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Posted to users@opennlp.apache.org by nikhil jain <ni...@yahoo.com.INVALID> on 2014/11/17 19:32:38 UTC
Need to speed up the model creation process of OpenNLP
Hi,
I am using OpenNLP Token Name Finder for parsing the unstructured data. I have created a corpus of about 4 million records. When I am creating a model out of the training set using openNLP API's in Eclipse using default setting (cut-off 5 and iterations 100), process is taking a good amount of time, around 2-3 hours.
Can someone suggest me how can I reduce the time as I want to experiment with different iterations but as the model creation process is taking so much time, I am not able to experiment with it. This is really a time consuming process.
Please provide some feedback.
Thanks in advance.Nikhil Jain
Re: Need to speed up the model creation process of OpenNLP
Posted by Joern Kottmann <ko...@gmail.com>.
The runtime almost scales with the number of cores your
CPU you have. If you have a 4 core CPU you might come down
from 3 hours to 1 hour.
To enabled it you need to train with the -params argument and provide
a config file for the learner. There are samples shipped with OpenNLP.
Jörn
On Wed, 2014-11-19 at 20:19 +0000, nikhil jain wrote:
> Hi Rodrigo,
> No, I am not using multi-threading, it's a simple Java program, took help from openNLP documentation but it is worth mentioning over here is that as the corpus is containing 4 million records so my Java program running in eclipse was frequently giving me java heap space issue (out of memory issue) so I investigate a bit and found that process was taking around 10GB memory for building the model so i increased the memory to 10 GB using -Xmx parameter. so it worked properly but took 3 hours.
> Thanks-NIkhil
> From: Rodrigo Agerri <ra...@apache.org>
> To: "dev@opennlp.apache.org" <de...@opennlp.apache.org>; nikhil jain <ni...@yahoo.com>
> Cc: "users@opennlp.apache.org" <us...@opennlp.apache.org>
> Sent: Wednesday, November 19, 2014 2:17 AM
> Subject: Re: Need to speed up the model creation process of OpenNLP
>
> Hi,
>
> Are you using multithreading, lots of threads, RAM memory?
>
> R
>
>
>
>
> On Tue, Nov 18, 2014 at 5:46 PM, nikhil jain
> <ni...@yahoo.com.invalid> wrote:
> > Hi,
> > I asked below question yesterday, did anyone get a chance to look at this.
> > I am new in OpenNLP and really need some help. Please provide some clue or link or example.
> > ThanksNIkhil
> > From: nikhil jain <ni...@yahoo.com.INVALID>
> > To: "users@opennlp.apache.org" <us...@opennlp.apache.org>; Dev at Opennlp Apache <de...@opennlp.apache.org>
> > Sent: Tuesday, November 18, 2014 12:02 AM
> > Subject: Need to speed up the model creation process of OpenNLP
> >
> > Hi,
> > I am using OpenNLP Token Name Finder for parsing the unstructured data. I have created a corpus of about 4 million records. When I am creating a model out of the training set using openNLP API's in Eclipse using default setting (cut-off 5 and iterations 100), process is taking a good amount of time, around 2-3 hours.
> > Can someone suggest me how can I reduce the time as I want to experiment with different iterations but as the model creation process is taking so much time, I am not able to experiment with it. This is really a time consuming process.
> > Please provide some feedback.
> > Thanks in advance.Nikhil Jain
> >
> >
>
>
Re: Need to speed up the model creation process of OpenNLP
Posted by nikhil jain <ni...@yahoo.com.INVALID>.
Hi Rodrigo,
No, I am not using multi-threading, it's a simple Java program, took help from openNLP documentation but it is worth mentioning over here is that as the corpus is containing 4 million records so my Java program running in eclipse was frequently giving me java heap space issue (out of memory issue) so I investigate a bit and found that process was taking around 10GB memory for building the model so i increased the memory to 10 GB using -Xmx parameter. so it worked properly but took 3 hours.
