- Installation & Quick Start
- Toolkit
- Finetuning on custom data and creating new models
- Applications
- Additional Requirements
- April 2021:
- APIs for creating synthetic data now available for English language. See Synthetic data creation.
neuspellis now available through pip. See Installation through pip- Added support for different transformer-based models such DistilBERT, XLM-RoBERTa, etc. See Finetuning on custom data and creating new models section for more details.
- March, 2021:
- Code-base reformatted. Addressed bug fixes and issues.
- November, 2020:
- Neuspell's
BERTpretrained model is now available as part of huggingface models asmurali1996/bert-base-cased-spell-correction. We provide an example code snippet at ./scripts/huggingface for curious practitioners.
- Neuspell's
- September, 2020:
- This work is accepted at EMNLP 2020 (system demonstrations)
git clone https://github.com/neuspell/neuspell; cd neuspell
pip install -e .To install extra requirements,
pip install -r extras-requirements.txtor individually as:
pip install -e .[elmo]
pip install -e .[spacy]NOTE: For zsh, use ".[elmo]" and ".[spacy]" instead
Additionally, spacy models can be downloaded as:
python -m spacy download en_core_web_smThen, download pretrained models of neuspell following Download Checkpoints
Here is a quick-start code snippet (command line usage) to use a checker model. See test_neuspell_correctors.py for more usage patterns.
import neuspell
from neuspell import available_checkers, BertChecker
""" see available checkers """
print(f"available checkers: {neuspell.available_checkers()}")
# → available checkers: ['BertsclstmChecker', 'CnnlstmChecker', 'NestedlstmChecker', 'SclstmChecker', 'SclstmbertChecker', 'BertChecker', 'SclstmelmoChecker', 'ElmosclstmChecker']
""" select spell checkers & load """
checker = BertChecker()
checker.from_pretrained()
""" spell correction """
checker.correct("I luk foward to receving your reply")
# → "I look forward to receiving your reply"
checker.correct_strings(["I luk foward to receving your reply", ])
# → ["I look forward to receiving your reply"]
checker.correct_from_file(src="noisy_texts.txt")
# → "Found 450 mistakes in 322 lines, total_lines=350"
""" evaluation of models """
checker.evaluate(clean_file="bea60k.txt", corrupt_file="bea60k.noise.txt")
# → data size: 63044
# → total inference time for this data is: 998.13 secs
# → total token count: 1032061
# → confusion table: corr2corr:940937, corr2incorr:21060,
# incorr2corr:55889, incorr2incorr:14175
# → accuracy is 96.58%
# → word correction rate is 79.76%Alternatively, once can also select and load a spell checker differently as follows:
from neuspell import SclstmChecker
checker = SclstmChecker()
checker = checker.add_("elmo", at="input") # "elmo" or "bert", "input" or "output"
checker.from_pretrained()This feature of adding ELMO or BERT model is currently supported for selected models. See List of neural models in the toolkit for details.
If interested, follow Additional Requirements for installing non-neural spell
checkers- Aspell and Jamspell.
pip install neuspellIn v1.0, allennlp library is not automatically installed which is used for models containing ELMO. Hence, to utilize
those checkers, do a source install as in Installation & Quick Start
NeuSpell is an open-source toolkit for context sensitive spelling correction in English. This toolkit comprises of 10 spell checkers, with evaluations on naturally occurring mis-spellings from multiple (publicly available) sources. To make neural models for spell checking context dependent, (i) we train neural models using spelling errors in context, synthetically constructed by reverse engineering isolated mis-spellings; and (ii) use richer representations of the context.This toolkit enables NLP practitioners to use our proposed and existing spelling correction systems, both via a simple unified command line, as well as a web interface. Among many potential applications, we demonstrate the utility of our spell-checkers in combating adversarial misspellings.
Live demo available at http://neuspell.github.io/
CNN-LSTMSC-LSTMNested-LSTMBERTSC-LSTM plus ELMO (at input)SC-LSTM plus ELMO (at output)SC-LSTM plus BERT (at input)SC-LSTM plus BERT (at output)
This pipeline corresponds to the `SC-LSTM plus ELMO (at input)` model.
| Spell Checker |
Word Correction Rate |
Time per sentence (in milliseconds) |
|---|---|---|
Aspell |
48.7 | 7.3* |
Jamspell |
68.9 | 2.6* |
CNN-LSTM |
75.8 | 4.2 |
SC-LSTM |
76.7 | 2.8 |
Nested-LSTM |
77.3 | 6.4 |
BERT |
79.1 | 7.1 |
SC-LSTM plus ELMO (at input) |
79.8 | 15.8 |
SC-LSTM plus ELMO (at output) |
78.5 | 16.3 |
SC-LSTM plus BERT (at input) |
77.0 | 6.7 |
SC-LSTM plus BERT (at output) |
76.0 | 7.2 |
Performance of different correctors in the NeuSpell toolkit on the BEA-60K dataset with real-world spelling
mistakes. ∗ indicates evaluation on a CPU (for others we use a GeForce RTX 2080 Ti GPU).
