nlc | Neural Language Correction implemented on Tensorflow | Machine Learning library
kandi X-RAY | nlc Summary
kandi X-RAY | nlc Summary
Neural Language Correction implemented on Tensorflow
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Top functions reviewed by kandi - BETA
- Train NLC data
- Prepare nlc data
- Basic tokenizer
- Create a vocabulary
- Batch decoder
- Detokenize TLE tokens
- Detokenize a list of tokens
- Compute the rank of a list of strings
- Setup the encoder
- Downscale a tensor
- Embedding layer
- Setup the beam
- Computes the decoder graph
- Initialize NLC data
- Fix the sentence rank
- Setup batch decoding
- Setup the decoder
- Run the decoder
nlc Key Features
nlc Examples and Code Snippets
Community Discussions
Trending Discussions on nlc
QUESTION
I´m trying to estimate the parameters of a 3-parameter weibull distribution (translation parameter beta= -0.5). The problem is that I have to fit two sets of data simultaneously. Using nlc (see code below) i was able to estimate the parameters of the distribution for each set of data individually, but not simultaneously. GAMMA is something like a shared parameter (the estimated GAMMA has to be the same in both nlc estimations).
My data looks like this:
...ANSWER
Answered 2021-Apr-26 at 03:43What you are doing is fitting a nonlinear regression model y = f(x) + error with f the density function of a Weibull distribution. This has nothing to do with fitting a Weibull distribution to the sample.
If this is really what you want to do, here is how to answer your question:
QUESTION
In the documentation of IBM Watson Natural Language Classifier (NLC) is written that we pass a CSV file with our phrases and their responding classes.
So my question is: How many phrases/classes or simply said rows can we put in one file?
...ANSWER
Answered 2021-Mar-14 at 12:25The limit is 20.000 phrases and no more than 3.000 classes, as specified here.
I hope this answer helps you.
QUESTION
I've been interested in learning MPC control, and I wanted to try to nlc python example found here:
http://apmonitor.com/wiki/index.php/Main/PythonApp
When I ran the initial demo example, I got an HTTP error. I was able to run the demo example by changing the instances of "http" to "https" in the apm.py file, similar to the problem found here:
https://github.com/olivierhagolle/LANDSAT-Download/issues/33
I've been trying to run the nlc example now and I'm getting the same kind of error (shown below). However, changing the instances of "http" to "https" no longer seem to be helping.
*Traceback (most recent call last): File "C:\Users\veli95839\Documents\Python\Scripts\example_nlc\nlc.py", line 88, in response = apm_meas(server,app,x,value) File "C:\Users\veli95839\Documents\Python\Scripts\example_nlc\apm.py", line 607, in load_meas f = urllib.request.urlopen(url_base,params_en) File "C:\Users\veli95839\Documents\Python\lib\urllib\request.py", line 222, in urlopen return opener.open(url, data, timeout) File "C:\Users\veli95839\Documents\Python\lib\urllib\request.py", line 531, in open response = meth(req, response) File "C:\Users\veli95839\Documents\Python\lib\urllib\request.py", line 640, in http_response response = self.parent.error( File "C:\Users\veli95839\Documents\Python\lib\urllib\request.py", line 569, in error return self._call_chain(*args) File "C:\Users\veli95839\Documents\Python\lib\urllib\request.py", line 502, in _call_chain result = func(args) File "C:\Users\veli95839\Documents\Python\lib\urllib\request.py", line 649, in http_error_default raise HTTPError(req.full_url, code, msg, hdrs, fp) urllib.error.HTTPError: HTTP Error 503: Service Unavailable
Please let me know if anyone has experienced similar issues!
Thanks,
Claire
...ANSWER
Answered 2020-May-14 at 13:21You may have received that error if your computer was not connected to the Internet or the server was unavailable at the time you ran the test. You can install a local APM server (for Windows or Linux) to avoid any disruptions. Another option is to switch to Python gekko that uses the same underlying APM engine but can run locally with remote=False. Here is the same MPC example in Python gekko.
QUESTION
I'm new to machine learning and I'm doing my "hello world" using sklearn and nltk, but I have problems with the result of the prediction, it always throws me a single value.
