I currently have an LSTM which uses sequence length as input, but this only allows the LSTM to predict when the input length is equal to the used sequence lengt
I have bunch of text data describing people's eduation. I have already done some basic NLP processing to those text data. An example would be this : XXX receive
I have succesfully trained a text emotion classifier fine-tuning a RoBERTa language model, mostly using a helpful notebook found online. Now I am trying to writ
I have just started a project in NLP. Suppose I have a graph for each word that shows the polarity distribution of sentiments for that word in different sentenc
I would like an example of using Prodigy to train a dataset (text file with some named entities). This file would be in Portuguese. The idea would be to train a
Hi stackoverflow community! Long-time reader but first-time poster. I'm currently trying my hand at NLP and after reading a few forum posts touching upon this t
I am new to Prodigy and spaCy as well as CLI coding. I'd like to use Prodigy to label my data for an NER model, and then use spaCy in python to create models.
I trained a supervised model in FastText using the Python interface and I'm getting weird results for precision and recall. First, I trained a model: model = fa
I have tried to remove non-English words from a text. Problem many other words are absent from the NLTK words corpus. My code: import pandas as pd lst = ['
Assume there is a large record of all different kinds of inter-employee and customer communications (e.g. mails, chat transcripts, OCRed letters) which should b
I am trying to use nlp for german language but it does not work! I was making the pipeline and then NER to find the entity of each element in sentence which is
I have imported ` from itertools import chain import nltk import sklearn import scipy.stats import sklearn_crfsuite from sklearn_crfsuite import scorers,CR
I'm trying to create a dataframe containing specific keywords-in-context using the kwic() function, but unfortunately, I'm running into some error when attempti
In the paper describing BERT, there is this paragraph about WordPiece Embeddings. We use WordPiece embeddings (Wu et al., 2016) with a 30,000 token vocab
This is a pretty dumb question, but I couldn't find anywhere, so I will take my chances in here... I'm building a classifier using CatBoost. Since this is a NLP
I am trying to calculate the Meteor score for the following: print (nltk.translate.meteor_score.meteor_score( ["this is an apple", "that is an apple"], "an
I am trying to run the textEmbed function in R. Set up needed: require(quanteda) require(quanteda.textstats) require(udpipe) require(reticulate) #udpi
I have made a resume parser but to parse my resumes, I am using a for loop to run my parse function over each resume. Is there a way to vectorize this approach?
I would like to store vector features, like Bag-of-Words or Word-Embedding vectors of a large number of texts, in a dataset, stored in a SQL Database. What're t
I am working with the R programming language. I have the following data: text = structure(list(id = 1:8, reviews = c("I guess the employee decided to buy their