Thanks-NIkhil
From: Rodrigo Agerri <ra...@apache.org>
To: "dev@opennlp.apache.org" <de...@opennlp.apache.org>; nikhil jain <ni...@yahoo.com>
Cc: "users@opennlp.apache.org" <us...@opennlp.apache.org>
Sent: Wednesday, November 19, 2014 2:17 AM
Subject: Re: Need to speed up the model creation process of OpenNLP
Hi,
Are you using multithreading, lots of threads, RAM memory?
R
On Tue, Nov 18, 2014 at 5:46 PM, nikhil jain
<ni...@yahoo.com.invalid> wrote:
> Hi,
> I asked below question yesterday, did anyone get a chance to look at this.
> I am new in OpenNLP and really need some help. Please provide some clue or link or example.
> ThanksNIkhil
> From: nikhil jain <ni...@yahoo.com.INVALID>
> To: "users@opennlp.apache.org" <us...@opennlp.apache.org>; Dev at Opennlp Apache <de...@opennlp.apache.org>
> Sent: Tuesday, November 18, 2014 12:02 AM
> Subject: Need to speed up the model creation process of OpenNLP
>
> Hi,
> I am using OpenNLP Token Name Finder for parsing the unstructured data. I have created a corpus of about 4 million records. When I am creating a model out of the training set using openNLP API's in Eclipse using default setting (cut-off 5 and iterations 100), process is taking a good amount of time, around 2-3 hours.
> Can someone suggest me how can I reduce the time as I want to experiment with different iterations but as the model creation process is taking so much time, I am not able to experiment with it. This is really a time consuming process.
> Please provide some feedback.
> Thanks in advance.Nikhil Jain
>
>
Re: Need to speed up the model creation process of OpenNLP
Posted by nikhil jain <ni...@yahoo.com.INVALID>.
Hi Rodrigo,
No, I am not using multi-threading, it's a simple Java program, took help from openNLP documentation but it is worth mentioning over here is that as the corpus is containing 4 million records so my Java program running in eclipse was frequently giving me java heap space issue (out of memory issue) so I investigate a bit and found that process was taking around 10GB memory for building the model so i increased the memory to 10 GB using -Xmx parameter. so it worked properly but took 3 hours.
Thanks-NIkhil
From: Rodrigo Agerri <ra...@apache.org>
To: "dev@opennlp.apache.org" <de...@opennlp.apache.org>; nikhil jain <ni...@yahoo.com>
Cc: "users@opennlp.apache.org" <us...@opennlp.apache.org>
Sent: Wednesday, November 19, 2014 2:17 AM
Subject: Re: Need to speed up the model creation process of OpenNLP
Hi,
Are you using multithreading, lots of threads, RAM memory?
R
On Tue, Nov 18, 2014 at 5:46 PM, nikhil jain
<ni...@yahoo.com.invalid> wrote:
> Hi,
> I asked below question yesterday, did anyone get a chance to look at this.
> I am new in OpenNLP and really need some help. Please provide some clue or link or example.
> ThanksNIkhil
> From: nikhil jain <ni...@yahoo.com.INVALID>
> To: "users@opennlp.apache.org" <us...@opennlp.apache.org>; Dev at Opennlp Apache <de...@opennlp.apache.org>
> Sent: Tuesday, November 18, 2014 12:02 AM
> Subject: Need to speed up the model creation process of OpenNLP
>
> Hi,
> I am using OpenNLP Token Name Finder for parsing the unstructured data. I have created a corpus of about 4 million records. When I am creating a model out of the training set using openNLP API's in Eclipse using default setting (cut-off 5 and iterations 100), process is taking a good amount of time, around 2-3 hours.
> Can someone suggest me how can I reduce the time as I want to experiment with different iterations but as the model creation process is taking so much time, I am not able to experiment with it. This is really a time consuming process.
> Please provide some feedback.
> Thanks in advance.Nikhil Jain
>
>
Re: Need to speed up the model creation process of OpenNLP
Posted by Rodrigo Agerri <ra...@apache.org>.
Hi,
Are you using multithreading, lots of threads, RAM memory?
R
On Tue, Nov 18, 2014 at 5:46 PM, nikhil jain
<ni...@yahoo.com.invalid> wrote:
> Hi,
> I asked below question yesterday, did anyone get a chance to look at this.
