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I am looking for a library that when given text like this :
"I don't know, this wasn't the first time. But as I said I'm 45yo male now and I'd rather not do things like that again"
Will return some object containing informations like this : {"gender":"male", "age":45}
(Ideally something evolved enough that it recognizes different ways of saying it, ex: "I'm 45" / "Im 45" / "I am now 45yo", "I turned 45 yesterday" etc
Within NLP, the task you're looking for is identifying/detecting PII (personally identifiable information). Depending on your budget/scope, AWS offers PII detection within Amazon Comprehend.
Alternatively, pii-codex offers an open source solution that may meet your needs.
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I was thinking about how a particular word on a Google Doc get detected/tracked every time we open the doc, the word may have multiple entries but always the correct one is chosen every time.
Also about changing the position of the commented text (let's say from line 1 to last line) which also works properly.
How does that work? I am particularly interested in the detection/tracking and backend. Are any documentation/references available?
Went through some sites but they explained how to use Google Docs.
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Is there any open source project or free avaliable source where I can query a webpage's category type (like https://www.trustedsource.org/en/feedback/url). I have more than 200K webpage in my dataset.
To me it looks like more of a classification problem which is suitable for Machine Learning. For this purpose you can make your model in popular ML frameworks (such as Keras/TensorFlow and PyTorch) or search for available ones on internet and use your dataset to do a transfer learning.
I could find a project on GitHub (link) that can be a good starting point.
Hi today and happy weekend!
that's interesting to know if a category is used as category pages, since google shows up multiple spots of one domain when it has category pages.
Examples:
danlok(com)
best example to see: bloomberg....
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I am risking asking a "question that appears subjective"... yet I find it precise, please forgive: I was wondering whether there existed a platform where people can post a specific programming need they have, and programmers could sign up for the tasks.
I am thinking of low-skill/low-time tasks here, with money involved as well. Targeted not at professionals (who have a full-time job and enough money...), but on e.g. students with some coding skills who are happy to spend 30 minutes to implement something like "write me a Python algorithm to collect all images from this webpage" for a person not knowing Python and for e.g. 30 bucks.
Could be win-win - one guy gets his stuff done without investing much time, the other has a way to earn a few bucks in their leisure time.
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Hello i haven't been using EAGLE for a while now and had mostly forgot where to get any good and complete library of basic parts like resistors, LEDs, transistors... I have tried to find a library i need on EAGLE web page, but i haven't found any, that would offer quite large amount of basic parts.
If anyone could point me to a library with a good and large set of basic parts he would really save my day.
The Sparkfun Eagle libraries are quite good. Download at https://github.com/sparkfun/SparkFun-Eagle-Libraries
dear you can use "ORCAD" software rather than using EAGLE as it is easy in use and easy availability of its libraries on net.
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I want to determine which part of audio file contain speech or music.
I hope someone has a made something like this or can tell me where to start.
Can you please suggest some method/tutorial for doing the same.
Thank you.
Check out the pyAudioAnalysis python library. Among others, it has a pre-trained speech-music classifier and two segmentation-classification methods (one based on fix-sized windows and another based on HMMs).
You can extract speech and music parts of an audio recording quite easily, e.g.:
from pyAudioAnalysis import audioSegmentation as aS
[flagsInd, classesAll, acc] = aS.mtFileClassification("data/scottish.wav", "data/svmSM", "svm", True, 'data/scottish.segments')
with a result as the one in this image
There's lots of prior art in this area, but I'd suggest browsing through some of Dan Ellis's papers. The slides for this talk has some good background. In short it's all down to picking the right feature vectors.