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