> I am new in OpenNLP and really need some help. Please provide some clue or link or example.
> ThanksNIkhil
> From: nikhil jain <ni...@yahoo.com.INVALID>
> To: "users@opennlp.apache.org" <us...@opennlp.apache.org>; Dev at Opennlp Apache <de...@opennlp.apache.org>
> Sent: Tuesday, November 18, 2014 12:02 AM
> Subject: Need to speed up the model creation process of OpenNLP
>
> Hi,
> I am using OpenNLP Token Name Finder for parsing the unstructured data. I have created a corpus of about 4 million records. When I am creating a model out of the training set using openNLP API's in Eclipse using default setting (cut-off 5 and iterations 100), process is taking a good amount of time, around 2-3 hours.
> Can someone suggest me how can I reduce the time as I want to experiment with different iterations but as the model creation process is taking so much time, I am not able to experiment with it. This is really a time consuming process.
> Please provide some feedback.
> Thanks in advance.Nikhil Jain
>
>
Re: Need to speed up the model creation process of OpenNLP
Posted by Rodrigo Agerri <ra...@apache.org>.
Hi,
Are you using multithreading, lots of threads, RAM memory?
R
On Tue, Nov 18, 2014 at 5:46 PM, nikhil jain
<ni...@yahoo.com.invalid> wrote:
> Hi,
> I asked below question yesterday, did anyone get a chance to look at this.
> I am new in OpenNLP and really need some help. Please provide some clue or link or example.
> ThanksNIkhil
> From: nikhil jain <ni...@yahoo.com.INVALID>
> To: "users@opennlp.apache.org" <us...@opennlp.apache.org>; Dev at Opennlp Apache <de...@opennlp.apache.org>
> Sent: Tuesday, November 18, 2014 12:02 AM
> Subject: Need to speed up the model creation process of OpenNLP
>
> Hi,
> I am using OpenNLP Token Name Finder for parsing the unstructured data. I have created a corpus of about 4 million records. When I am creating a model out of the training set using openNLP API's in Eclipse using default setting (cut-off 5 and iterations 100), process is taking a good amount of time, around 2-3 hours.
> Can someone suggest me how can I reduce the time as I want to experiment with different iterations but as the model creation process is taking so much time, I am not able to experiment with it. This is really a time consuming process.
> Please provide some feedback.
> Thanks in advance.Nikhil Jain
>
>
Re: Need to speed up the model creation process of OpenNLP
Posted by Samik Raychaudhuri <sa...@gmail.com>.
Hi Nikhil,
I did a bit of research in #2 and it doesn't look good. You are probably
better off doing #1 along with whatever speed-ups you can achieve
through parallelism. Regarding your question in #1, I am not aware of
CLI/API in openNLP to do that (that doesn't necessarily mean it is not
there) - other members might be able to help you there.
Hope this helps.
Best,
-Samik
On 24/11/2014 10:47 PM, nikhil jain wrote:
> Thanks Samik for the suggestions.
>
> #1: I think I should go with this one. I know about confusion
> matrix(matrix of True positive, False positive and so on) but does
> openNLP provide any CLI or API's for creating this confusion matrix or
> do you know any other tool/library which I can use for this.
>
> #2 Every time when I add some records or class in my corpus, I need to
> train it from scratch.So, I don't think so there is a way to retrain
> the model again.
>
> Thanks
> Nikhil
>
> ------------------------------------------------------------------------
> *From:* Samik Raychaudhuri <sa...@gmail.com>
> *To:* dev@opennlp.apache.org
> *Sent:* Thursday, November 20, 2014 11:46 PM
> *Subject:* Re: Need to speed up the model creation process of OpenNLP
>
> Hi Nikhil,
>
> #1: What I meant was: see if you can build a model on 1M records, check
> the confusion matrix and see the performance. Then create a model on
> 1.5M records, check the confusion matrix and compare. If the improvement
> is noticeable, then it would essentially make sense to train on more
> data, on the other hand, if the improvement is not noticeable, then you
> have already reached a plateau in terms of learning by the model. Please
> look up confusion matrix related information on the web.
