Grammatical Error Correction with OpenVINO

This tutorial is also available as a Jupyter notebook that can be cloned directly from GitHub. See the installation guide for instructions to run this tutorial locally on Windows, Linux or macOS.


AI-based auto-correction products are becoming increasingly popular due to their ease of use, editing speed, and affordability. These products improve the quality of written text in emails, blogs, and chats.

Grammatical Error Correction (GEC) is the task of correcting different types of errors in text such as spelling, punctuation, grammatical and word choice errors. GEC is typically formulated as a sentence correction task. A GEC system takes a potentially erroneous sentence as input and is expected to transform it into a more correct version. See the example given below:

Input (Erroneous)

Output (Corrected)

I like to rides my bicycle.

I like to ride my bicycle.

As shown in the image below, different types of errors in written language can be corrected.



This tutorial shows how to perform grammatical error correction using OpenVINO. We will use pre-trained models from the Hugging Face Transformers library. To simplify the user experience, the Hugging Face Optimum library is used to convert the models to OpenVINO™ IR format.

It consists of the following steps:

How does it work?

A Grammatical Error Correction task can be thought of as a sequence-to-sequence task where a model is trained to take a grammatically incorrect sentence as input and return a grammatically correct sentence as output. We will use the FLAN-T5 model finetuned on an expanded version of the JFLEG dataset.

The version of FLAN-T5 released with the Scaling Instruction-Finetuned Language Models paper is an enhanced version of T5 that has been finetuned on a combination of tasks. The paper explores instruction finetuning with a particular focus on scaling the number of tasks, scaling the model size, and finetuning on chain-of-thought data. The paper discovers that overall instruction finetuning is a general method that improves the performance and usability of pre-trained language models.

flan-t5 training

flan-t5 training

For more details about the model, please check out paper, original repository, and Hugging Face model card

Additionally, to reduce the number of sentences required to be processed, you can perform grammatical correctness checking. This task should be considered as a simple binary text classification, where the model gets input text and predicts label 1 if a text contains any grammatical errors and 0 if it does not. You will use the roberta-base-CoLA model, the RoBERTa Base model finetuned on the CoLA dataset. The RoBERTa model was proposed in RoBERTa: A Robustly Optimized BERT Pretraining Approach paper. It builds on BERT and modifies key hyperparameters, removing the next-sentence pre-training objective and training with much larger mini-batches and learning rates. Additional details about the model can be found in a blog post by Meta AI and in the Hugging Face documentation

Now that we know more about FLAN-T5 and RoBERTa, let us get started. 🚀


First, we need to install the Hugging Face Optimum library accelerated by OpenVINO integration. The Hugging Face Optimum API is a high-level API that enables us to convert and quantize models from the Hugging Face Transformers library to the OpenVINO™ IR format. For more details, refer to the Hugging Face Optimum documentation.

!pip install -q "git+" onnx onnxruntime

Download and Convert Models

Optimum Intel can be used to load optimized models from the Hugging Face Hub and create pipelines to run an inference with OpenVINO Runtime using Hugging Face APIs. The Optimum Inference models are API compatible with Hugging Face Transformers models. This means we just need to replace AutoModelForXxx class with the corresponding OVModelForXxx class.

Below is an example of the RoBERTa text classification model

-from transformers import AutoModelForSequenceClassification
+from import OVModelForSequenceClassification
from transformers import AutoTokenizer, pipeline

model_id = "textattack/roberta-base-CoLA"
-model = AutoModelForSequenceClassification.from_pretrained(model_id)
+model = OVModelForSequenceClassification.from_pretrained(model_id, from_transformers=True)

Model class initialization starts with calling from_pretrained method. When downloading and converting Transformers model, the parameter from_transformers=True should be added. We can save the converted model for the next usage with the save_pretrained method. Tokenizer class and pipelines API are compatible with Optimum models.

from pathlib import Path
from transformers import pipeline, AutoTokenizer
from import OVModelForSeq2SeqLM, OVModelForSequenceClassification
2023-02-22 08:52:28.563283: I tensorflow/core/util/] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable TF_ENABLE_ONEDNN_OPTS=0.
/home/ea/work/my_ov/openvino/tmp_notebooks_env/lib/python3.8/site-packages/openvino/offline_transformations/ FutureWarning: The module is private and following namespace offline_transformations will be removed in the future, use openvino.runtime.passes instead!
INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino
No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'

