input_config.json

{
  "title": "gpt3_engine_settings",

  "gpt3_engine": {
  "engine": "davinci-instruct-beta",
  "temperature": 0,
  "max_tokens": 100,
  "stop_sequence": "###",
  "frequency_penalty": 0,
  "presence_penalty": 0,
  "top_p": 0,
  "n": 1,
  "logprobs": 3
  },
  "notes": "Specify all relevant parameters as shown on OpenAI's playground here",

  "title": "classification_task",
  "classification_task": "valence",
  "notes": "This will be either sentiment or arousal",

  "title": "train_mode",
  "train_mode": {
    "train_or_zeroshot": "train",
    "output_dir": "C:\\Users\\vta\\gpt3playaround\\gpt3playaround",
    "output_filename": "output1.txt",
    "num_lines_per_category": 1,
    "num_lines_per_batch": 5,
    "notes": "If 'train', it is advised to choose engine 'davinci-instruct-beta', in 'prompt_design' provide 'prompt' as first line of prompt, put 'prompt_type' as 'long', in 'zeroshot' provide 'descriptive_prompt' to further give the engine instruction on task and categories",
    "notes": "If 'zeroshot', advised to choose engine davinci or 'cushman-alpha' (if available at time of use), in 'prompt_design' provide 'short' under 'prompt_type', provide 'prompt' in 'prompt_design' with a more descriptive prompt",

    "input_file_to_label": "sm_text_sentiment_training_new.txt",
    "train": {
      "train_input_file": "valence\\train_data\\training_data_fin.txt"
    },
    "zeroshot": {
      "descriptive_prompt": "Given a tweet, classify it into 3 sentiment categories: Positive, Neutral or Negative."
    }
  },

  "title": "prompt_design",
  "prompt_design":{

    "prompt": "Given a tweet, classify it into 3 sentiment categories: Positive, Neutral or Negative.",
    "prompt_type": "short",
    "input_prefix": "Tweet: ",
    "output_prefix": "Output:"
  }
}