ojus1 / date2vec Goto Github PK

I'm interested to use this repo to my project but I'm still new to this. How do I import the Date2VecConvert class to my notebook?

I can't install this repo as package because it has no setuptools for pip to install.

Running the example on the read me returns a 2,32 embedding. You get the correct result if you change to call function to the following (removing the unsqueeze):

        with torch.no_grad():
            return self.model.encode(torch.Tensor(x)).squeeze(0).cpu()

Hello Surya,

Firstly, thank you for the nice job.
I am trying uderstand the usefulness of time2vec and if I can use it in some way in my use case. In general time2vec paper claims that "...developing a general-purpose model-agnostic representation for time that can be potentially used in any architecture. In particular, we develop a learnable vector representation (or embedding) for time as a vector representation can be easily combined with many models or architectures..."

In my case, I have 12 values for each new acquisition and I have a new acquisition every 3 to 5 days. This acquisition date I want instead of use it as a simple column feature (i.e. doy of year) to transform it using time2vec in a continuous embedding.

But, what is the proper way to create this useful time represenation for me? You provide some pretrained date2vec representation (based on time2vec approach), and I am thinking that is a kind of general time representation trained in big dataset likes pretrained word2vec (by google) in english wikipedia corpus. Is it true? In what dataset and "pretext" task you have pretrain? Are these time representations general enough?

Also, if the better is to train my own time2vec embeddings in my own dataset, how I will do it? I will train a NN with 1 time2vec layer + 1 lstm layer with task let's say to predict one of the 12 values per acquisition and in the end I will keep the time2vec layer as the time embedding?

Sorry for bombarding with questions.

BR,
Ilias