yanaiela / pararel Goto Github PK

Thanks for releasing the code and data. This work is really amazing.
I'm trying to get identical results with Table 3 in the paper (https://arxiv.org/pdf/2102.01017.pdf). I run run_lm_consistent.py with --baseline or Roberta-base models, then average the "lama_acc", "consistency", "lama_group_acc" metrics over relations, the results are:

reproduce with roberta-base

reproduce with --baseline

I place the reproduction results on the left side and the result from the paper on the right side.

I got almost the same "consistency" with Table 3 by averaging over 31 relations. But the "accuracy" metrics are slightly different.
How can I get the same probing results with the paper for the baseline models? Do I miss something in the procedure? Thanks for your help.

Hello! Thanks for releasing the code and data.
When I tried to reproduce the results, I found there are 39 relations here [https://github.com/yanaiela/pararel/tree/main/data/pattern_ data/graphs_json.] while in the article they should be 38.
I wonder if I have miss anything to filter the data and a little confused about which one should be removed.

Meanwhile, I tried to reproduce the results in table 2 and 3. So after filtering, I run run_lm_consistent.py with bert-base-cased as language model. However, the results are slightly different.

Here are the comparison of my result and the original ones represented in your paper.

Do you have any idea about what I may miss in the procedure and what can I do to get the same result?
Thanks for your kindly help.

For example:

{"pattern": "[X] originated from [Y].", "lemma": "originate", "extended_lemma": "originate-from", "tense": "past"}

What's the meaning of each keyword？

Hi~
I'm wondering if the code of Figure 3, t-SNE could be added into the repository?