We seem to have some consensus that for the upcoming learning to rank work we will build out a python library to handle the bulk of the backend data plumbing work. The library will primarily be code integrating with pyspark to do various pieces such as:
# Sampling from the click logs to generate the set of queries + page's that will be labeled with click models # Distributing the work of running click models against those sampled data sets # Pushing queries we use for feature generation into kafka, and reading back the resulting feature vectors (the other end of this will run those generated queries against either the hot-spare elasticsearch cluster or the relforge cluster to get feature scores) # Merging feature vectors with labeled data, splitting into test/train/validate sets, and writing out files formatted for whichever training library we decide on (xgboost, lightgbm and ranklib are in the running currently) # Whatever plumbing is necessary to run the actual model training and do hyper parameter optimization # Converting the resulting models into a format suitable for use with the elasticsearch learn to rank plugin # Reporting on the quality of models vs some baseline
The high level goal is that we would have relatively simple python scripts in our analytics repository that are called from oozie, those scripts would know the appropriate locations to load/store data and pass into this library for the bulk of the processing. There will also be some script, probably within the library, that combines many of these steps for feature engineering purposes to take some set of features and run the whole thing.
So, what do we call this thing? Horrible first attempts:
* ltr-pipeline * learn-to-rank-pipeline * bob * cirrussearch-ltr * ???
You can't call it Bob for historical reasons https://en.wikipedia.org/wiki/Microsoft_Bob! I don't think cirrussearch-ltr is too bad. (Though "LTR" always makes me think we're neglecting RTL languages somehow.)
Trey Jones Software Engineer, Discovery Wikimedia Foundation
On Wed, Apr 5, 2017 at 3:28 PM, Erik Bernhardson <ebernhardson@wikimedia.org
wrote:
We seem to have some consensus that for the upcoming learning to rank work we will build out a python library to handle the bulk of the backend data plumbing work. The library will primarily be code integrating with pyspark to do various pieces such as:
# Sampling from the click logs to generate the set of queries + page's that will be labeled with click models # Distributing the work of running click models against those sampled data sets # Pushing queries we use for feature generation into kafka, and reading back the resulting feature vectors (the other end of this will run those generated queries against either the hot-spare elasticsearch cluster or the relforge cluster to get feature scores) # Merging feature vectors with labeled data, splitting into test/train/validate sets, and writing out files formatted for whichever training library we decide on (xgboost, lightgbm and ranklib are in the running currently) # Whatever plumbing is necessary to run the actual model training and do hyper parameter optimization # Converting the resulting models into a format suitable for use with the elasticsearch learn to rank plugin # Reporting on the quality of models vs some baseline
The high level goal is that we would have relatively simple python scripts in our analytics repository that are called from oozie, those scripts would know the appropriate locations to load/store data and pass into this library for the bulk of the processing. There will also be some script, probably within the library, that combines many of these steps for feature engineering purposes to take some set of features and run the whole thing.
So, what do we call this thing? Horrible first attempts:
- ltr-pipeline
- learn-to-rank-pipeline
- bob
- cirrussearch-ltr
- ???
discovery mailing list discovery@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/discovery
Build-A-Rank Workshop?
Rank-Pipe?
Omg-look-at-that-great-search-result-page-pipeline? (OLAT-GrSERPP)
On Wed, Apr 5, 2017 at 12:33 PM, Trey Jones tjones@wikimedia.org wrote:
You can't call it Bob for historical reasons https://en.wikipedia.org/wiki/Microsoft_Bob! I don't think cirrussearch-ltr is too bad. (Though "LTR" always makes me think we're neglecting RTL languages somehow.)
