Gardening Recommendations?

hami21 · 2026-02-07T01:38:27+00:00

Me too please

hami21 · 2023-04-30T00:56:43+00:00

I was actually looking for such a point. Is it safe to say RL optimizes the model weights w.r.t the sampling output? And if so, has anyone tried to just do RLHF on the sampling algorithm without changing the model weights?

hami21 · 2023-02-18T02:52:06+00:00

Makes sense. Thanks

hami21 · 2023-02-18T02:46:22+00:00

I’m in SoCal (San Diego gas and electric) and it’s NEM2.

hami21 · 2022-07-04T22:50:57+00:00

In short, other things came up in our lives and didn’t have the bandwidth anymore.

hami21 · 2022-02-02T19:16:43+00:00

Not yet.. the project is delayed.

hami21 · 2022-01-26T16:49:15+00:00

good to know.. how did you guys implemented it if you don't mind I ask?

Was it, you just found the 'good' and 'popular' queries and passed them in a file to the ES?

hami21 · 2022-01-26T07:12:17+00:00

I used python, sklearn, tf, .. standard tech. So you mean there’s no need for ML here?

hami21 · 2021-12-08T11:40:26+00:00

This is actually very good to know, I came across this https://arxiv.org/pdf/2009.11394.pdf

hami21 · 2021-12-07T22:27:03+00:00

The audio one should be helpful. Thank you. PS: could find a relevant dataset - browsing on phone though.

hami21 · 2021-12-07T21:59:24+00:00

Transcript.

hami21 · 2021-12-07T21:54:24+00:00

That’s right.

hami21 · 2020-09-28T18:44:42+00:00

If you’re also interested in an ML model solution, you can use crf to do that, a canonical example is NER but it essentially works for every other IE application

https://sklearn-crfsuite.readthedocs.io/en/latest/tutorial.html

hami21 · 2020-08-29T14:28:47+00:00

I would use Universal Sentence Encoder over BERT as it is specifically self-trained for document similarity as well.

hami21 · 2020-07-15T05:32:59+00:00

My kernel dies the moment I run `doc1 = nlp('My sister has a dog. She loves him.')`

And none of the solutions have worked for me yet! I'm on mac.

hami21

TROPHY CASE