To download selected checkpoints, select a Checkpoint name from below and then run download. Each checkpoint is associated with a neural spell checker as shown in the table.
| Spell Checker | Class | Checkpoint name | Disk space (approx.) |
|---|---|---|---|
CNN-LSTM |
CnnlstmChecker |
'cnn-lstm-probwordnoise' | 450 MB |
SC-LSTM |
SclstmChecker |
'scrnn-probwordnoise' | 450 MB |
Nested-LSTM |
NestedlstmChecker |
'lstm-lstm-probwordnoise' | 455 MB |
BERT |
BertChecker |
'subwordbert-probwordnoise' | 740 MB |
SC-LSTM plus ELMO (at input) |
ElmosclstmChecker |
'elmoscrnn-probwordnoise' | 840 MB |
SC-LSTM plus BERT (at input) |
BertsclstmChecker |
'bertscrnn-probwordnoise' | 900 MB |
SC-LSTM plus BERT (at output) |
SclstmbertChecker |
'scrnnbert-probwordnoise' | 1.19 GB |
SC-LSTM plus ELMO (at output) |
SclstmelmoChecker |
'scrnnelmo-probwordnoise' | 1.23 GB |
import neuspell
neuspell.seq_modeling.downloads.download_pretrained_model("subwordbert-probwordnoise")Alternatively, download all Neuspell neural models by running the following (available in versions after v1.0):
import neuspell
neuspell.seq_modeling.downloads.download_pretrained_model("_all_")Alternatively,
We curate several synthetic and natural datasets for training/evaluating neuspell models. For full details, check our paper. Run the following to download all the datasets.
cd data/traintest
python download_datafiles.py
See data/traintest/README.md for more details.
Train files are dubbed with names .random, .word, .prob, .probword for different noising
startegies used to create them. For each strategy (see Synthetic data creation), we noise
∼20% of the tokens in the clean corpus. We use 1.6 Million sentences from
the One billion word benchmark dataset as our clean corpus.
In order to setup a demo, follow these steps:
- Do Installation and then install flask requirements as
pip install -e ".[flask]" - Download checkpoints (Note: If you wish to use only one of the neural checkers, you need to manually disable others in the imports of ./scripts/flask-server/app.py)
- Start a flask server in folder ./scripts/flask-server by
running
CUDA_VISIBLE_DEVICES=0 python app.py(on GPU) orpython app.py(on CPU)
This toolkit offers 3 kinds of noising strategies (identfied from existing literature) to generate synthetic parallel
training data to train neural models for spell correction. The strategies include a simple lookup based noisy spelling
replacement (en-word-replacement-noise), a character level noise induction such as swapping/deleting/adding/replacing
characters (en-char-replacement-noise), and a confusion matrix based probabilistic character replacement driven by
mistakes patterns in a large corpus of spelling mistakes (en-probchar-replacement-noise). For full details about these
approaches, checkout our paper.
Following are the corresponding class mappings to utilize the above noise curations. As some pre-built data files are used for some of the noisers, we also provide their approximate disk space.
| Folder | Class name | Disk space (approx.) |
|---|---|---|
en-word-replacement-noise |
WordReplacementNoiser |
2 MB |
en-char-replacement-noise |
CharacterReplacementNoiser |
-- |
en-probchar-replacement-noise |
ProbabilisticCharacterReplacementNoiser |
80 MB |
Following is a snippet for using these noisers-
from neuspell.noising import WordReplacementNoiser
example_texts = [
"This is an example sentence to demonstrate noising in the neuspell repository.",
"Here is another such amazing example !!"
]
word_repl_noiser = WordReplacementNoiser(language="english")
word_repl_noiser.load_resources()
noise_texts = word_repl_noiser.noise(example_texts)
print(noise_texts)Coming Soon ...
from neuspell import BertChecker
checker = BertChecker()
checker.from_pretrained()
checker.finetune(clean_file="sample_clean.txt", corrupt_file="sample_corrupt.txt", data_dir="default")This feature is only available for BertChecker and ElmosclstmChecker.
