Comments (2)
@AM-H Thank you very much for your interest in our research. Which part of the code did you run? The writing part or the link prediction part?
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For paper reading, the data are loaded on LinkPredictionDataset, you can try to include part of the train2id.txt, valid2id.txt. Also, you can reduce the batch size by adding --batch_size 2 after python train.py.
For paper writing, the data are loaded on AssembleMem, you can try to change line 83 and line 86 of New paper writing/loader/preprocessing.py by truncating the data. You can reduce the batch size by adding --batch_size 2 after python train.py --data_path data/pubmed_abstract --model_dp abstract_model/.
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Related Issues (20)
- How 'terms' are related to KB HOT 6
- excellent completed work !! can you share some details about how to produce the dataset,such as entity_text_title_tokenized.json,term.pth in PubMed Paper Reading Dataset ? thanks HOT 36
- Confusingly slow on testing of existing_model_reading model HOT 5
- 处理one-hop节点的代码的一个疑问 HOT 4
- It said that I need to have more RAM?tks HOT 12
- How many epoches do you use in these two tasks? HOT 3
- --
- Model weights sharing and training stopping criterion HOT 3
- Something has been deprecated. HOT 1
- How to generate KGs? HOT 4
- Questions about code and paper_reading dataset HOT 2
- 这个项目现在还更新吗,还有人在使用吗?
- PubTator-MeSH-CTD HOT 1
- METEOR in eval.py HOT 4
- CUDA out of memory HOT 7
- Are new entities formed or just links? HOT 2
- RuntimeError HOT 13
- TypeError: can't convert np.ndarray of type numpy.int32. HOT 18
- TypeError: can't convert np.ndarray of type numpy.int32. HOT 3
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