>
> #2: Here the approach is somewhat different. If you have specific
> classes of things that you need to identify, then start off with even
> smaller data set containing training data related to one such class
> (say, just 5K~10K set), then add training data incrementally from other
> classes (and train again - from scratch). Note that, I do not think
> there is a way to 'warm start' the learning: I do not think you can take
> a model that has been trained on one class of data, and incrementally
> make it learn on another set/class of data. That would be a nice
> research problem. (BTW, if this is already possible, let me know).
>
> Bottom line, if you have more data to train, it will take time. You can
> consider some trade-offs in terms of ML as mentioned above. You should
> definitely use the above along with parallelization, as mentioned by
> Rodrigo/Joern - it would be a sin not to use it if you are on a
> multi-core CPU. You might still need the 10gig java heap to process the
> data though, IMHO.
>
> HTH.
> Best,
> -Samik
>
>
>
> On 19/11/2014 12:09 PM, nikhil jain wrote:
> > Hi Samik,
> > Thank you so much for the quick feedback.
> > 1. You can possibly have smaller training sets and see if the models
> deteriorate substantially:
> > Yes I have 4 training sets each containing 1 million records but i
> dont understand how it would be useful? because when I am creating a
> one model out of these 4 training sets then I have to pass all the
> records at once for creating a model so it would take time, right?
> > 2. Another strategy is to incrementally introduce training sets
> containing specific class of Token Names - that would provide a
> quicker turnaroundRight, I am doing the same thing as you mentioned,
> like I have 4 different classes and each class contains 1 Million
> records. so initially I created a model on 1 Millions records so it
> took less time and worked properly then I added another one, so size
> of the corpus become 2 million and again created a model based on 2
> million records and so on, but the problem is when i am adding more
> records in the corpus then model creation process is taking time.is it
> possible to reuse the model with new training set, means like i have a
> model based on 2 million records and now i can say reuse the old model
> but adjust the model again based on new records. if this is possible
> then small training sets would be useful, right?
> > As I mentioned, I am new in openNLP and machine learning. so please
> explain with example if I am missing something.
> >
> > Thanks Nikhil
> > From: Samik Raychaudhuri <samikr@gmail.com
> <ma...@gmail.com>>
> > To: dev@opennlp.apache.org <ma...@opennlp.apache.org>
> > Sent: Wednesday, November 19, 2014 6:00 AM
> > Subject: Re: Need to speed up the model creation process of OpenNLP
> >
> > Hi,
> > This is essentially a machine learning problem, nothing to do with
> > OpenNLP. If you have such a large corpus, it would take a substantial
> > amount of time to train models. You can possibly have smaller training
> > sets and see if the models deteriorate substantially. Another strategy
> > is to incrementally introduce training sets containing specific class of
> > Token Names - that would provide a quicker turnaround.
> > Hope this help.
> > Best,
> > -Samik
> >
> >
> >
> >
> > On 18/11/2014 8:46 AM, nikhil jain wrote:
> >> Hi,
> >> I asked below question yesterday, did anyone get a chance to look
> at this.
> >> I am new in OpenNLP and really need some help. Please provide some
> clue or link or example.
> >> ThanksNIkhil
> >> From: nikhil jain <nikhil_jain1234@yahoo.com.INVALID
> <ma...@yahoo.com.INVALID>>
> >> To: "users@opennlp.apache.org <ma...@opennlp.apache.org>"
> <users@opennlp.apache.org <ma...@opennlp.apache.org>>; Dev at
> Opennlp Apache <dev@opennlp.apache.org <ma...@opennlp.apache.org>>
> >> Sent: Tuesday, November 18, 2014 12:02 AM
> >> Subject: Need to speed up the model creation process of OpenNLP
> >>
> >> Hi,
> >> I am using OpenNLP Token Name Finder for parsing the unstructured
> data. I have created a corpus of about 4 million records. When I am
> creating a model out of the training set using openNLP API's in
> Eclipse using default setting (cut-off 5 and iterations 100), process
> is taking a good amount of time, around 2-3 hours.
