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* refactored code into base class; added quantization method; separated non_neural models into a different folder and will load conditionally;

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NeuSpell: A Neural Spelling Correction Toolkit





  • March, 2021:
    • Code-base reformatted. Addressed bug fixes and issues.
  • November, 2020:
    • Neuspell's BERT pretrained model is now available as part of huggingface models as murali1996/bert-base-cased-spell-correction. We provide an example code snippet at ./scripts/huggingface for curious practitioners.
  • September, 2020:
    • This work is accepted at EMNLP 2020 (system demonstrations)


git clone; cd neuspell
pip install -e .

To install extra requirements,

pip install -r extras-requirements.txt

or 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_sm

Then, download pretrained models of neuspell following Download Checkpoints

Here is a quick-start code snippet (command line usage) to use a checker model. See 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()

""" 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"]
# → "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"

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.

Installation through pip

pip install neuspell

In 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

List of neural models in the toolkit:

This pipeline corresponds to the `SC-LSTM plus ELMO (at input)` model.

Time per
(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).

Download Checkpoints

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


Alternatively, download all Neuspell neural models by running the following (available in versions after v1.0):

import neuspell




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

See data/traintest/ 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.

Demo Setup

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/
  • Start a flask server in folder ./scripts/flask-server by running CUDA_VISIBLE_DEVICES=0 python (on GPU) or python (on CPU)

Synthetic data creation


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")
noise_texts = word_repl_noiser.noise(example_texts)
Other languages
Coming Soon ...

Finetuning on custom data and creating new models

Finetuning on top of neuspell pretrained models

from neuspell import BertChecker

checker = BertChecker()
checker.finetune(clean_file="sample_clean.txt", corrupt_file="sample_corrupt.txt", data_dir="default")

This feature is only available for BertChecker and ElmosclstmChecker.

Training other Transformers/BERT-based models

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

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()
    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)

Multilingual Models

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.

Potential applications for practitioners

  • Defenses against adversarial attacks in NLP
    • example implementation available in folder ./applications/Adversarial-Misspellings-arxiv. See
  • Improving OCR text correction systems
  • Improving grammatical error correction systems
  • Improving Intent/Domain classifiers in conversational AI
  • Spell Checking in Collaboration and Productivity tools

Additional requirements

Requirements for Aspell checker:

tar -C . -xvf aspell-python-py3-1.15.tar.bz2
cd aspell-python-py3-1.15
python install

Requirements for Jamspell checker:

sudo apt-get install -y swig3.0
wget -P ./
tar xf ./en.tar.gz --directory ./


    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 = "",
    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",

Link for the publication. Any questions or suggestions, please contact the authors at jsaimurali001 [at] gmail [dot] com