Trey Jones Software Engineer, Discovery Wikimedia Foundation
On Wed, Apr 5, 2017 at 3:28 PM, Erik Bernhardson < ebernhardson@wikimedia.org> wrote:
We seem to have some consensus that for the upcoming learning to rank work we will build out a python library to handle the bulk of the backend data plumbing work. The library will primarily be code integrating with pyspark to do various pieces such as:
# Sampling from the click logs to generate the set of queries + page's that will be labeled with click models # Distributing the work of running click models against those sampled data sets # Pushing queries we use for feature generation into kafka, and reading back the resulting feature vectors (the other end of this will run those generated queries against either the hot-spare elasticsearch cluster or the relforge cluster to get feature scores) # Merging feature vectors with labeled data, splitting into test/train/validate sets, and writing out files formatted for whichever training library we decide on (xgboost, lightgbm and ranklib are in the running currently) # Whatever plumbing is necessary to run the actual model training and do hyper parameter optimization # Converting the resulting models into a format suitable for use with the elasticsearch learn to rank plugin # Reporting on the quality of models vs some baseline
The high level goal is that we would have relatively simple python scripts in our analytics repository that are called from oozie, those scripts would know the appropriate locations to load/store data and pass into this library for the bulk of the processing. There will also be some script, probably within the library, that combines many of these steps for feature engineering purposes to take some set of features and run the whole thing.
So, what do we call this thing? Horrible first attempts:
- ltr-pipeline
- learn-to-rank-pipeline
- bob
- cirrussearch-ltr
- ???
discovery mailing list discovery@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/discovery
discovery mailing list discovery@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/discovery
Hi!
So, what do we call this thing? Horrible first attempts:
- ltr-pipeline
- learn-to-rank-pipeline
- bob
- cirrussearch-ltr
- ???
rank forge? ML ranking? sorting hat? :)
LTR is kind of confusing - for me it's the opposite of RTL which is handling Hebrew & Arabic :)
SnakePipe - get it? Python and 'plumbing'?
Yours, Chris Koerner Community Liaison - Discovery Wikimedia Foundation
On Wed, Apr 5, 2017 at 2:28 PM, Erik Bernhardson <ebernhardson@wikimedia.org
wrote:
We seem to have some consensus that for the upcoming learning to rank work we will build out a python library to handle the bulk of the backend data plumbing work. The library will primarily be code integrating with pyspark to do various pieces such as:
# Sampling from the click logs to generate the set of queries + page's that will be labeled with click models # Distributing the work of running click models against those sampled data sets # Pushing queries we use for feature generation into kafka, and reading back the resulting feature vectors (the other end of this will run those generated queries against either the hot-spare elasticsearch cluster or the relforge cluster to get feature scores) # Merging feature vectors with labeled data, splitting into test/train/validate sets, and writing out files formatted for whichever training library we decide on (xgboost, lightgbm and ranklib are in the running currently) # Whatever plumbing is necessary to run the actual model training and do hyper parameter optimization # Converting the resulting models into a format suitable for use with the elasticsearch learn to rank plugin # Reporting on the quality of models vs some baseline
The high level goal is that we would have relatively simple python scripts in our analytics repository that are called from oozie, those scripts would know the appropriate locations to load/store data and pass into this library for the bulk of the processing. There will also be some script, probably within the library, that combines many of these steps for feature engineering purposes to take some set of features and run the whole thing.
So, what do we call this thing? Horrible first attempts:
- ltr-pipeline
- learn-to-rank-pipeline
- bob
- cirrussearch-ltr
- ???
discovery mailing list discovery@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/discovery
How about Horace? I heard that it's a name that isn't being used much anymore by parents naming their kids. It could be:
Horace Learns to Rank
joking, but not really :)
-- deb tankersley irc: debt Product Manager, Discovery Wikimedia Foundation
On Wed, Apr 5, 2017 at 2:53 PM, Chris Koerner ckoerner@wikimedia.org wrote:
SnakePipe - get it? Python and 'plumbing'?
