I got some questions when tried to read and learn the Transformer paper "Attention is all you need":
Which parameters exactly are Tranformer model learned during training process since the attention weight matrix is temporarily calculated from "softmax(QKT/√dk)"? The only trained parameters i know are the linear transformation factor applied on input before entering Multi-head Attention and factors inside FFN. Is there any parameter else? I wish to have a clear and unambiguous summary please.
What is the role of FFN in this model? How does it process the data and why we need it? I wish to have a simple and direct explanation please.
Please forgive my grammar mistakes since English is not my native language. Thank you so much.
the parameters are the weights of linear layers refer to this question
take a look into this answer
Related
We are trying to understand the underlying model of Rasa - the forums there still didnt get us an answer - on two main questions:
we understand that Rasa model is a transformer-based architecture. Was it
pre-trained on any data set? (eg wikipedia, etc)
then, if we
understand correctly, the intent classification is a fine tuning task
on top of that transformer. How come it works with such small
training sets?
appreciate any insights!
thanks
Lior
the transformer model is not pre-trained on any dataset. We use quite a shallow stack of transformer which is not as data hungry as deeper stacks of transformers used in large pre-trained language models.
Having said that, there isn't an exact number of data points that will be sufficient for training your assistant as it varies by the domain and your problem. Usually a good estimate is 30-40 examples per intent.
I have been trying some frameworks and algorithms, and I can't find one that do what I want - which is classify the column of the data based on the value.
I tried to use Bayes algorithm, but it isn't very precise because I can't expect that the data that is being searched for is in the training set - but I can expect that the pattern is in the training.
I don't have background in Machine Learning / AI, but I was looking for some working example before really going deeper in the implementation.
I built a smaller ARFF to exemplify. Also tried lots of Weka classifying algorithms but none of them gave me good results.
#relation recommend
#attribute class {name,email,taxid,phone}
#attribute text String
#data
name,'Erik Kolh'
name,'Eric Candid'
name,'Allan Pavinan'
name,'Jubaru Guttenberg'
name,'Barabara Bere'
name,'Chuck Azul'
email,'erik#gmail.com'
email,'steven#spielberg.com'
email,'dogs#cats.com'
taxid,'123611216'
taxid,'123545413'
taxid,'562321677'
taxid,'671312678'
taxid,'123123216'
phone,'438-597-7427'
phone,'478-711-7678'
phone,'321-651-5468'
My expectation is train a huge dataset like the above one and get recommendations based on the pattern, e.g.:
joao#bing.com -> email
Joao Vitor -> name
400-123-5519 -> phone
Can you please suggest any algorithms, examples or ideas to research?
I couldn't find a good fit, maybe it's just lack of vocabulary.
Thank you!
What you are trying to do is called named entity recognition (NER). Weka is most likely not a real help here. The library Mallet (http://mallet.cs.umass.edu) might be a good fit. I would recommend a Conditional Random Field (CRF) based approach.
If you would like to stay with weka, you need to change your feature space. Then Naive bayes will be do ok on your data as presented
E.g. add a features for
whether the word has only characters
whether it is alphanumeric
whether it is numeric data
number of Numbers,
whether it starts captilized
... (just be creative)
I'm not sure if this is the best place to ask this, but you guys have been helpful with plenty of my CS homework in the past so I figure I'll give it a shot.
I'm looking for an algorithm to blindly combine several dependent variables into an index that produces the best linear fit with an external variable. Basically, it would combine the dependent variables using different mathematical operators, include or not include each one, etc. until an index is developed that best correlates with my external variable.
Has anyone seen/heard of something like this before? Even if you could point me in the right direction or to the right place to ask, I would appreciate it. Thanks.
Sounds like you're trying to do Multivariate Linear Regression or Multiple Regression. The simplest method (Read: less accurate) to do this is to individually compute the linear regression lines of each of the component variables and then do a weighted average of each of the lines. Beyond that I am afraid I will be of little help.
This appears to be simple linear regression using multiple explanatory variables. As the implication here is that you are using a computational approach you could do something as simple apply a linear model to your data using every possible combination of your explanatory variables that you have (whether you want to include interaction effects is your choice), choose a goodness of fit measure (R^2 being just one example) and use that to rank the fit of each model you fit?? The quality of a model is also somewhat subjective in many fields - you could reject a model containing 15 variables if it only moderately improves the fit over a far simpler model just containing 3 variables. If you have not read it already I don't doubt that you will find many useful suggestions in the following text :
Draper, N.R. and Smith, H. (1998).Applied Regression Analysis Wiley Series in Probability and Statistics
You might also try doing a google for the LASSO method of model selection.
