I understand, but if Lucene itself would allow to overwrite the default
max size programmatically, then I think it should be clear that you do
this at your own risk :-)
Thanks for the links to your blog posts, which sound very interesting.
Thanks
Michael
Am 15.02.22 um 17:25 schrieb Alessandro Benedetti:
I believe it could make sense, but as Michael pointed out in the Jira
ticket related to the Solr integration, then we'll get complaints like
"I set it to 1.000.000 and my Solr instance doesn't work anymore" (I
kept everything super simple just to simulate a realistic scenario).
So I tend to agree to keep it to 1024 at the moment and potentially
extend it(providing some benchmark on common machines as a reference
to justify the increase).
In terms of your original question, how are you
training/fine-tuning your models?
Using pre-trained language models won't probably help you that much,
on top of that, queries are short, so you may require a well-curated
fine-tuning step.
We have a series of blog posts on that, and one is coming soon:
https://sease.io/2021/12/using-bert-to-improve-search-relevance.html
https://sease.io/2022/01/tackling-vocabulary-mismatch-with-document-expansion.html
Cheers
--------------------------
Alessandro Benedetti
Apache Lucene/Solr PMC member and Committer
Director, R&D Software Engineer, Search Consultant
www.sease.io <http://www.sease.io>
On Tue, 15 Feb 2022 at 09:10, Michael Wechner
<michael.wech...@wyona.com> wrote:
fair enough, but wouldn't it make sense that one can increase it
programmatically, e.g.
.setVectorMaxDimension(2028)
?
Thanks
Michael
Am 14.02.22 um 23:34 schrieb Michael Sokolov:
> I think we picked the 1024 number as something that seemed so large
> nobody would ever want to exceed it! Obviously that was naive. Still
> the limit serves as a cautionary point for users; if your
vectors are
> bigger than this, there is probably a better way to accomplish what
> you are after (eg better off-line training to reduce
dimensionality).
> Is 1024 the magic number? Maybe not, but before increasing I'd
like to
> see some strong evidence that bigger vectors than that are indeed
> useful as part of a search application using Lucene.
>
> -Mike
>
> On Mon, Feb 14, 2022 at 5:08 PM Julie Tibshirani
<juliet...@gmail.com> wrote:
>> Sounds good, hope the testing goes well! Memory and CPU
(largely from more expensive vector distance calculations) are
indeed the main factors to consider.
>>
>> Julie
>>
>> On Mon, Feb 14, 2022 at 1:02 PM Michael Wechner
<michael.wech...@wyona.com> wrote:
>>> 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
>>>>>
>>>>>
>>>>>
>
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