Comments (3)
doc_length is len(doc)
, I.e. the number of tokens. However, it is not the same as n_tokens
(from descriptive stats) as
it filters punctuations and similar non-word tokens. Hopefully this answer your question.
@HLasse we should probably make n_tokens
--> n_words
and make a n_tokens
in descriptive stats as well which is equal to len(doc)
. In the quality component, we should rename the doc_length
to n_tokens
. Do you agree with this?
from textdescriptives.
Thank you for your reply.
doc_length is len(doc), I.e. the number of tokens.
Is this the lengh of SpaCy tokens?
However, it is not the same as n_tokens (from descriptive stats) as it filters punctuations and similar non-word tokens.
Could you please tell which one does what? Does n_tokens
filer puncts?
from textdescriptives.
Yes n_tokens filters puncts, while doc_length doesn't filter anything.
And yes len(doc)==doc_length is the number of spacy tokens. That means that it is dependent on the tokenizer that you use.
from textdescriptives.
Related Issues (20)
- Avoid calculating metrics multiple times HOT 3
- Allow threshold for the quality pipeline to be changed after data thresholds have been set. HOT 1
- Make output of doc._.quality specify if each filter was passed HOT 3
- New Metric: hertz component HOT 2
- New Metric: Approximate entropy HOT 2
- Add proportion of word in vocabuary
- Add Unigram entropy
- Ensure component consistency HOT 4
- Quick start not working as expected HOT 4
- Quickstart broke on Spacy example HOT 1
- Issue with tests HOT 1
- Failing test HOT 5
- Demos / Browser-Based Usage HOT 34
- Fails for empty strings HOT 4
- Support transformer models
- Pyphen does not support all languages that spaCy does HOT 2
- Listed metrics deviate between extraction functions in docs HOT 7
- References for readability metrics HOT 4
- Croatian language requires `lexeme_prob` table HOT 6
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