I am following a tutorial that I obtained, that has errors and I have been modifying it little by little until in the end it gave me the result, but it is not the expected one.
Attach the tutorial link: https://towardsdatascience.com/text-classification-using-k-nearest-neighbors-46fa8a77acc5
I attach my current code: (always show: "Conditions" as final result)
...ANSWER
Answered 2019-Jun-11 at 20:25After printing out x_train and y_train, you'll figure out the bug.
For some reason, your Y is the feature while your X is your label. If you changed the line x_train, y_train = X, Y to x_train, y_train = Y, X, it would work.
QUESTION
I'm setting up a Jupyter Notebook that apply a Machine learning model from the Ibm watson studio API to some datas that are coming from my Postgresql database.
While reshaping the data to be readable by the API, a JSONDecodeError: Expecting property name enclosed in double quotes: line 1 column 2 (char 1) appeared and I can't solve it.
This is the full traceback:
...ANSWER
Answered 2019-May-26 at 15:04The problem was that json.dumps() was returning (json representation) and the input to the classify_collections() required . Hence we don't use json.dumps() here and simply replace to double quotes(") for the keys and pass to the function.
QUESTION
I have set up a Jupyter Notebook that connect my Postgresql database, call the datas within a table and apply a Machine Learning model from an API to these datas, but I keep getting a TypeError: the JSON object must be str, not 'DetailedResponse'.
My Notebook is set up in 3 cells but I put them together below for clarity:
...ANSWER
Answered 2019-May-23 at 16:38I would add a comment but I do not have the reputation yet
json.loads() takes a string and it doesn't seem your classes variable is a string. You may have to do str(classes) to use it's string representation
QUESTION
I have a set of automated UI tests for our iOS app, written with XCTest. It was required for some test cases to be verified in bad network connection conditions.
I am aware that it is possible to simulate bad network connection by using Network Link Conditioner. I know that you can enable it in settings of a real device and a simulator as well. There seem to be only manual steps involved in enabling and setting the desired state.
But, I was wondering if it was possible to automate this process - how would you go about running a suite of automated tests on the CI (if some of them are to be tested in bad network conditions)?
We are not using real devices for automated testing. I do not have the access to the machines running test suites for CI. I do not have a dedicated machine that could always have NLC enabled and set, nor can I manipulate network itself (router etc). We do not use mocks in our tests.
...ANSWER
Answered 2019-Mar-18 at 14:34Unfortunately, bad network connection/no network connection is not easily testable with XCTest and there is no easy way to set something like this up.
There are (generally) two ways to solve this:
QUESTION
IBM Watson Natural Language Classifier (NLC) limits the text values in the training set to 1024 characters: https://console.bluemix.net/docs/services/natural-language-classifier/using-your-data.html#training-limits .
However the trained model can then classify every text whose length is at most 2048 characters: https://console.bluemix.net/apidocs/natural-language-classifier#classify-a-phrase .
This difference creates some confusion for me: I have always known that we should apply the same pre-processing to both training phase and production phase, therefore if I had to cap off the training data at 1024 chars I would do the same also in production.
Is my reasoning correct or not? Should I cap off the text in production at 1024 chars (as I think I should) or at 2048 chars (maybe because 1024 chars are too few)?
Thank you in advance!
...ANSWER
Answered 2018-Nov-27 at 08:20Recently, I had the same question and one of the answers on an article clarified the same
Currently, the limits are set at 1024 for training and 2048 for testing/classification. The 1024 limit may require some curation of the training data prior to training. Most organizations who require larger character limits for their data end up chunking their input text into 1024 chunks. Additionally, in use cases with data similar to the Airbnb reviews, the primary category can typically be assessed within the first 2048 characters since there is often a lot of noise in lengthy reviews.
Here's the link to the article
QUESTION
I am not very experienced with loops so I am not sure where I went wrong here... I have a dataframe that looks like:
...ANSWER
Answered 2018-Aug-19 at 15:15Based on your loop, it looks like you want to run the regression grouped by year and month and then extract the coefficients in a new dataframe (correct me if thats wrong)
QUESTION
In my table a:
...ANSWER
Answered 2018-Jun-20 at 08:10You can use the cross join that will return your desired result as below:
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Install nlc
You can use nlc like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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