If I want to transform an image to another image,
then
transformer_model = nn.Transformer(img_size, n_heads)
transformer_model(source_image, target_image)
is this the correct way to use nn.Transformer?
No, this is not what the Transformer module does. The Transformer is primarily used for pre-training general use models for NLP on large bodies of text. If you're curious to learn more, I strongly recommend you read the article which introduced the architecture, "Attention is All You Need". If you've heard of models like BERT or GPT-2, these are examples of transformers.
It's not entirely clear what you are trying to accomplish when you ask how to "transform an image into another image." I'm thinking maybe you are looking for something this? https://junyanz.github.io/CycleGAN/
In any event, to re-answer your question: no, that's not how you use nn.Transformer. You should try to clarify what you are trying to accomplish with "transforming one picture into another," and post that description as a separate question.
Related
I'm trying to create a UML diagram from the command line without preexisting code.
Eventually I'll write code in C++/Java, but I need to create a diagram first.
I'm thinking of a header-like file, which could be read and could generate a diagram.
Of course, I could just create a header and generate it. However, I'm not supposed to write any code until I've submitted my diagram (I'd also just like to have an efficient way to do this for the future).
UML does not require application to draw. On the contrary one of the main usages of UML is to model the application that you're going to build to understand it better and make a better code as a result.
You also don't use application you create to draw UML diagram. You can use any application that supports UML modelling. Simple Google search or visit on Wikipedia will give you tons of options. You may even take a sheet of paper and a pencil. I've seen a course of UML, where participants did not use computers. They were supposed to learn UML, not tools that allow to draw it.
Finally (answering the question stated in the question topic), UML is in no way limited to model only graphical applications. Static structure and dynamic behaviour of the system exists regardless if user communicates with it through a GUI or command line.
Are you sure you understood the reason why you are supposed to make a UML diagram or do you disagree with that reason? What I can definitely suggest is to find a good book about IT business analysis using UML. This site is not to recommend specific books, but again Google will be your friend here.
As according to the comment the goal is to actually generate a UML class diagram from text let me add a second part of answer
First disclaimer. In general SO is not a place to ask for tools and that question is brushing it. Let me make my answer more generic though.
UML is in general graphical language so technically what you need is something that will parse text version of your "diagram" into a nice picture.
Most if not all tools keep UML in some textual format, be it XMI or some internal legacy solution. The problem with that is that the format is usually pretty complex.
There are some tools that are intended to "draw" diagram by typing text and that's probably something that would suit you best. In general I definitely prefer "normal" GUI but if you insist yuml.me has a nice and easy to understand textual layer based on which it generates really cool diagrams. You may expect you will find others as well, so as usually, ask uncle Google. As suggested by Thomas Kilian in a comment, PlantUML is another example as it can work without GUI and "is an open-source tool allowing users to create UML diagrams from a plain text language." (quote after Wikipedia)
I was thinking about how train the Universal Sentence Enconder in Portuguese, can you share some tips, what kind of dataset I need for example, transfer learning make a sense?
Thank you!
you can use the TensorFlow-Hub module in a TensorFlow model that trains on Portuguese tasks. So which tasks/data to use is the main question, and that's outside the realm of tensorflow-hub, i.e. you can ask that question and tag it with "machine-learning" and "nlp".
You might also want to take a look at links such as
http://www.nltk.org/howto/portuguese_en.html
https://stackoverflow.com/search?q=Portuguese+corpus
I am looking for a tool that can analyze the emotion of short texts. I searched for a week and I couldn't find a good one that is publicly available. The ideal tool is one that takes a short text as input and guesses the emotion. It is preferably a standalone application or library.
I don't need tools that is trained by texts. And although similar questions are asked before no satisfactory answers are got.
I searched the Internet and read some papers but I can't find a good tool I want. Currently I found SentiStrength, but the accuracy is not good. I am using emotional dictionaries right now. I felt that some syntax parsing may be necessary but it's too complex for me to build one. Furthermore, it's researched by some people and I don't want to reinvent the wheels. Does anyone know such publicly/research available software? I need a tool that doesn't need training before using.
Thanks in advance.
I think that you will not find a more accurate program than SentiStrength (or SoCal) for this task - other than machine learning methods in a specific narrow domain. If you have a lot (>1000) of hand-coded data for a specific domain then you might like to try a generic machine learning approach based on your data. If not, then I would stop looking for anything better ;)
Identifying entities and extracting precise information from short texts, let alone sentiment, is a very challenging problem specially with short text because of lack of context. Hovewer, there are few unsupervised approaches to extracting sentiments from texts mainly proposed by Turney (2000). Look at that and may be you can adopt the method of extracting sentiments based on adjectives in the short text for your use-case. It is hovewer important to note that this might require you to efficiently POSTag your short text accordingly.
Maybe EmoLib could be of help.
One simple question (but I haven't quite found an obvious answer in the NLP stuff I've been reading, which I'm very new to):
I want to classify emails with a probability along certain dimensions of mood. Is there an NLP package out there specifically dealing with this? Is there an obvious starting point in the literature I start reading at?
For example, if I got a short email something like "Hi, I'm not very impressed with your last email - you said the order amount would only be $15.95! Regards, Tom" then it might get 8/10 for Frustration and 0/10 for Happiness.
The actual list of moods isn't so important, but a short list of generally positive vs generally negative moods would be useful.
Thanks in advance!
--Trindaz on Fedang #NLP
You can do this with a number of different NLP tools, but nothing to my knowledge comes with it ready out of the box. Perhaps the easiest place to start would be with LingPipe (java), and you can use their very good sentiment analysis tutorial. You could also use NLTK if python is more your bent. There are some good blog posts over at Streamhacker that describe how you would use Naive Bayes to implement that.
Check out AlchemyAPI for sentiment analysis tools and scikit-learn or any other open machine learning library for the classifier.
if you have not decided to code the implementation, you can also have the data classified by some other tool. google prediction api may be an alternative.
Either way, you will need some labeled data and do the preprocessing. But if you use a tool that may help you get better accuracy easily.
I am interested to do automatic tagging for bodies of text. I am pretty new to NLP so I would like to hear some methods which you guys are familiar with in this context.
Any recommendations will be appreciated.
bag-of-words approaches should work okay. you're just trying place one tag on the entire text, right?
however, as another member pointed out to me one time, StackOverflow is mostly a programming Q&A.