> >> Can someone suggest me how can I reduce the time as I want to
> experiment with different iterations but as the model creation process
> is taking so much time, I am not able to experiment with it. This is
> really a time consuming process.
> >> Please provide some feedback.
> >> Thanks in advance.Nikhil Jain
> >>
> >>
> >
> >
> >
>
>
>
Re: Need to speed up the model creation process of OpenNLP
Posted by nikhil jain <ni...@yahoo.com.INVALID>.
Thanks Samik for the suggestions.
#1: I think I should go with this one. I know about confusion matrix(matrix of True positive, False positive and so on) but does openNLP provide any CLI or API's for creating this confusion matrix or do you know any other tool/library which I can use for this.
#2 Every time when I add some records or class in my corpus, I need to train it from scratch.So, I don't think so there is a way to retrain the model again.
ThanksNikhil
From: Samik Raychaudhuri <sa...@gmail.com>
To: dev@opennlp.apache.org
Sent: Thursday, November 20, 2014 11:46 PM
Subject: Re: Need to speed up the model creation process of OpenNLP
Hi Nikhil,
#1: What I meant was: see if you can build a model on 1M records, check
the confusion matrix and see the performance. Then create a model on
1.5M records, check the confusion matrix and compare. If the improvement
is noticeable, then it would essentially make sense to train on more
data, on the other hand, if the improvement is not noticeable, then you
have already reached a plateau in terms of learning by the model. Please
look up confusion matrix related information on the web.
#2: Here the approach is somewhat different. If you have specific
classes of things that you need to identify, then start off with even
smaller data set containing training data related to one such class
(say, just 5K~10K set), then add training data incrementally from other
classes (and train again - from scratch). Note that, I do not think
there is a way to 'warm start' the learning: I do not think you can take
a model that has been trained on one class of data, and incrementally
make it learn on another set/class of data. That would be a nice
research problem. (BTW, if this is already possible, let me know).
Bottom line, if you have more data to train, it will take time. You can
consider some trade-offs in terms of ML as mentioned above. You should
definitely use the above along with parallelization, as mentioned by
Rodrigo/Joern - it would be a sin not to use it if you are on a
multi-core CPU. You might still need the 10gig java heap to process the
data though, IMHO.
HTH.
Best,
-Samik
On 19/11/2014 12:09 PM, nikhil jain wrote:
> Hi Samik,
> Thank you so much for the quick feedback.
> 1. You can possibly have smaller training sets and see if the models deteriorate substantially:
> Yes I have 4 training sets each containing 1 million records but i dont understand how it would be useful? because when I am creating a one model out of these 4 training sets then I have to pass all the records at once for creating a model so it would take time, right?
> 2. Another strategy is to incrementally introduce training sets containing specific class of Token Names - that would provide a quicker turnaroundRight, I am doing the same thing as you mentioned, like I have 4 different classes and each class contains 1 Million records. so initially I created a model on 1 Millions records so it took less time and worked properly then I added another one, so size of the corpus become 2 million and again created a model based on 2 million records and so on, but the problem is when i am adding more records in the corpus then model creation process is taking time.is it possible to reuse the model with new training set, means like i have a model based on 2 million records and now i can say reuse the old model but adjust the model again based on new records. if this is possible then small training sets would be useful, right?
> As I mentioned, I am new in openNLP and machine learning. so please explain with example if I am missing something.
>
> Thanks Nikhil
> From: Samik Raychaudhuri <sa...@gmail.com>
> To: dev@opennlp.apache.org
> Sent: Wednesday, November 19, 2014 6:00 AM
> Subject: Re: Need to speed up the model creation process of OpenNLP
>
> Hi,
> This is essentially a machine learning problem, nothing to do with
> OpenNLP. If you have such a large corpus, it would take a substantial
> amount of time to train models. You can possibly have smaller training
> sets and see if the models deteriorate substantially. Another strategy
> is to incrementally introduce training sets containing specific class of
> Token Names - that would provide a quicker turnaround.
> Hope this help.
