit
  datasets:
  - natural questions
  
  widget:
  - text: "Do you like my new haircut?\nperson beta:\n\n"  
    example_title: "haircut"
  - text: "I love to learn new things.. are you willing to teach me something?\nperson beta:\n\n"  
    example_title: "teaching"
  - text: "What's your favorite animal? Mine is the dog? \nperson beta:\n\n"
    example_title: "favorite"
  - text: "how much does it cost?\nperson beta:\n\n"
    example_title: "money"
  
  inference:
    parameters:
      min_length: 2
      max_length: 64
      length_penalty: 0.6
      no_repeat_ngram_size: 3
      do_sample: True
      top_p: 0.85
      top_k: 10
      repetition_penalty: 2.1
      
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # pszemraj/gpt2-medium-vaguely-human-dialogue
  
  This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on a parsed version of Wizard of Wikipedia. Because the batch size was so large, it learned a general understanding of words that makes sense together but does not specifically respond to anything - sort of like an alien learning to imitate human words to convince others that it is human.
  
  It achieves the following results on the evaluation set:
  - Loss: 4.3281
  
  ## Model description
  
  -  a decent example of what happens when your batch size is too large and the global optima does not reflect specific prompts / use cases.
  
  ## Intended uses & limitations
  
  - there are no intended uses
  
  
  ## Training and evaluation data
  
  - a parsed version of the wizard of Wikipedia dataset
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 2e-05
  - train_batch_size: 32
  - eval_batch_size: 32
  - seed: 42
  - distributed_type: multi-GPU
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 64
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: cosine
  - lr_scheduler_warmup_ratio: 0.05
  - num_epochs: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss |
  |:-------------:|:-----:|:----:|:---------------:|
  | 34.991        | 1.0   | 837  | 14.8359         |
  | 12.2881       | 2.0   | 1674 | 9.375           |
  | 8.5071        | 3.0   | 2511 | 7.2148          |
  | 7.6031        | 4.0   | 3348 | 6.1758          |
  | 6.4808        | 5.0   | 4185 | 5.5820          |
  | 5.8562        | 6.0   | 5022 | 5.0977          |
  | 5.6094        | 7.0   | 5859 | 4.8203          |
  | 5.2591        | 8.0   | 6696 | 4.5977          |
  | 5.0031        | 9.0   | 7533 | 4.4219          |
  | 4.8837        | 10.0  | 8370 | 4.3281          |
  
  
  ### Framework versions
  
  - Transformers 4.16.1
  - Pytorch 1.10.0+cu111
  - Tokenizers 0.11.0
  