Is there a way to create a TextBlob without having to hard code it? I tried for several hours to submit an array of strings to it but without any success. Any help is appreciated. Source: Python..
I need to get a confidence score for the tags predicted by NER ‘de_core_news_lg’ model. There was a well known solution to the problem in the Spacy 2: nlp = spacy.load(‘de_core_news_lg’) doc = nlp(‘ich möchte mit frau Mustermann in der Musterbank sprechen’) text = content doc = nlp.make_doc(text) beams = nlp.entity.beam_parse([doc], beam_width=16, beam_density=0.0001) for score, ..
I am try to make a project in which farming advices are generated based on weather condition on a specific district. I have a sample dataset for now, as shown below. state district month rainfall max_temp min_temp max_rh min_rh wind_speed advice Orissa Kendrapada february 0.0 34.6 19.4 88.2 29.6 12.0 chances of foot rot disease ..
I’m trying to write a code to remove all the words coming after a certain word from a set of words as below. I tested out but since I’m quite new to python I’m not sure if this code might cause a problem. Can anyone review this code and point any possible risk from using ..
I tried using regular expressions but it doesn’t do it with any context Examples:: "250 kg Oranges for Sale" "I want to sell 100kg of Onions at 100 per kg" Source: Python..
I am trying to run a supervised model on Sagemaker using BlazingText model. Here is my code for running the estimator, hyperparameter tuning, deploying to container and model invocation. I am seeing "Attribute Error: cant set attribute". bt = sagemaker.estimator.Estimator(container, role, instance_count=1, instance_type=’ml.m5.large’, input_mode=’File’, output_path=s3_output_location) bt.set_hyperparameters(mode=’supervised’) Code for model tuning bt.fit(inputs=s3_channels) Code to deploy to ..
Say there is a big list of unigrams + ngrams: ["car", "car wash", "housing", "hotel room", "shopping", "customer card", …] And there is some text string s . Question would be what is the most efficient way to calculate how many times each ngram occurs in text (exact match)? Counting for how many times each ..
NLP/K-MEANS/PYTHON Hi all, Im currently doing a short-text clustering task of NLP. Im trying to cluster the short text by K-means. I have completed embedding the sentences(by using GLOVE) and feed to CNN, and then I used K-means to do clustering. In order to evalute the clusters better and display the result to people who ..
for example I want to save inevitable, unavoidable, certain, sure = "necessary" if mentioned words are using in my giving sentence, so my program automatically change these words into "necessary" and give me sentence for example it is inevitable or unavoidable or certain or sure, that person age should be 18 so my python program ..
I have a text full of emojis. import matplotlib.pyplot as plt from wordcloud import WordCloud wordcloud = WordCloud().generate(text) plt.imshow(wordcloud) plt.axis("off") plt.show() when i try to get a wordcloud for those emojis like the code above, it returns an error like this: ValueError: We need at least 1 word to plot a word cloud, got 0. ..