readme: ---
  license: apache-2.0
  widget:
   - text: "2021\n\n"
  ---
  
  Full code and details at https://github.com/csinva/gpt-paper-title-generator
  
  **Model**
  - finetunes starting from the[gpt-neo-2.7B checkpoint](https://huggingface.co/EleutherAI/gpt-neo-2.7B)
      - for training details see [the training script](https://github.com/csinva/gpt-paper-title-generator/blob/0157f26be9b0763b4ea6480e5b149fdb8dff4626/gptneo/02_finetune_hf.py)
  - inference
      - should prepend with a year and two newlines before querying for a title, e.g. `2022\n\n`
  
  ```python    
  from transformers import AutoModelForCausalLM, pipeline, AutoTokenizer
  model = AutoModelForCausalLM.from_pretrained("csinva/gpt-neo-2.7B-titles")
  tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-2.7B")
  pipe = pipeline('text-generation', model=model, tokenizer=tokenizer)
  pipe('2022\n\n')
  ```
  
  **Data**
  - all [papers on arXiv](https://www.kaggle.com/datasets/Cornell-University/arxiv) in the categories cs.AI, cs.LG, stat.ML
      - date cutoff: only finetuned on papers with dat on or before Apr 1, 2022
      - random 5% of papers also excluded
      - this results in 98,388 papers for finetuning
  - during finetuning each paper title was given starting with the prompt `<year>\n\n <title>\n` (e.g. `2022\n\n Emb-GAM: an Interpretable and Efficient Predictor using Pre-trained Language Models\n`)