> Best,
> -Samik
>
>
>
>
> On 18/11/2014 8:46 AM, nikhil jain wrote:
>> Hi,
>> I asked below question yesterday, did anyone get a chance to look at this.
>> I am new in OpenNLP and really need some help. Please provide some clue or link or example.
>> ThanksNIkhil
>> From: nikhil jain <ni...@yahoo.com.INVALID>
>> To: "users@opennlp.apache.org" <us...@opennlp.apache.org>; Dev at Opennlp Apache <de...@opennlp.apache.org>
>> Sent: Tuesday, November 18, 2014 12:02 AM
>> Subject: Need to speed up the model creation process of OpenNLP
>>
>> Hi,
>> I am using OpenNLP Token Name Finder for parsing the unstructured data. I have created a corpus of about 4 million records. When I am creating a model out of the training set using openNLP API's in Eclipse using default setting (cut-off 5 and iterations 100), process is taking a good amount of time, around 2-3 hours.
>> Can someone suggest me how can I reduce the time as I want to experiment with different iterations but as the model creation process is taking so much time, I am not able to experiment with it. This is really a time consuming process.
>> Please provide some feedback.
>> Thanks in advance.Nikhil Jain
>>
>>
>
>
>
Re: Need to speed up the model creation process of OpenNLP
Posted by Samik Raychaudhuri <sa...@gmail.com>.
Hi Nikhil,
#1: What I meant was: see if you can build a model on 1M records, check
the confusion matrix and see the performance. Then create a model on
1.5M records, check the confusion matrix and compare. If the improvement
is noticeable, then it would essentially make sense to train on more
data, on the other hand, if the improvement is not noticeable, then you
have already reached a plateau in terms of learning by the model. Please
look up confusion matrix related information on the web.
#2: Here the approach is somewhat different. If you have specific
classes of things that you need to identify, then start off with even
smaller data set containing training data related to one such class
(say, just 5K~10K set), then add training data incrementally from other
classes (and train again - from scratch). Note that, I do not think
there is a way to 'warm start' the learning: I do not think you can take
a model that has been trained on one class of data, and incrementally
make it learn on another set/class of data. That would be a nice
research problem. (BTW, if this is already possible, let me know).
Bottom line, if you have more data to train, it will take time. You can
consider some trade-offs in terms of ML as mentioned above. You should
definitely use the above along with parallelization, as mentioned by
Rodrigo/Joern - it would be a sin not to use it if you are on a
multi-core CPU. You might still need the 10gig java heap to process the
data though, IMHO.
HTH.
Best,
-Samik
On 19/11/2014 12:09 PM, nikhil jain wrote:
> Hi Samik,
> Thank you so much for the quick feedback.
> 1. You can possibly have smaller training sets and see if the models deteriorate substantially:
> Yes I have 4 training sets each containing 1 million records but i dont understand how it would be useful? because when I am creating a one model out of these 4 training sets then I have to pass all the records at once for creating a model so it would take time, right?
> 2. Another strategy is to incrementally introduce training sets containing specific class of Token Names - that would provide a quicker turnaroundRight, I am doing the same thing as you mentioned, like I have 4 different classes and each class contains 1 Million records. so initially I created a model on 1 Millions records so it took less time and worked properly then I added another one, so size of the corpus become 2 million and again created a model based on 2 million records and so on, but the problem is when i am adding more records in the corpus then model creation process is taking time.is it possible to reuse the model with new training set, means like i have a model based on 2 million records and now i can say reuse the old model but adjust the model again based on new records. if this is possible then small training sets would be useful, right?
> As I mentioned, I am new in openNLP and machine learning. so please explain with example if I am missing something.
>
> Thanks Nikhil
> From: Samik Raychaudhuri <sa...@gmail.com>
> To: dev@opennlp.apache.org
> Sent: Wednesday, November 19, 2014 6:00 AM
> Subject: Re: Need to speed up the model creation process of OpenNLP
>
> Hi,
> This is essentially a machine learning problem, nothing to do with
> OpenNLP. If you have such a large corpus, it would take a substantial
> amount of time to train models. You can possibly have smaller training
> sets and see if the models deteriorate substantially. Another strategy
> is to incrementally introduce training sets containing specific class of
> Token Names - that would provide a quicker turnaround.
