Sentiment | example project using a feed-forward neural network | Machine Learning library
kandi X-RAY | Sentiment Summary
kandi X-RAY | Sentiment Summary
Our objective is to predict the sentiment (either positive or negative) of a blob of English text using machine learning. We sometimes refer to this type of ML as Natural Language Processing (NLP) because it involves machines making sense of language. The dataset provided to us contains 25,000 training and 25,000 testing samples each consisting of a blob of English text reviewing a movie on the IMDB website. The samples have been labeled positive or negative based on the score (1 - 10) the reviewer gave to the movie. From there, we'll use the IMDB dataset to train a multilayer neural network to predict the sentiment of any English text we show it. "Story of a man who has unnatural feelings for a pig. Starts out with a opening scene that is a terrific example of absurd comedy. A formal orchestra audience is turned into an insane, violent mob by the crazy chantings of it's singers. Unfortunately it stays absurd the WHOLE time with no general narrative eventually making it just too off putting. ...".
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Lines of Code : 113License : Permissive (MIT)
Copy$samples = $labels = [];
foreach (['positive', 'negative'] as $label) {
foreach (glob("test/$label/*.txt") as $file) {
$samples[] = [file_get_contents($file)];
$labels[] = $label;
}
}
use Rubix\ML\Datasets\Labeled;
$dataset use Rubix\ML\PersistentModel;
use Rubix\ML\Pipeline;
use Rubix\ML\Transformers\TextNormalizer;
use Rubix\ML\Transformers\WordCountVectorizer;
use Rubix\ML\Transformers\TfIdfTransformer;
use Rubix\ML\Transformers\ZScaleStandardizer;
use Rubix\ML\Other $samples = $labels = [];
foreach (['positive', 'negative'] as $label) {
foreach (glob("train/$label/*.txt") as $file) {
$samples[] = [file_get_contents($file)];
$labels[] = $label;
}
}
use Rubix\ML\Datasets\Labeled;
$datase Community Discussions
Trending Discussions on Sentiment
QUESTION
I am working on a CNN Sentiment analysis machine learning model which uses the IMDb dataset provided by the Torchtext library. On one of my lines of code
vocab = Vocab(counter, min_freq = 1, specials=('\', '\', '\', '\'))
I am getting a TypeError for the min_freq argument even though I am certain that it is one of the accepted arguments for the function. I am also getting UserWarning Lambda function is not supported for pickle, please use regular python function or functools partial instead. Full code
...ANSWER
Answered 2022-Apr-04 at 09:26As https://github.com/pytorch/text/issues/1445 mentioned, you should change "Vocab" to "vocab". I think they miss-type the legacy-to-new notebook.
correct code:
QUESTION
What is the loss function used in Trainer from the Transformers library of Hugging Face?
I am trying to fine tine a BERT model using the Trainer class from the Transformers library of Hugging Face.
In their documentation, they mention that one can specify a customized loss function by overriding the compute_loss method in the class. However, if I do not do the method override and use the Trainer to fine tine a BERT model directly for sentiment classification, what is the default loss function being use? Is it the categorical crossentropy? Thanks!
ANSWER
Answered 2022-Mar-23 at 10:12It depends!
Especially given your relatively vague setup description, it is not clear what loss will be used. But to start from the beginning, let's first check how the default compute_loss() function in the Trainer class looks like.
You can find the corresponding function here, if you want to have a look for yourself (current version at time of writing is 4.17). The actual loss that will be returned with default parameters is taken from the model's output values:
loss = outputs["loss"] if isinstance(outputs, dict) else outputs[0]
which means that the model itself is (by default) responsible for computing some sort of loss and returning it in outputs.
Following this, we can then look into the actual model definitions for BERT (source: here, and in particular check out the model that will be used in your Sentiment Analysis task (I assume a BertForSequenceClassification model.
The code relevant for defining a loss function looks like this:
QUESTION
Bullish
ANSWER
Answered 2022-Feb-16 at 13:26You don't need the "text()" in the xpath:
QUESTION
I have a table as shown in the image, where each comment has a publication date, with year, month, day and time, I would like to add the sentiment values by day.
this is how the table is composed
serie <- data.frame(comments$created_time,sentiment2$positive-sentiment2$negative)
ANSWER
Answered 2022-Feb-01 at 18:58Using dplyr you can do:
QUESTION
I'm trying to average some metrics by user id, and by hour, in a select statement.
Would love some help understanding the best approach to doing this without using parameters in a python script :P
this is the current query,
...ANSWER
Answered 2022-Jan-11 at 13:47In posgresql you can group by year, month, day and hours using,
QUESTION
This follows as a result of experimenting on Compiler Explorer as to ascertain the compiler's (rustc's) behaviour when it comes to the log2()/leading_zeros() and similar functions. I came across this result with seems exceedingly both bizarre and concerning:
Code:
...ANSWER
Answered 2021-Dec-26 at 01:56Old x86-64 CPUs don't support lzcnt, so rustc/llvm won't emit it by default. (They would execute it as bsr but the behavior is not identical.)
Use -C target-feature=+lzcnt to enable it. Try.
More generally, you may wish to use -C target-cpu=XXX to enable all the features of a specific CPU model. Use rustc --print target-cpus for a list.
In particular, -C target-cpu=native will generate code for the CPU that rustc itself is running on, e.g. if you will run the code on the same machine where you are compiling it.
QUESTION
my problem is the follow: I want to do a sentiment analysis on Italian tweet and I would to tokenise and lemmatise my Italian text in order to find new analysis dimension for my thesis. The problem is that I would like to tokenise my hashtag, splitting also the composed one. For example if I have #nogreenpass, I would have also without the # symbol, because the sentiment of the phrase would be better understand with all word of the text. How could I do this? I tried with sapCy, but I have no results. I created a function to clean my text, but I can't have the hashtag in the way I would. I'm using this code:
...ANSWER
Answered 2021-Dec-10 at 14:34You can tweak your current clean_code with
QUESTION
I want to compare 2 column. If same will True if not same will False like this:
filtering lemmatization check [hello, world] [hello, world] True [grape, durian] [apple, grape] FalseThe output from my code is all False. But, the data actually is different. Why?
You can get my data github
...ANSWER
Answered 2021-Nov-29 at 13:47Here is difference between columns - in one column missing '' around strings, possible solution is convert both columns to lists, for comapre use Series.eq (working like ==):
QUESTION
This is the server
...ANSWER
Answered 2021-Nov-03 at 05:42Try this.
QUESTION
Hey guys so I'm building a websocket using flask and I'm currently creating a new thread for every request so that thread can do some heavy processing and not take too much time to return something from the request. The problem is that at some point i get so many threads open that it starts causing problems and i was wondering if i could setup some queue in flask to limit the app for creating like 8 thread each time only.
My code:
...ANSWER
Answered 2021-Oct-29 at 13:05This is a pretty common problem when multi-threading, the solution you need here is called the producer-consumer model where there is a producer (the entity that creates work) then there is a (threadsafe) queue where this work is pushed into, then there are consumers (worker threads) that one by one pop out work from the queue until the queue is empty.
Doing this limits the number of worker threads. One neat way to do this is to use the concurrent.futures library available in python. @aaron as given an appropriate link for that.
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