From 97f24303e83dc67756c02111ec08930cd1e05bda Mon Sep 17 00:00:00 2001 From: chrisliu <59010212+chrisliu298@users.noreply.github.com> Date: Mon, 29 Jun 2020 08:34:52 -0700 Subject: [PATCH] Add link to file and fix typos in model card (#5367) * Merge upstream * Merge upstream * Add generate.py link --- model_cards/chrisliu298/arxiv-ai-gpt2/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/model_cards/chrisliu298/arxiv-ai-gpt2/README.md b/model_cards/chrisliu298/arxiv-ai-gpt2/README.md index 22e68a7628..f39b4e5216 100644 --- a/model_cards/chrisliu298/arxiv-ai-gpt2/README.md +++ b/model_cards/chrisliu298/arxiv-ai-gpt2/README.md @@ -12,13 +12,13 @@ datasets: ## Model description -This GPT-2 (774M) model is capable of generating abstracts given paper titles. It was trained using all research papers under aritficial intelligence (AI), machine learning (LG), computation and language (CL), and computer vision and pattern recognition (CV) on arXiv. +This GPT-2 (774M) model is capable of generating abstracts given paper titles. It was trained using all research paper titles and abstracts under artificial intelligence (AI), machine learning (LG), computation and language (CL), and computer vision and pattern recognition (CV) on arXiv. ## Intended uses & limitations #### How to use -To generate paper abstracts, use the provided `generate.py`. This file is very similar to HuggingFace's `run_generation.py` [here](https://github.com/huggingface/transformers/tree/master/examples/text-generation). You can simply replace the text with with your own model path (line 89) and change the input string to your paper title (line 127). +To generate paper abstracts, use the provided `generate.py` [here](https://gist.github.com/chrisliu298/ccb8144888eace069da64ad3e6472d64). This is very similar to the HuggingFace's `run_generation.py` [here](https://github.com/huggingface/transformers/tree/master/examples/text-generation). You can simply replace the text with with your own model path (line 89) and change the input string to your paper title (line 127). ## Training data I selected a subset of the [arXiv Archive](https://github.com/staeiou/arxiv_archive) dataset (Geiger, 2019) as the training and evaluation data to fine-tune GPT-2. The original arXiv Archive dataset contains a full archive of metadata about papers on arxiv.org, from the start of the site in 1993 to the end of 2019. Our subset includes all the paper titles (query) and abstracts (context) under the Artificial Intelligence (cs.AI), Machine Learning (cs.LG), Computation and Language (cs.CL), and Computer Vision and Pattern Recognition (cs.CV) categories. I provide the information of the sub-dataset and the distribution of the training and evaluation dataset as follows.