The thing you're asking for is essentially the entirety of regression analysis.
this is what linear regression does, and this is a good portion of what "machine learning" does (machine learning is basically just a name for more complicated regression and classification algorithms). There are hundreds or thousands of different approaches with various tradeoffs, but the basic ones frequently work quite well.
If you want to learn more, the coursera course on machine learning is a great place to get a deeper understanding of this.
Does anyone know how to build automatic tagging (blog post/document) algorithm? Any example will be appreciated.
I agree with what Wooble is saying. However the naïve solution is to simply write an algorithm that calculates the lexical similarities and differences of the given blog post compared to a corpus of text. This lexical difference will give you words that are found in the blog post with more frequency than those found in the corpus. And from those words, you can infer a tag.
But I strongly recommend against it. Automatic tagging doesn't seem to work in practice. Just outsource the tagging work to your users or to services like Mechanical Turk
Late response but also had this task for a course - so in case someone else is looking to explore this, here is a starting point:
If you are looking for simple solutions or perhaps as a machine learning exercise, you might view automatic tagging as a text categorization/classification task. Naive Bayes classifiers are simple tools to figure out and there is plenty of pseudocode and material to understand these. TFIDF (term frequency-inverse document frequency) metric is something else you can look into - although commonly associated with information retrieval it can be tasked for this problem when combined with other machine learning techniques.
However, instead of assigning the new sample a single label based on a the definition of NB classifier, you will have to determine multiple labels. You can probably use the tag co-occurrence information from training set to help you with this.
This is a simplistic and naive solution and there are a lot of details on feature selection left out (stemming to reduce independent parameters, information gain, etc). Plenty of easily accessible papers on this research topic to try it out!
I've read the book Programming Collective Intelligence and found it fascinating. I'd recently heard about a challenge amazon had posted to the world to come up with a better recommendation engine for their system.
The winner apparently produced the best algorithm by limiting the amount of information that was being fed to it.
As a first rule of thumb I guess... "More information is not necessarily better when it comes to fuzzy algorithms."
I know's it's subjective, but ultimately it's a measurable thing (clicks in response to recommendations).
Since most of us are dealing with the web these days and search can be considered a form of recommendation... I suspect I'm not the only one who'd appreciate other peoples ideas on this.
In a nutshell, "What is the best way to build a recommendation ?"
You don't want to use "overall popularity" unless you have no information about the user. Instead, you want to align this user with similar users and weight accordingly.
This is exactly what Bayesian Inference does. In English, it means adjusting the overall probability you'll like something (the average rating) with ratings from other people who generally vote your way as well.
Another piece of advice, but this time ad hoc: I find that there are people where if they like something I will almost assuredly not like it. I don't know if this effect is real or imagined, but it might be fun to build in a kind of "negative effect" instead of just clumping people by similarity.
Finally there's a company specializing in exactly this called SenseArray. The owner (Ian Clarke of freenet fame) is very approachable. You can use my name if you call him up.
There is an entire research area in computer science devoted to this subject. I'd suggest reading some articles.
Agree with #Ricardo. This question is too broad, like asking "What's the best way to optimize a system?"
One common feature to nearly all existing recommendation engines is that making the final recommendation boils down to multiplying some number of matrices and vectors. For example multiply a matrix containing proximity weights between users by a vector of item ratings.
(Of course you have to be ready for most of your vectors to be super sparse!)
My answer is surely too late for #Allain but for other users finding this question through search -- send me a PM and ask a more specific question and I will be sure to respond.
(I design recommendation engines professionally.)
#Lao Tzu, I agree with you.
According to me, recommendation engines are made up of:
Context Input fed from context aware systems (logging all your data)
Logical reasoning to filter the most obvious
Expert systems that improve your subjective data over the period of time based on context inputs, and
Probabilistic reasoning to do decision-making close-to-proximity based on weighted sum of previous actions(beliefs, desires, & intentions).
P.S.
I made such recommendation engine.