> Hope this help.
> Best,
> -Samik
>
>
>
>
> On 18/11/2014 8:46 AM, nikhil jain wrote:
>> Hi,
>> I asked below question yesterday, did anyone get a chance to look at this.
>> I am new in OpenNLP and really need some help. Please provide some clue or link or example.
>> ThanksNIkhil
>> From: nikhil jain <ni...@yahoo.com.INVALID>
>> To: "users@opennlp.apache.org" <us...@opennlp.apache.org>; Dev at Opennlp Apache <de...@opennlp.apache.org>
>> Sent: Tuesday, November 18, 2014 12:02 AM
>> Subject: Need to speed up the model creation process of OpenNLP
>>
>> Hi,
>> I am using OpenNLP Token Name Finder for parsing the unstructured data. I have created a corpus of about 4 million records. When I am creating a model out of the training set using openNLP API's in Eclipse using default setting (cut-off 5 and iterations 100), process is taking a good amount of time, around 2-3 hours.
>> Can someone suggest me how can I reduce the time as I want to experiment with different iterations but as the model creation process is taking so much time, I am not able to experiment with it. This is really a time consuming process.
>> Please provide some feedback.
>> Thanks in advance.Nikhil Jain
>>
>>
>
>
>
Re: Need to speed up the model creation process of OpenNLP
Posted by nikhil jain <ni...@yahoo.com.INVALID>.
Hi Samik,
Thank you so much for the quick feedback.
1. You can possibly have smaller training sets and see if the models deteriorate substantially:
Yes I have 4 training sets each containing 1 million records but i dont understand how it would be useful? because when I am creating a one model out of these 4 training sets then I have to pass all the records at once for creating a model so it would take time, right?
2. Another strategy is to incrementally introduce training sets containing specific class of Token Names - that would provide a quicker turnaroundRight, I am doing the same thing as you mentioned, like I have 4 different classes and each class contains 1 Million records. so initially I created a model on 1 Millions records so it took less time and worked properly then I added another one, so size of the corpus become 2 million and again created a model based on 2 million records and so on, but the problem is when i am adding more records in the corpus then model creation process is taking time.is it possible to reuse the model with new training set, means like i have a model based on 2 million records and now i can say reuse the old model but adjust the model again based on new records. if this is possible then small training sets would be useful, right?
As I mentioned, I am new in openNLP and machine learning. so please explain with example if I am missing something.
Thanks Nikhil
From: Samik Raychaudhuri <sa...@gmail.com>
To: dev@opennlp.apache.org
Sent: Wednesday, November 19, 2014 6:00 AM
Subject: Re: Need to speed up the model creation process of OpenNLP
Hi,
This is essentially a machine learning problem, nothing to do with
OpenNLP. If you have such a large corpus, it would take a substantial
amount of time to train models. You can possibly have smaller training
sets and see if the models deteriorate substantially. Another strategy
is to incrementally introduce training sets containing specific class of
Token Names - that would provide a quicker turnaround.
Hope this help.
Best,
-Samik
On 18/11/2014 8:46 AM, nikhil jain wrote:
> Hi,
> I asked below question yesterday, did anyone get a chance to look at this.
> I am new in OpenNLP and really need some help. Please provide some clue or link or example.
> ThanksNIkhil
> From: nikhil jain <ni...@yahoo.com.INVALID>
> To: "users@opennlp.apache.org" <us...@opennlp.apache.org>; Dev at Opennlp Apache <de...@opennlp.apache.org>
> Sent: Tuesday, November 18, 2014 12:02 AM
> Subject: Need to speed up the model creation process of OpenNLP
>
> Hi,
> I am using OpenNLP Token Name Finder for parsing the unstructured data. I have created a corpus of about 4 million records. When I am creating a model out of the training set using openNLP API's in Eclipse using default setting (cut-off 5 and iterations 100), process is taking a good amount of time, around 2-3 hours.