Yours, Chris Koerner Community Liaison - Discovery Wikimedia Foundation
On Wed, Apr 5, 2017 at 2:28 PM, Erik Bernhardson < ebernhardson@wikimedia.org> wrote:
We seem to have some consensus that for the upcoming learning to rank work we will build out a python library to handle the bulk of the backend data plumbing work. The library will primarily be code integrating with pyspark to do various pieces such as:
# Sampling from the click logs to generate the set of queries + page's that will be labeled with click models # Distributing the work of running click models against those sampled data sets # Pushing queries we use for feature generation into kafka, and reading back the resulting feature vectors (the other end of this will run those generated queries against either the hot-spare elasticsearch cluster or the relforge cluster to get feature scores) # Merging feature vectors with labeled data, splitting into test/train/validate sets, and writing out files formatted for whichever training library we decide on (xgboost, lightgbm and ranklib are in the running currently) # Whatever plumbing is necessary to run the actual model training and do hyper parameter optimization # Converting the resulting models into a format suitable for use with the elasticsearch learn to rank plugin # Reporting on the quality of models vs some baseline
The high level goal is that we would have relatively simple python scripts in our analytics repository that are called from oozie, those scripts would know the appropriate locations to load/store data and pass into this library for the bulk of the processing. There will also be some script, probably within the library, that combines many of these steps for feature engineering purposes to take some set of features and run the whole thing.
So, what do we call this thing? Horrible first attempts:
- ltr-pipeline
- learn-to-rank-pipeline
- bob
- cirrussearch-ltr
- ???
discovery mailing list discovery@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/discovery
discovery mailing list discovery@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/discovery
Hi!
btw I looked around and there's this wikipedia site that says the right TLA for machine-learning ranking is MLR (as in machine-learned ranking). So we may consider this one too :)
If you want an obscure name tangentially connected to linguistic: Plural (PLUmbing + RAnking + Learning).
OK, my creativity is temporarily exhausted, but I may be back :)
I don't have good suggestions, I like PLURAL.
On Thu, Apr 6, 2017 at 6:01 AM, Pine W wiki.pine@gmail.com wrote:
+1 for PLURAL.
Pine
discovery mailing list discovery@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/discovery
MjöLnir?
P.S. I like PLURAL.
On Thu, Apr 6, 2017 at 7:30 AM, David Causse dcausse@wikimedia.org wrote:
I don't have good suggestions, I like PLURAL.
On Thu, Apr 6, 2017 at 6:01 AM, Pine W wiki.pine@gmail.com wrote:
+1 for PLURAL.
Pine
discovery mailing list discovery@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/discovery
discovery mailing list discovery@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/discovery
Got to capitalize the final R or don't capitalize the L!
Plus, whatever are the two main components that go into building MjöLniR would be, somewhat opaquely, Sindri and Brokkr https://en.wikipedia.org/wiki/Mj%C3%B6lnir.
Trey Jones Software Engineer, Discovery Wikimedia Foundation
On Thu, Apr 6, 2017 at 12:32 PM, Mikhail Popov mpopov@wikimedia.org wrote:
MjöLnir?
P.S. I like PLURAL.
On Thu, Apr 6, 2017 at 7:30 AM, David Causse dcausse@wikimedia.org wrote:
I don't have good suggestions, I like PLURAL.
On Thu, Apr 6, 2017 at 6:01 AM, Pine W wiki.pine@gmail.com wrote:
+1 for PLURAL.
Pine
discovery mailing list discovery@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/discovery
discovery mailing list discovery@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/discovery
discovery mailing list discovery@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/discovery
OH I JUST GOT WHY WE CAN CAPITALIZE THE FINAL R.
Okay, so MjöLniR => the hammer used for _M_achine _L_earning & _R_anking, with the added benefit of the pronunciation being "myol-near" => ML-near => learning to rank articles _near_ the query.
BOOM! *mic drop*
On Thu, Apr 6, 2017 at 9:40 AM, Trey Jones tjones@wikimedia.org wrote:
Got to capitalize the final R or don't capitalize the L!