We now support initializing a huggingface model and finetuning it on your custom data. Here is a code snippet demonstrating that:
First mark your files containing clean and corrupt texts in a line-seperated format
from neuspell.commons import DEFAULT_TRAINTEST_DATA_PATH
data_dir = DEFAULT_TRAINTEST_DATA_PATH
clean_file = "sample_clean.txt"
corrupt_file = "sample_corrupt.txt"from neuspell.seq_modeling.helpers import load_data, train_validation_split
from neuspell.seq_modeling.helpers import get_tokens
from neuspell import BertChecker
# Step-0: Load your train and test files, create a validation split
train_data = load_data(data_dir, clean_file, corrupt_file)
train_data, valid_data = train_validation_split(train_data, 0.8, seed=11690)
# Step-1: Create vocab file. This serves as the target vocab file and we use the defined model's default huggingface
# tokenizer to tokenize inputs appropriately.
vocab = get_tokens([i[0] for i in train_data], keep_simple=True, min_max_freq=(1, float("inf")), topk=100000)
# # Step-2: Initialize a model
checker = BertChecker(device="cuda")
checker.from_huggingface(bert_pretrained_name_or_path="distilbert-base-cased", vocab=vocab)
# Step-3: Finetune the model on your dataset
checker.finetune(clean_file=clean_file, corrupt_file=corrupt_file, data_dir=data_dir)You can further evaluate your model on a custom data as follows:
from neuspell import BertChecker
checker = BertChecker()
checker.from_pretrained(
bert_pretrained_name_or_path="distilbert-base-cased",
ckpt_path=f"{data_dir}/new_models/distilbert-base-cased" # "<folder where the model is saved>"
)
checker.evaluate(clean_file=clean_file, corrupt_file=corrupt_file, data_dir=data_dir)Following usage above, once can now seamlessly utilize multilingual models such as xlm-roberta-base,
bert-base-multilingual-cased and distilbert-base-multilingual-cased on a non-English script.
- Defenses against adversarial attacks in NLP
- example implementation available in folder
./applications/Adversarial-Misspellings-arxiv. See README.md.
- example implementation available in folder
- Improving OCR text correction systems
- Improving grammatical error correction systems
- Improving Intent/Domain classifiers in conversational AI
- Spell Checking in Collaboration and Productivity tools
Requirements for Aspell checker:
wget https://files.pythonhosted.org/packages/53/30/d995126fe8c4800f7a9b31aa0e7e5b2896f5f84db4b7513df746b2a286da/aspell-python-py3-1.15.tar.bz2
tar -C . -xvf aspell-python-py3-1.15.tar.bz2
cd aspell-python-py3-1.15
python setup.py install
Requirements for Jamspell checker:
sudo apt-get install -y swig3.0
wget -P ./ https://github.com/bakwc/JamSpell-models/raw/master/en.tar.gz
tar xf ./en.tar.gz --directory ./
@inproceedings{jayanthi-etal-2020-neuspell,
title = "{N}eu{S}pell: A Neural Spelling Correction Toolkit",
author = "Jayanthi, Sai Muralidhar and
Pruthi, Danish and
Neubig, Graham",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = oct,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-demos.21",
doi = "10.18653/v1/2020.emnlp-demos.21",
pages = "158--164",
abstract = "We introduce NeuSpell, an open-source toolkit for spelling correction in English. Our toolkit comprises ten different models, and benchmarks them on naturally occurring misspellings from multiple sources. We find that many systems do not adequately leverage the context around the misspelt token. To remedy this, (i) we train neural models using spelling errors in context, synthetically constructed by reverse engineering isolated misspellings; and (ii) use richer representations of the context. By training on our synthetic examples, correction rates improve by 9{\%} (absolute) compared to the case when models are trained on randomly sampled character perturbations. Using richer contextual representations boosts the correction rate by another 3{\%}. Our toolkit enables practitioners to use our proposed and existing spelling correction systems, both via a simple unified command line, as well as a web interface. Among many potential applications, we demonstrate the utility of our spell-checkers in combating adversarial misspellings. The toolkit can be accessed at neuspell.github.io.",
}
Link for the publication. Any questions or suggestions, please contact the authors at jsaimurali001 [at] gmail [dot] com
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