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https://nlpaug.readthedocs.io/en/latest/augmenter/word/spelling.html
Here is the code:
nlpaug/nlpaug/model/word_dict/spelling.py
Lines 21 to 43
in
7618bd9
def read(self, model_path):...
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I'm trying to use nlpaug
python 3.9.12
i installed :
! pip install -q numpy
! pip install -q requests==2.28.1
! pip install -q nlpaug==1.1.11
when i call
import nlpaug.augmenter.word as naw
import nlpaug.flow as nafc
from nlpaug.util import Action
i get this issue :
----------------------------...
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After installing nlpaug version 1.1.11 and importing the library, I get a SIGILL and the process stops:
import nlpaug.augmenter.word as naw
Process finished with exit code 132 (interrupted by signal 4: SIGILL)
Python version 3.7.9
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1 aug = naw.WordEmbsAug(model_type='word2vec', model_path=model_path, action="insert")
2 augmented_text = aug.augment(text)
3 print("Original:")
4 print(text)
5 print("Augmented Text:")
/usr/local/lib/python3.7/dist-packages/nlpaug/augmenter/word/word_embs.py in init(self, model_type, model_path, mo...
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In the notebook with examples https://github.com/makcedward/nlpaug/blob/master/example/textual_augmenter.ipynb
nas.AbstSummAug used with num_beam parameter which doesn't exist
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First of all let me thank you for this amazing library !
I am using nlpaug.augmenter.char to augment my text data in different ways and I noticed that when I call the .augment() method with n=1 it returns a string:
import nlpaug.augmenter.char as nac
aug_typo = nac.KeyboardAug(...)
augmentations = a...
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Downloading files from Google Drive no longer works with the provided script.
Below is a test for downloading the Word2Vec weights. Similar happens with other files.
from nlpaug.util.file.download import DownloadUtil
DownloadUtil.download_from_google_drive(
_id='0B7XkCwpI5KDYNlNUTTlSS21p...
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Hi, I wanted to use local models downloaded from HuggingFaces, but I haven't seen any issue or anything in the documentation about this. Is this doable right now? You can use a word2vec model saved locally, but can you use local models with BackTranslation for example?
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Hi! I can see that BackTranslationAug has an argument max_length to specify the maximum length of the output text, but I am wondering if there is any way to specify the max length of the input text, similar to how HF Tokenizers lets you truncate text with a tokenizer.
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If I'd like not to change some specified words in one text when conducting data augmentation, could I achieve this with 'nlpaug'?
For example, 'nlpaug' augmented the text 'The quick brown fox jumps over the lazy dog.' into 'The quick 40Mbtye fox jumps particular the lazy L6Y1XGT.', in which words 'f...