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docs: update dataset & readme docs #104
docs: update dataset & readme docs #104
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Nice.
@@ -37,6 +25,7 @@ With "implementing" we mean the following: | |||
- Make an issue in this repository that you want to add this dataset (we will label this issue and assign it to you) | |||
- Make a PR that adds in a new folder in `data` | |||
- `meta.yaml` describing the dataset in the form that `transform.py` produces. We will use this later to construct the prompts. | |||
> If your dataset has multiple natural splits (i.e. train, test, validation) you can create a <split>\_meta.yaml for each. |
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I guess this is one way - but I think we handled it differently now in different datasets by adding a split_col
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I think that PR handles the case where datasets have been included in a larger benchmark but my dataset splits are more related to the fact that the dataset itself has natural splits of train, test, validation based on its' usage alone in other papers?
If your dataset is part of a benchmark (here)
If we are specifying the benchmark split in #98 we might also want to add in which benchmark the dataset is a part of so we can remove it from benchmarking? Although there'll be some duplicate information I can't see us handling both cases with the split_col
.
TLDR
split_col
in chore: run pre-commit in CI, rework datasets #98 indicates the benchmarking split if your dataset is part of a benchmark (but we also don't know the benchmark?)<split>_meta.yaml
in docs: update dataset & readme docs #104 indicates a split in the dataset and handles the case where the dataset alone was used in other papers and we want to maintain that original train,test split.
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Thanks, I don't think that they are the same thing. The thing we're perhaps missing at the moment is recording the benchmark in which the dataset has been used. Perhaps my use of "benchmark" has been confusing as many of our tabular datasets so far are part of MoleculeNet or TDC, which are kind of "benchmarks" with leaderboards, but we didn't decide yet if we will also use them to benchmark the models ChemNLP produces (because then we would probably also need to drop the molecules that are in the test in one of the benchmarks from all other training datasets.)
The datasets revised in #98 are all part of TDC, which has been used in papers (hopefully with the train/val/test splits the TDC indicated).
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Yes agreed, so maybe changing split_col
to benchmark_split
and then adding a benchmark_name
field could work?
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I only have a comment about the yaml
s for different splits. Otherwise, great addition, many thanks!
Update installation and dataset creation documentation to reflect discussions from Discord