corpora | Public repository for Coptic SCRIPTORIUM Corpora Releases
kandi X-RAY | corpora Summary
kandi X-RAY | corpora Summary
This is the public repository for Coptic SCRIPTORIUM corpora. The documents are available in multiple formats: CoNLL-U, relANNIS, PAULA XML, TEI XML, and TreeTagger SGML (*.tt). The *.tt files generally contain the most complete representations of document annotations, though note that corpus level metadata is only included in the PAULA XML and relANNIS versions. Corpora can be searched, viewed, and queried with complex queries Project homepage is
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QUESTION
I would greatly appreciate any feedback you might offer regarding the issue I am having with my Word Prediction Shiny APP Code for the JHU Capstone Project.
My UI code runs correctly and displays the APP. (see image and code below)
Challenge/Issue: My problem is that after entering text into the "Text input" box of the APP, my server.R code does not return the predicted results.
Prediction Function:
When I run this line of code in the RConsole -- predict(corpus_train,"case of") -- the following results are returned: 1 "the" "a" "beer"
When I use this same line of code in my server.r Code, I do not get prediction results.
Any insight suggestions and help would be greatly appreciated.
...ANSWER
Answered 2021-Apr-27 at 06:46Eiterh you go for verbatimTextOutput
and renderPrint
(you will get a preformatted output) OR for textOutput
and renderText
and textOutput
(you will get unformatted text).
QUESTION
I cannot pass Story #5: "When I click a .nav-link button in the nav element, I am taken to the corresponding section of the landing page." I have all of my href
attributes set to the corresponding id
attributes and when i click on them they take me to the correct section of the page, but I am still failing this test... What am I Doing Wrong???
The code I wrote is below:
...ANSWER
Answered 2021-May-28 at 01:41The error reads
QUESTION
I managed to generate vectors for every sentence in my two corpora and calculate the Cosine Similarity between every possible pair (dot product):
...ANSWER
Answered 2021-May-22 at 14:52You might use, np.argsort(...)
for sorting,
QUESTION
I am using Python with TextBlob for sentiment analysis. I want to deploy my app (build in Plotly Dash) to Google Cloud Run with Google Cloud Build (without using Docker). When using locally on my virtual environment all goes fine, but after deploying it on the cloud the corpora is not downloaded. Looking at the requriements.txt file, there was also no reference to this corpora.
I have tried to add python -m textblob.download_corpora
to my requriements.txt file but it doesn't download when I deploy it. I have also tried to add
ANSWER
Answered 2021-May-10 at 18:00Since Cloud Run creates and destroys containers as needed for your traffic levels you'll want to embed your corpora in the pre-built container to ensure a fast cold start time (instead of downloading it when the container starts)
The easiest way to do this is add another line inside of a docker file that downloads and installs the corpora at build time like so:
QUESTION
I have te same error as this thread : ValueError: cannot compute LDA over an empty collection (no terms) but the solution needed isn't the same.
I'm working on a notebook with Sklearn, and I've done an LDA and a NMF.
I'm now trying to do the same using Gensim: https://radimrehurek.com/gensim/auto_examples/tutorials/run_lda.htm
Here is a piece of code (in Python) from my notebook of what I'm trying to do :
...ANSWER
Answered 2021-Apr-28 at 13:30Just don't use id2token.
Your model should be :
QUESTION
from nltk.tokenize import RegexpTokenizer
#from stop_words import get_stop_words
from gensim import corpora, models
import gensim
import os
from os import path
from time import sleep
filename_2 = "buisness1.txt"
file1 = open(filename_2, encoding='utf-8')
Reader = file1.read()
tdm = []
# Tokenized the text to individual terms and created the stop list
tokens = Reader.split()
#insert stopwords files
stopwordfile = open("StopWords.txt", encoding='utf-8')
# Use this to read file content as a stream
readstopword = stopwordfile.read()
stop_words = readstopword.split()
for r in tokens:
if not r in stop_words:
#stopped_tokens = [i for i in tokens if not i in en_stop]
tdm.append(r)
dictionary = corpora.Dictionary(tdm)
corpus = [dictionary.doc2bow(i) for i in tdm]
sleep(3)
#Implemented the LdaModel
ldamodel = gensim.models.ldamodel.LdaModel(corpus, num_topics=10, id2word = dictionary)
print(ldamodel.print_topics(num_topics=1, num_words=1))
...ANSWER
Answered 2021-Apr-29 at 09:35This is almost certainly a duplicate, but use this instead:
QUESTION
I have a working topic model called model
, with the following settings:
ANSWER
Answered 2021-Apr-27 at 16:50Following https://radimrehurek.com/gensim/models/ldamodel.html, all the topics that have probability lower than the parameter minimum_probability will be discarded (default: 0.01). If you set minimum_probability=0, you will get the whole topic probability distribution of the document (in the form of tuples).
As for your second question, I believe that the only way that allows you to obtain the topic-document distribution is the one above. So, you need to iterate over all the documents of your dataset to get the document-topic matrix.
QUESTION
I am testing the google drive api V3 files.list method after testing the API on the Google site
Try me I received the expected results.
...ANSWER
Answered 2021-Apr-09 at 20:10files = results.get('files', [])
returns the files
object of the whole response, which should be in results
on the previous line.
To print the whole response, return results
instead of files
.
Reference:
QUESTION
I'm trying to retrieve all Google Drive files that where created by users within my organisation (Domain-wide delegation and the drive role are set).
...ANSWER
Answered 2021-Mar-30 at 10:11The way domain wide delegation works is that it allows the service account to impersonate or act like a single user. The service account doesn't just get out write access to everyone's data.
This is due to a limitation on how the APIS work. Each request to an api must include a authorization header which contains an access token granting access to a single users data. If you want to access John's data then you need an access token for John, this will not give you access to John and Janes data.
So for the service account to work you need to be able to delegate to John then send another request deligateing to Jane to access her data.
THis may not be optimal for your application but its how it works. You will need to delegate to each user one at a time.
QUESTION
I need to extract the text (header and its paragraphs) that match a header level 1 string passed to the python function. Below an example mardown text where I'm working:
...ANSWER
Answered 2021-Mar-21 at 12:38If I understand correctly, you are trying to capture only one # symbol at the beginning of each line.
The regular expression that helps you solve the issue is: r"(?:^|\s)(?:[#]\ )(.*\n+##\ ([^#]*\n)+)"
. The brackets isolate the capturing or non capturing groups. The first group (?:^|\s)
is a non capturing group, because it starts with a question mark. Here you want that your matched string starts with the beginning of a line or a whitespace, then in the second group ([#]\ )
, [#]
will match exactly one # character. \
matches the space between the hash and the h1 tag text content. finally you want to match any possible character until the end of the line so you use the special characther .
, which identifies any character, followed by +
that will match any repetition of the previous matched character.
This is probably the code snippet you are looking for, I tested it with the same sample test you used.
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