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Converted PLSC to hierarchical
Checklist for adding MMTEB dataset
Reason for dataset addition: Converted both PLSC tasks (S2S, P2P) to hierarchical clustering. #702
- [x] I have tested that the dataset runs with the
mteb
package. - [x] I have run the following models on the task (adding the results to the pr). These can be run using the
mteb run -m {model_name} -t {task_name}
command.- [x]
sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
- [x]
intfloat/multilingual-e5-small
- [x]
- [x] I have checked that the performance is neither trivial (both models gain close to perfect scores) nor random (both models gain close to random scores).
- [x] If the dataset is too big (e.g. >2048 examples), considering using
self.stratified_subsampling() under dataset_transform()
- [x] I have filled out the metadata object in the dataset file (find documentation on it here).
- [x] Run tests locally to make sure nothing is broken using
make test
. - [x] Run the formatter to format the code using
make lint
. - [ ] I have added points for my submission to the points folder using the PR number as the filename (e.g.
438.jsonl
).
The later levels seem very hard. Maybe we should limit the levels to two?
I'm not sure whether the way I formulated the task makes sense. @rafalposwiata You added the dataset initially, therefore you might know: Is the "disciplines" column hierarchically ordered or just multilabel? Or could "scientific_fields" be used as the first level and "disciplines" as the second? What's your take on this?
Is the "disciplines" column hierarchically ordered or just multilabel?
Disciplines are multilabel but for the added clustering tasks I chose only those cases where there is one discipline.
Or could "scientific_fields" be used as the first level and "disciplines" as the second?
Yes, "scientific_fields" could be used as the first level and "disciplines" as the second.
The entire dataset is available at https://huggingface.co/datasets/rafalposwiata/plsc
@x-tabdeveloping will you add points for this then I believe it is ready to merge
I'm not sure though. The task formulation might be wrong. I think doing "scientific_fields" as first level and "disciplines" as the second might be the way to go. From what I've gathered it seems that this is just multilabel, not hierarchical the way I formulated it, right @rafalposwiata ?
@x-tabdeveloping but the current approach is fine with that right? As I understand it is just does the clustering at each level?
Yes, unless the order is not fixed, and I don't know if it is (we have to check)
Right. Once checked we can either close or merge
Nope, it's not hierarchical at all. We can maybe rephrase it as multilabel classification if we really want to, otherwise fine to leave it as flat clustering.
Let us leave it as flat clustering