Plus, whatever are the two main components that go into building MjöLniR would be, somewhat opaquely, Sindri and Brokkr https://en.wikipedia.org/wiki/Mj%C3%B6lnir.
Trey Jones Software Engineer, Discovery Wikimedia Foundation
On Thu, Apr 6, 2017 at 12:32 PM, Mikhail Popov mpopov@wikimedia.org wrote:
MjöLnir?
P.S. I like PLURAL.
On Thu, Apr 6, 2017 at 7:30 AM, David Causse dcausse@wikimedia.org wrote:
I don't have good suggestions, I like PLURAL.
On Thu, Apr 6, 2017 at 6:01 AM, Pine W wiki.pine@gmail.com wrote:
+1 for PLURAL.
Pine
discovery mailing list discovery@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/discovery
discovery mailing list discovery@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/discovery
discovery mailing list discovery@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/discovery
discovery mailing list discovery@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/discovery
Something about PLURAL just doesn't strike me. MjoLniR on the other hand doesn't seem too bad, if a little esoteric. And sorry but i think using non-ascii in the name of a git repository is just asking for trouble somewhere :P. I'm also not opposed to being very boring and calling it cirrusearch-mlr or cirrussearch-ltrank
On Thu, Apr 6, 2017 at 9:46 AM, Mikhail Popov mpopov@wikimedia.org wrote:
OH I JUST GOT WHY WE CAN CAPITALIZE THE FINAL R.
Okay, so MjöLniR => the hammer used for _M_achine _L_earning & _R_anking, with the added benefit of the pronunciation being "myol-near" => ML-near => learning to rank articles _near_ the query.
BOOM! *mic drop*
On Thu, Apr 6, 2017 at 9:40 AM, Trey Jones tjones@wikimedia.org wrote:
Got to capitalize the final R or don't capitalize the L!
Plus, whatever are the two main components that go into building MjöLniR would be, somewhat opaquely, Sindri and Brokkr https://en.wikipedia.org/wiki/Mj%C3%B6lnir.
Trey Jones Software Engineer, Discovery Wikimedia Foundation
On Thu, Apr 6, 2017 at 12:32 PM, Mikhail Popov mpopov@wikimedia.org wrote:
MjöLnir?
P.S. I like PLURAL.
On Thu, Apr 6, 2017 at 7:30 AM, David Causse dcausse@wikimedia.org wrote:
I don't have good suggestions, I like PLURAL.
On Thu, Apr 6, 2017 at 6:01 AM, Pine W wiki.pine@gmail.com wrote:
+1 for PLURAL.
Pine
discovery mailing list discovery@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/discovery
discovery mailing list discovery@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/discovery
discovery mailing list discovery@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/discovery
discovery mailing list discovery@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/discovery
discovery mailing list discovery@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/discovery
cirrus-machine-learning2rank
too long?
-- deb tankersley irc: debt Product Manager, Discovery Wikimedia Foundation
On Thu, Apr 6, 2017 at 12:11 PM, Erik Bernhardson < ebernhardson@wikimedia.org> wrote:
Something about PLURAL just doesn't strike me. MjoLniR on the other hand doesn't seem too bad, if a little esoteric. And sorry but i think using non-ascii in the name of a git repository is just asking for trouble somewhere :P. I'm also not opposed to being very boring and calling it cirrusearch-mlr or cirrussearch-ltrank
On Thu, Apr 6, 2017 at 9:46 AM, Mikhail Popov mpopov@wikimedia.org wrote:
OH I JUST GOT WHY WE CAN CAPITALIZE THE FINAL R.
Okay, so MjöLniR => the hammer used for _M_achine _L_earning & _R_anking, with the added benefit of the pronunciation being "myol-near" => ML-near => learning to rank articles _near_ the query.
BOOM! *mic drop*
On Thu, Apr 6, 2017 at 9:40 AM, Trey Jones tjones@wikimedia.org wrote:
Got to capitalize the final R or don't capitalize the L!