> Can someone suggest me how can I reduce the time as I want to experiment with different iterations but as the model creation process is taking so much time, I am not able to experiment with it. This is really a time consuming process.
> Please provide some feedback.
> Thanks in advance.Nikhil Jain
>
>
Re: Need to speed up the model creation process of OpenNLP
Posted by Samik Raychaudhuri <sa...@gmail.com>.
Hi,
This is essentially a machine learning problem, nothing to do with
OpenNLP. If you have such a large corpus, it would take a substantial
amount of time to train models. You can possibly have smaller training
sets and see if the models deteriorate substantially. Another strategy
is to incrementally introduce training sets containing specific class of
Token Names - that would provide a quicker turnaround.
Hope this help.
Best,
-Samik
On 18/11/2014 8:46 AM, nikhil jain wrote:
> Hi,
> I asked below question yesterday, did anyone get a chance to look at this.
> I am new in OpenNLP and really need some help. Please provide some clue or link or example.
> ThanksNIkhil
> From: nikhil jain <ni...@yahoo.com.INVALID>
> To: "users@opennlp.apache.org" <us...@opennlp.apache.org>; Dev at Opennlp Apache <de...@opennlp.apache.org>
> Sent: Tuesday, November 18, 2014 12:02 AM
> Subject: Need to speed up the model creation process of OpenNLP
>
> Hi,
> I am using OpenNLP Token Name Finder for parsing the unstructured data. I have created a corpus of about 4 million records. When I am creating a model out of the training set using openNLP API's in Eclipse using default setting (cut-off 5 and iterations 100), process is taking a good amount of time, around 2-3 hours.
> Can someone suggest me how can I reduce the time as I want to experiment with different iterations but as the model creation process is taking so much time, I am not able to experiment with it. This is really a time consuming process.
> Please provide some feedback.
> Thanks in advance.Nikhil Jain
>
>
Re: Need to speed up the model creation process of OpenNLP
Posted by nikhil jain <ni...@yahoo.com.INVALID>.
Hi,
I asked below question yesterday, did anyone get a chance to look at this.
I am new in OpenNLP and really need some help. Please provide some clue or link or example.
ThanksNIkhil
From: nikhil jain <ni...@yahoo.com.INVALID>
To: "users@opennlp.apache.org" <us...@opennlp.apache.org>; Dev at Opennlp Apache <de...@opennlp.apache.org>
Sent: Tuesday, November 18, 2014 12:02 AM
Subject: Need to speed up the model creation process of OpenNLP
Hi,
I am using OpenNLP Token Name Finder for parsing the unstructured data. I have created a corpus of about 4 million records. When I am creating a model out of the training set using openNLP API's in Eclipse using default setting (cut-off 5 and iterations 100), process is taking a good amount of time, around 2-3 hours.
Can someone suggest me how can I reduce the time as I want to experiment with different iterations but as the model creation process is taking so much time, I am not able to experiment with it. This is really a time consuming process.
Please provide some feedback.
Thanks in advance.Nikhil Jain
Re: Need to speed up the model creation process of OpenNLP
Posted by nikhil jain <ni...@yahoo.com.INVALID>.
Hi,
I asked below question yesterday, did anyone get a chance to look at this.
I am new in OpenNLP and really need some help. Please provide some clue or link or example.
ThanksNIkhil
From: nikhil jain <ni...@yahoo.com.INVALID>
To: "users@opennlp.apache.org" <us...@opennlp.apache.org>; Dev at Opennlp Apache <de...@opennlp.apache.org>
Sent: Tuesday, November 18, 2014 12:02 AM
Subject: Need to speed up the model creation process of OpenNLP
Hi,
I am using OpenNLP Token Name Finder for parsing the unstructured data. I have created a corpus of about 4 million records. When I am creating a model out of the training set using openNLP API's in Eclipse using default setting (cut-off 5 and iterations 100), process is taking a good amount of time, around 2-3 hours.
Can someone suggest me how can I reduce the time as I want to experiment with different iterations but as the model creation process is taking so much time, I am not able to experiment with it. This is really a time consuming process.
Please provide some feedback.
Thanks in advance.Nikhil Jain