langdetect | Port of Google 's language-detection library to Python
kandi X-RAY | langdetect Summary
kandi X-RAY | langdetect Summary
Port of Nakatani Shuyo’s [language-detection] library (version from 03/03/2014) to Python.
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Top functions reviewed by kandi - BETA
- Normalize character .
- Load languages from a directory .
- Detect block of text .
- Removes less frequencies from the word .
- Return the Unicode block name .
- Detect languages in text .
- Initialize the detector factory .
- Return a string representation of the language .
- Get code .
- Get a message value
langdetect Key Features
langdetect Examples and Code Snippets
Lines of Code : 78License : Permissive (Apache-2.0)
CopyPUT _ingest/pipeline/langdetect-pipeline
{
"description": "A pipeline to do whatever",
"processors": [
{
"langdetect" : {
"field" : "my_field",
"target_field" : "language"
}
}
]
}
PUT /my-index/my-type/1?pip import spacy
from spacy.tokens import Doc, Span
from spacy_langdetect import LanguageDetector
# install using pip install googletrans
from googletrans import Translator
nlp = spacy.load("en")
def custom_detection_function(spacy_object):
# custom import spacy
from spacy_langdetect import LanguageDetector
nlp = spacy.load("en")
nlp.add_pipe(LanguageDetector(), name="language_detector", last=True)
text = "This is English text. Er lebt mit seinen Eltern und seiner Schwester in Berlin. Yo me divi df = pd.read_csv('Sample.csv')
def f(x):
try:
detect(x)
return False
except:
return True
s = df.loc[df.text.apply(f), 'text']
df = pd.read_csv('Sample.csv')
def f1(x):
try:
df_new = df[df.input_text.apply(detect).eq('en')]
import spacy
from langdetect import detect
nlp={}
for lang in ["en", "es", "pt", "ru"]: # Fill in the languages you want, hopefully they are supported by spacy.
if lang == "en":
nlp[lang]=spacy.load(lang + '_core_web_lg')
import json
from langdetect import detect
with open("kiel.json", 'r') as f:
data = json.loads(f.read())
for item in data['alerts']:
item['lang'] = detect(item['description'])
#'ADDED_KEY' = 'lang' - should be added as a data fiefor item in data["alerts"]:
item["ADDED_KEY"] = "ADDED_VALUE"df['Text'].str.replace('[^0-9a-zA-Z.]|[.]+$',' ').str.replace('\s{2,}',' ')
0 The is in with a... KIDS
1 BoneMA Synthesis and Characteriof a M
2 Law Translate
conda install -n base nb_conda_kernels
conda install -n MYENV ipykernel
jupyter-notebook # Run this from the base environment
conda activate MYENV # or source activate MYENV
python -m pip install MYPACKAGE.whl
Community Discussions
Trending Discussions on langdetect
QUESTION
I have a pandas df which has 6 columns, the last one is input_text. I want to remove from df all rows that have non-english text in that column. I would like to use langdetect's detect function.
Some template
...ANSWER
Answered 2021-Jun-01 at 13:31You can do it as below on your df and get all the rows with english text in the input_text column:
QUESTION
How do I provide an OpenNLP model for tokenization in vespa? This mentions that "The default linguistics module is OpenNlp". Is this what you are referring to? If yes, can I simply set the set_language index expression by referring to the doc? I did not find any relevant information on how to implement this feature in https://docs.vespa.ai/en/linguistics.html, could you please help me out with this?
Required for CJK support.
...ANSWER
Answered 2021-May-20 at 16:25Yes, the default tokenizer is OpenNLP and it works with no configuration needed. It will guess the language if you don't set it, but if you know the document language it is better to use set_language (and language=...) in queries, since language detection is unreliable on short text.
However, OpenNLP tokenization (not detecting) only supports Danish, Dutch, Finnish, French, German, Hungarian, Irish, Italian, Norwegian, Portugese, Romanian, Russian, Spanish, Swedish, Turkish and English (where we use kstem instead). So, no CJK.
To support CJK you need to plug in your own tokenizer as described in the linguistics doc, or else use ngram instead of tokenization, see https://docs.vespa.ai/documentation/reference/schema-reference.html#gram
n-gram is often a good choice with Vespa because it doesn't suffer from the recall problems of CJK tokenization, and by using a ranking model which incorporates proximity (such as e.g nativeRank) you'l still get good relevancy.