Plus, whatever are the two main components that go into building MjöLniR would be, somewhat opaquely, Sindri and Brokkr https://en.wikipedia.org/wiki/Mj%C3%B6lnir.
Trey Jones Software Engineer, Discovery Wikimedia Foundation
On Thu, Apr 6, 2017 at 12:32 PM, Mikhail Popov mpopov@wikimedia.org wrote:
MjöLnir?
P.S. I like PLURAL.
On Thu, Apr 6, 2017 at 7:30 AM, David Causse dcausse@wikimedia.org wrote:
I don't have good suggestions, I like PLURAL.
On Thu, Apr 6, 2017 at 6:01 AM, Pine W wiki.pine@gmail.com wrote:
+1 for PLURAL.
Pine
discovery mailing list discovery@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/discovery
discovery mailing list discovery@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/discovery
discovery mailing list discovery@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/discovery
discovery mailing list discovery@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/discovery
discovery mailing list discovery@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/discovery
discovery mailing list discovery@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/discovery
"we will build out a python library to handle the bulk of the backend data plumbing" - Mario?
On Thu, Apr 6, 2017 at 9:09 PM Deborah Tankersley dtankersley@wikimedia.org wrote:
cirrus-machine-learning2rank
too long?
-- deb tankersley irc: debt Product Manager, Discovery Wikimedia Foundation
On Thu, Apr 6, 2017 at 12:11 PM, Erik Bernhardson < ebernhardson@wikimedia.org> wrote:
Something about PLURAL just doesn't strike me. MjoLniR on the other hand doesn't seem too bad, if a little esoteric. And sorry but i think using non-ascii in the name of a git repository is just asking for trouble somewhere :P. I'm also not opposed to being very boring and calling it cirrusearch-mlr or cirrussearch-ltrank
On Thu, Apr 6, 2017 at 9:46 AM, Mikhail Popov mpopov@wikimedia.org wrote:
OH I JUST GOT WHY WE CAN CAPITALIZE THE FINAL R.
Okay, so MjöLniR => the hammer used for _M_achine _L_earning & _R_anking, with the added benefit of the pronunciation being "myol-near" => ML-near => learning to rank articles _near_ the query.
BOOM! *mic drop*
On Thu, Apr 6, 2017 at 9:40 AM, Trey Jones tjones@wikimedia.org wrote:
Got to capitalize the final R or don't capitalize the L!
Plus, whatever are the two main components that go into building MjöLniR would be, somewhat opaquely, Sindri and Brokkr https://en.wikipedia.org/wiki/Mj%C3%B6lnir.
Trey Jones Software Engineer, Discovery Wikimedia Foundation
On Thu, Apr 6, 2017 at 12:32 PM, Mikhail Popov mpopov@wikimedia.org wrote:
MjöLnir?
P.S. I like PLURAL.
On Thu, Apr 6, 2017 at 7:30 AM, David Causse dcausse@wikimedia.org wrote:
I don't have good suggestions, I like PLURAL.
On Thu, Apr 6, 2017 at 6:01 AM, Pine W wiki.pine@gmail.com wrote:
+1 for PLURAL.
Pine
discovery mailing list discovery@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/discovery
discovery mailing list discovery@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/discovery
discovery mailing list discovery@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/discovery
discovery mailing list discovery@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/discovery
discovery mailing list discovery@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/discovery
discovery mailing list discovery@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/discovery
discovery mailing list discovery@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/discovery
Looks like the results are in and we'll drop this code into the search/MjoLniR repository. Seems a reasonable enough name.
On Thu, Apr 6, 2017 at 1:00 PM, Jan Drewniak jdrewniak@wikimedia.org wrote:
"we will build out a python library to handle the bulk of the backend data plumbing" - Mario?
On Thu, Apr 6, 2017 at 9:09 PM Deborah Tankersley < dtankersley@wikimedia.org> wrote:
cirrus-machine-learning2rank
too long?
