Hi Julie
Thanks again for your feedback!
I will do some more tests with "all-mpnet-base-v2" (768) and
"all-roberta-large-v1" (1024), so 1024 is enough for me for the moment :-)
But yes, I could imagine, that eventually it might make sense to allow
more dimensions than 1024.
Beside memory and "CPU", are there other limiting factors re more
dimensions?
Thanks
Michael
Am 14.02.22 um 21:53 schrieb Julie Tibshirani:
Hello Michael, the max number of dimensions is currently hardcoded and
can't be changed. I could see an argument for increasing the default a
bit and would be happy to discuss if you'd like to file a JIRA issue.
However 12288 dimensions still seems high to me, this is much larger
than most well-established embedding models and could require a lot of
memory.
Julie
On Mon, Feb 14, 2022 at 12:08 PM Michael Wechner
<michael.wech...@wyona.com> wrote:
Hi Julie
Thanks very much for this link, which is very interesting!
Btw, do you have an idea how to increase the default max size of 1024?
https://lists.apache.org/thread/hyb6w5c4x5rjt34k3w7zqn3yp5wvf33o
Thanks
Michael
Am 14.02.22 um 17:45 schrieb Julie Tibshirani:
Hello Michael, I don't have personal experience with these
models, but I found this article insightful:
https://medium.com/@nils_reimers/openai-gpt-3-text-embeddings-really-a-new-state-of-the-art-in-dense-text-embeddings-6571fe3ec9d9.
It evaluates the OpenAI models against a variety of existing
models on tasks like sentence similarity and text retrieval.
Although the other models are cheaper and have fewer dimensions,
the OpenAI ones perform similarly or worse. This got me thinking
that they might not be a good cost/ effectiveness trade-off,
especially the larger ones with 4096 or 12288 dimensions.
Julie
On Sun, Feb 13, 2022 at 1:55 AM Michael Wechner
<michael.wech...@wyona.com> wrote:
Re the OpenAI embedding the following recent paper might be
of interest
https://arxiv.org/pdf/2201.10005.pdf
(Text and Code Embeddings by Contrastive Pre-Training, Jan
24, 2022)
Thanks
Michael
Am 13.02.22 um 00:14 schrieb Michael Wechner:
Here a concrete example where I combine OpenAI model
"text-similarity-ada-001" with Lucene vector search
INPUT sentence: "What is your age this year?"
Result sentences
1) How old are you this year?
score '0.98860765'
2) What was your age last year?
score '0.97811764'
3) What is your age?
score '0.97094905'
4) How old are you?
score '0.9600177'
Result 1 is great and result 2 looks similar, but is not
correct from an "understanding" point of view and results 3
and 4 are good again.
I understand "similarity" is not the same as
"understanding", but I hope it makes it clearer what I am
looking for :-)
Thanks
Michael
Am 12.02.22 um 22:38 schrieb Michael Wechner:
Hi Alessandro
I am mainly interested in detecting similarity, for example
whether the following two sentences are similar resp.
likely to mean the same thing
"How old are you?"
"What is your age?"
and that the following two sentences are not similar, resp.
do not mean the same thing
"How old are you this year?"
"How old have you been last year?"
But also performance or how OpenAI embeddings compare for
example with SBERT
(https://sbert.net/docs/usage/semantic_textual_similarity.html)
Thanks
Michael
Am 12.02.22 um 20:41 schrieb Alessandro Benedetti:
Hi Michael, experience to what extent?
We have been exploring the area for a while given we
contributed the first neural search milestone to Apache Solr.
What is your curiosity? Performance? Relevance impact? How
to integrate it?
Regards
On Fri, 11 Feb 2022, 22:38 Michael Wechner,
<michael.wech...@wyona.com> wrote:
Hi
Does anyone have experience using OpenAI embeddings in
combination with Lucene vector search?
https://beta.openai.com/docs/guides/embeddings|
for example comparing performance re vector size
||https://api.openai.com/v1/engines/|||text-similarity-ada-001|/embeddings
and
||https://api.openai.com/v1/engines/||||text-similarity-davinci-001||/embeddings
?
||
|Thanks
Michael
