I'm a monster noob though, so probably no idea what I'm on about
The word vectors are in the lg model for sure and that is in my code to where I am saving the doc object. It is the sm model which lacks them. However, upon reading from disk, I don't know if there is something explicitly I have to do to restore the word vectors (that is if the word vectors were really stored to disk in the first place)
I haven't had any response to my question either, so communication in this channel is a bit hit and miss
I trained a custom ner model with 20 labels. However when I am doing inference I am getting one odd tag (O-) for one particular word "Progress",however this not a part of my label tags. I wondered is this because of the out of vocabulary word but in vocabulary I could find the word progress. What am I missing here?
I have some labels for job skills. I don't know how many jds I need to train for IT domain. I need a guarantee that if I train 200 Jds for different job titles will the predictions be almost accurate. I have trained my model with 46 jds and prediction seems to be, in a crude sense, 70 percent accurate. Somebody please help.
Hello, when experimenting with the output model from spacy pretrain command, I found that whenever I use the --init-tok2vec to train a new model along with the word vectors it always results in a model with higher recall but lower precision. When I train without the --init-tok2vec (but just the word2vec vectors), the model scores seem more balanced (and better precision) For the amount of time it takes to train the pretrained model it seems that the effort doesn't pay off that much and results in degraded performance (for my case more false positives) Any thoughts on this??
when I'm installing the most recent version of spacy and loading 'en_core_web_lg' I'm getting a vocab size of 489
import spacy
nlp = spacy.load('en_core_web_md')
len(nlp.vocab)
489
When I do the same for version 2.2.4 I get
import spacy
nlp = spacy.load('en_core_web_lg')
len(nlp.vocab)
1340241
Am I doing something wrong with the newest version???
3 replies
I have text "galaxy in the constellation Cetus"
when i run code:
nlp = spacy.load("en_core_web_sm")
doc = nlp("galaxy in the constellation Cetus")
for chunk in doc.nounchunks:
print(chunk.text+""+root.text+""+root.dep+"__"+root.head.text)
it returns:
the constellationconstellationpobj__in
--- [the, Cetus]
galaxy in the constellation Cetus
[]
but when i ran it on spacy's page online, it return that galaxy is the ROOT
sorry it removed my "__" after chunk.text and after root.text
..... of course + import spacy on the top
i want to extract definition words from sentences.... and noun_chunks help a lot
or keywords
....that sounds better
so you want root.head.text to return galaxy there?
you can paste a code there and run it
oh, you modified the code in that example?
I need a dep parser that works like Stanford NLP dep parser. Spacy, allenai doesn't perform well on text like "creates test cases", "executes the deployment", ... Sentences that Don't have subject. Anybody, please help.
Hi, nlp n00b here (yay)... say I wanted to make a voice-driven calculator, and I have the speech-to-text working, would spacy be a good fit for processing the text, extracting numbers and operators? I see it can classify stuff with like_num, but from what I can see I'd have to combine multiple "like_num" tokens to a single number myself somehow, correct?
I’m able to use the NLP pipelines for all I need but having a difficult time exporting the tokens, entities etc for presenting findings on customer surveys. For example if I want to keep the customer ID, sentiment and the key topics, entities to be able to link those topics to a customer or survey what would be the best use or reference material?
Hey everyone, i'm following: explosion/spaCy#4486, we are exploring high memory usage with spacy, and I was wondering if anyone knows where this valgrind tutorial lives?
I've used valgrind many times so im not anticipating any real issues but, i just would love to see the notes.