QUESTION
I am writing some code to perform Named Entity Recognition (NER), which is coming along quite nicely for English texts. However, I would like to be able to apply NER to any language. To do this, I would like to 1) identify the language of a text, and then 2) apply the NER for the identified language. For step 2, I'm doubting to A) translate the text to English, and then apply the NER (in English), or B) apply the NER in the language identified.
Below is the code I have so far. What I would like is for the NER to work for text2, or in any other language, after this language is first recognized:
...ANSWER
Answered 2021-Apr-01 at 18:38Spacy needs to load the correct model for the right language.
See https://spacy.io/usage/models for available models.
QUESTION
I'm trying to use the spacy_langdetect package and the only example code I can find is (https://spacy.io/universe/project/spacy-langdetect):
...ANSWER
Answered 2021-Mar-20 at 23:11With spaCy v3.0 for components not built-in such as LanguageDetector, you will have to wrap it into a function prior to adding it to the nlp pipe. In your example, you can do the following:
QUESTION
I have weather alert data like
...ANSWER
Answered 2021-Feb-08 at 09:24You just need to iterate over the dictionary key alerts and add key, value to every item(which is a dictionary).
QUESTION
Given this dataframe (which is a subset of mine):
username user_message Polop I love this picture, which is very beautiful Artil Meh Artingo Es un cuadro preciosa, me recuerda a mi infancia. Zona I like it Soi Yuck, to say I hate it would be a euphemism Iyu NaNWhat I'm trying to do is drop rows for which a number of words (tokens) is less than 5 words, and that are not written in English. I'm not familiar with pandas, so I imagined a not so pretty solution:
...ANSWER
Answered 2021-Jan-23 at 22:43Use:
QUESTION
From today, I started getting error while installing modules from requirements.txt, I tried to find the error module and remove it but I couldn't find.
ANSWER
Answered 2021-Jan-17 at 12:41Create a list of all the dependencies and run the following code.
QUESTION
I am trying to run language detection on a Series object in a pandas dataframe. However, I am dealing with millions of rows of string data, and the standard Python language detection librarieslangdetect and langid are too slow, and after hours of running it still hasn't completed.
I set up my code as follows:
...ANSWER
Answered 2020-Oct-30 at 08:42You could use swifter to make your df.apply() more efficient. In addition to that, you might want to try whatthelang library which should be more efficient than langdetect.
QUESTION
I would need to remove from rows words which are not in English and specific symbols, like | or -, and three dots (...) if they are at the end of each row. In order to do this, I was considering to use googletranslate or langdetect packages in Python for detecting and removing from text words not in English, and create a list for symbols.
To apply them, I was doing as follows:
...ANSWER
Answered 2020-Dec-01 at 11:46like regex
QUESTION
some background: currently I am querying 4Mio rows (with 50 columns) from a MS SQL server with dbatools into a PSObject (in Batch 10.000 rows each query), processing the data with PowerShell (a lot of RegEx stuff) and writing back into a MariaDb with SimplySql. In average i get approx. 150 rows/sec. Had to use a lot of tricks (Net's Stringbuilder etc.) for this performance, its not that bad imho
As new requirements I want to detect the language of some text cells and I have to remove personal data (name & address). I found some good python libs (spacy and pycld2) for that purpose. I made tests with pycld2 - pretty good detection.
Simplified code for clarification (hint:I am a python noob):
...ANSWER
Answered 2020-Nov-29 at 21:30The following simplified example shows you how you can pass multiple [pscustomobject] ([psobject]) instances from PowerShell to a Python script (passed as a string via -c in this case):
by using JSON as the serialization format, via
ConvertTo-Json...... and passing that JSON via the pipeline, which Python can read via stdin (standard input).
Important:
Character encoding:
PowerShell uses the encoding specified in the
$OutputEncodingpreference variable when sending data to external programs (such as Python), which commendably defaults to BOM-less UTF-8 in PowerShell [Core] v6+, but regrettably to ASCII(!) in Windows PowerShell.Just like PowerShell limits you to sending text to an external program, it also invariably interprets what it receives as text, namely based on the encoding stored in
[Console]::OutputEncoding; regrettably, both PowerShell editions as of this writing default to the system's OEM code page.To both send and receive (BOM-less) UTF-8 in both PowerShell editions, (temporarily) set
$OutputEncodingand[Console]::OutputEncodingas follows:
$OutputEncoding = [Console]::OutputEncoding = [System.Text.Utf8Encoding]::new($false)
If you want your Python script to also output objects, again consider using JSON, which on the PowerShell you can parse into objects with
ConvertFrom-Json.
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