-- deb tankersley irc: debt Product Manager, Discovery Wikimedia Foundation
On Thu, Apr 6, 2017 at 12:11 PM, Erik Bernhardson < ebernhardson@wikimedia.org> wrote:
Something about PLURAL just doesn't strike me. MjoLniR on the other hand doesn't seem too bad, if a little esoteric. And sorry but i think using non-ascii in the name of a git repository is just asking for trouble somewhere :P. I'm also not opposed to being very boring and calling it cirrusearch-mlr or cirrussearch-ltrank
On Thu, Apr 6, 2017 at 9:46 AM, Mikhail Popov mpopov@wikimedia.org wrote:
OH I JUST GOT WHY WE CAN CAPITALIZE THE FINAL R.
Okay, so MjöLniR => the hammer used for _M_achine _L_earning & _R_anking, with the added benefit of the pronunciation being "myol-near" => ML-near => learning to rank articles _near_ the query.
BOOM! *mic drop*
On Thu, Apr 6, 2017 at 9:40 AM, Trey Jones tjones@wikimedia.org wrote:
Got to capitalize the final R or don't capitalize the L!
Plus, whatever are the two main components that go into building MjöLniR would be, somewhat opaquely, Sindri and Brokkr https://en.wikipedia.org/wiki/Mj%C3%B6lnir.
Trey Jones Software Engineer, Discovery Wikimedia Foundation
On Thu, Apr 6, 2017 at 12:32 PM, Mikhail Popov mpopov@wikimedia.org wrote:
MjöLnir?
P.S. I like PLURAL.
On Thu, Apr 6, 2017 at 7:30 AM, David Causse dcausse@wikimedia.org wrote:
I don't have good suggestions, I like PLURAL.
On Thu, Apr 6, 2017 at 6:01 AM, Pine W wiki.pine@gmail.com wrote:
+1 for PLURAL.
Pine
discovery mailing list discovery@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/discovery
discovery mailing list discovery@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/discovery
discovery mailing list discovery@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/discovery
discovery mailing list discovery@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/discovery
discovery mailing list discovery@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/discovery
discovery mailing list discovery@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/discovery
discovery mailing list discovery@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/discovery
-- Jan Drewniak UX Engineer, Discovery Wikimedia Foundation
discovery mailing list discovery@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/discovery
Looks like the results are in and we'll drop this code into the search/MjoLniR repository. Seems a reasonable enough name.
Y'know, the funky capitalization is cool and should be in the readme, but maybe it should be all lowercase in the directory name.
On Fri, Apr 7, 2017 at 3:12 PM, Justin Ormont justin.ormont@gmail.com wrote:
Can someone post a recording of folks attempting to pronounce this?
https://forvo.com/word/mj%C3%B6lnir/#de https://www.youtube.com/watch?v=AF_DJzgNnF0&t=27s https://www.youtube.com/watch?v=K0SnsP8YEN8&t=9s
Or did you mean us Discovery folks? :Þ
Meow Meow FTW! ;)
-- deb tankersley irc: debt Product Manager, Discovery Wikimedia Foundation
On Fri, Apr 7, 2017 at 1:26 PM, Trey Jones tjones@wikimedia.org wrote:
Looks like the results are in and we'll drop this code into the
search/MjoLniR repository. Seems a reasonable enough name.
Y'know, the funky capitalization is cool and should be in the readme, but maybe it should be all lowercase in the directory name.
On Fri, Apr 7, 2017 at 3:12 PM, Justin Ormont justin.ormont@gmail.com wrote:
Can someone post a recording of folks attempting to pronounce this?
https://forvo.com/word/mj%C3%B6lnir/#de https://www.youtube.com/watch?v=AF_DJzgNnF0&t=27s https://www.youtube.com/watch?v=K0SnsP8YEN8&t=9s
Or did you mean us Discovery folks? :Þ
discovery mailing list discovery@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/discovery