richardwth

Results 11 comments of richardwth

In V3.0.6, this can be solved by changing the default argument `mark_headers=False` to `mark_headers=True` at [extract.Extractor.clearn_text](https://github.com/attardi/wikiextractor/blob/f0ca16c3e92983b9094b6f32526992fc3a678f8f/wikiextractor/extract.py#L948). Then headings start with #, e.g. "## Section 1".

In V3.0.6, this can be solved by changing the default argument `mark_headers=False` to `mark_headers=True` at [extract.Extractor.clearn_text](https://github.com/attardi/wikiextractor/blob/f0ca16c3e92983b9094b6f32526992fc3a678f8f/wikiextractor/extract.py#L948). Then headings start with \#, e.g. "## Section 1".

Sorry for the late reply. If you check line 499 of DeepLearning/my_sngan.py, you may notice the method `eval_sampling` randomly generates image samples and save the generated images to a sprite...

I also noticed this long ago. My fix: ``` from grid2op.Chronics import GridStateFromFileWithForecasts class MyGridStateFromFileWithForecasts(GridStateFromFileWithForecasts): """ This class fixes a bug that the current chronix datatime is fixed to start...

Same here. The error message is ```AttributeError: type object 'LightSimBackend_l2rpn_wcci_2022' has no attribute 'process_grid2op_shunt_data'``` Can be solved by changing `process_grid2op_shunt_data` to `process_shunt_data`.

> can you train and load the agent with rllib ray correctly? No. I overwrite ray's `trainer.save_checkpoint` and `trainer.load_checkpoint` so that they are more user-friendly. Now they output straightforwardly model...

I further checked other parameters...and found four differences `make("l2rpn_wcci_2022").parameters.__dict__` gives ``` 'NB_TIMESTEP_OVERFLOW_ALLOWED': 2, 'NB_TIMESTEP_RECONNECTION': 10, 'NB_TIMESTEP_COOLDOWN_LINE': 0, 'NB_TIMESTEP_COOLDOWN_SUB': 0, ``` `make("../L2RPN_wcci2022_starting_kit/input_data_local").parameters.__dict__` gives ``` 'NB_TIMESTEP_OVERFLOW_ALLOWED': 3, 'NB_TIMESTEP_RECONNECTION': 12, 'NB_TIMESTEP_COOLDOWN_LINE': 3, 'NB_TIMESTEP_COOLDOWN_SUB':...

I came across the same problem every time it needs to prove several points on a circle, i.e., `? cyclic a b c d`. The problem cannot be avoided by...

不好意思,没有这方面的计划。我们提的方法好像也不能直接应用在中文上。

Sorry for this late reply. We used a pretrained RoBERTa-large to calculate the embeddings of all samples, which were then used to calculate the similarities for negative sampling. This step...