jzbjyb / lpaqa Goto Github PK

Hi, neither in the paper nor in the readme, is it specified, corresponding to which LM were those weights trained on. By that I mean, do I have to use those weights with the BERT-base or BERT-large? Moreover are they cased on uncased models?

I am interested in the code to mine prompts automatically, can you share them? Or recommend some others' code do this insteadly? thanks

if this error occur, just install the right overrides version:

pip install overrides==3.1.0

This works.

Hi, thanks for sharing the code!
I can see templates by manual_paraphrase, but I'm don't know which one is manual or paraphrased. Could you also share the manual templates only?
Thanks

from knowledge_bert import BertTokenizer, BertForMaskedLM, BasicTokenizer
from knowledge_bert.tokenization import whitespace_tokenize_ent

"knowledge_bert" is not in the https://github.com/facebookresearch/LAMA/tree/main/lama/modules. How can I get them?

Thanks!

I'm trying to run the experiments (run_exp.sh) and I'm running into a problem with the rel_file. I assume it's one of the mine/paraphrase prompt files so for the --rel_file parameter I put something like "prompts/mine/P19.jsonl" but then I get an error saying the key 'relation' is expected. Do I need to add a relations key like in get_test_phrase_parameters?
i.e. relations = [{"relation": "P108", "template": ["[X] works for [Y] .", "[Y] commentator [X] ."]}]

More specifically, how do I recreate the micro/macro averaged accuracies of Table 2 and 3 in the paper?