readme: ---
  tags:
  - generated_from_trainer
  widget:
    - text: "Sthewillswes emy hedrpi cepl ritie"
    - text: "orel nol hammug antees sopa raus"
    - text: "Gan nstho lanuat tharestlint erks"
    - text: "Jel chatr thefl harewh wh's"
  ---
  
  <!-- 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. -->
  
  # fake-gpt-2-17m
  
  This model is a GPTJ (with 17,637,632 parameters) trained from scratch on a synthetic dataset (1gb of documents created in 4 fake languages, each with a formal and informal writing style) for 1 epoch.
  
  It achieves the following results on the evaluation set:
  - Loss: 3.5592
  
  ## Intended uses & limitations
  
  This model is to be used as a base model for fine-tuning any language/task to probe the effectiveness of both pre-training on an algorithmically generated corpus and effectiveness of extremely small language models (SLMs?). It can only generate text based on its training data (which will be uploaded as a huggingface dataset soon).
  
  ## Training and evaluation data
  
  More information needed
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 0.001
  - batch_size 64
  - seed: 42
  - optimizer: Adam
  - lr_scheduler_type: linear
  - num_epochs: 1
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss |
  |:-------------:|:-----:|:-----:|:---------------:|
  | 3.5175        | 1.0   | 46857 | 3.5592          |
  
  
  ### Framework versions
  
  - Transformers 4.22.1
  - Pytorch 1.12.0
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  