Grammar Checker

grammar_checker_model_id = "textattack/roberta-base-CoLA"
grammar_checker_dir = Path("roberta-base-cola")
grammar_checker_tokenizer = AutoTokenizer.from_pretrained(grammar_checker_model_id)

if grammar_checker_dir.exists():
    grammar_checker_model = OVModelForSequenceClassification.from_pretrained(grammar_checker_dir)
    grammar_checker_model = OVModelForSequenceClassification.from_pretrained(grammar_checker_model_id, from_transformers=True)

Let us check model work, using inference pipeline for text-classification task. You can find more information about usage Hugging Face inference pipelines in this tutorial

input_text = "They are moved by salar energy"
grammar_checker_pipe = pipeline("text-classification", model=grammar_checker_model, tokenizer=grammar_checker_tokenizer)
result = grammar_checker_pipe(input_text)[0]
print(f"input text: {input_text}")
print(f'predicted label: {"contains_errors" if result["label"] == "LABEL_1" else "no errors"}')
print(f'predicted score: {result["score"] :.2}')
input text: They are moved by salar energy
predicted label: contains_errors
predicted score: 0.88

Great! Looks like the model can detect errors in the sample.

Grammar Corrector

The steps for loading the Grammar Corrector model are very similar, except for the model class that is used. Because FLAN-T5 is a sequence-to-sequence text generation model, we should use the OVModelForSeq2SeqLM class and the text2text-generation pipeline to run it.

grammar_corrector_model_id = "pszemraj/flan-t5-large-grammar-synthesis"
grammar_corrector_dir = Path("flan-t5-large-grammar-synthesis")
grammar_corrector_tokenizer = AutoTokenizer.from_pretrained(grammar_corrector_model_id)

if grammar_corrector_dir.exists():
    grammar_corrector_model = OVModelForSeq2SeqLM.from_pretrained(grammar_corrector_dir)
    grammar_corrector_model = OVModelForSeq2SeqLM.from_pretrained(grammar_corrector_model_id, from_transformers=True)
In-place op on output of tensor.shape. See
In-place op on output of tensor.shape. See
grammar_corrector_pipe = pipeline("text2text-generation", model=grammar_corrector_model, tokenizer=grammar_corrector_tokenizer)
result = grammar_corrector_pipe(input_text)[0]
print(f"input text:     {input_text}")
print(f'generated text: {result["generated_text"]}')
input text:     They are moved by salar energy
generated text: They are powered by solar energy.

Nice! The result looks pretty good!

Prepare Demo Pipeline

Now let us put everything together and create the pipeline for grammar correction. The pipeline accepts input text, verifies its correctness, and generates the correct version if required. It will consist of several steps:

  1. Split text on sentences.

  2. Check grammatical correctness for each sentence using Grammar Checker.

  3. Generate an improved version of the sentence if required.

import re
import transformers
from tqdm.notebook import tqdm

def split_text(text: str) -> list:
    Split a string of text into a list of sentence batches.

    text (str): The text to be split into sentence batches.

    list: A list of sentence batches. Each sentence batch is a list of sentences.
    # Split the text into sentences using regex
    sentences = re.split(r"(?<=[^A-Z].[.?]) +(?=[A-Z])", text)

    # Initialize a list to store the sentence batches
    sentence_batches = []

    # Initialize a temporary list to store the current batch of sentences
    temp_batch = []

    # Iterate through the sentences
    for sentence in sentences:
        # Add the sentence to the temporary batch

        # If the length of the temporary batch is between 2 and 3 sentences, or if it is the last batch, add it to the list of sentence batches
        if len(temp_batch) >= 2 and len(temp_batch) <= 3 or sentence == sentences[-1]:
            temp_batch = []

    return sentence_batches

def correct_text(text: str, checker: transformers.pipelines.Pipeline, corrector: transformers.pipelines.Pipeline, separator: str = " ") -> str:
    Correct the grammar in a string of text using a text-classification and text-generation pipeline.

    text (str): The inpur text to be corrected.
    checker (transformers.pipelines.Pipeline): The text-classification pipeline to use for checking the grammar quality of the text.
    corrector (transformers.pipelines.Pipeline): The text-generation pipeline to use for correcting the text.
    separator (str, optional): The separator to use when joining the corrected text into a single string. Default is a space character.

    str: The corrected text.
    # Split the text into sentence batches
    sentence_batches = split_text(text)

    # Initialize a list to store the corrected text
    corrected_text = []

    # Iterate through the sentence batches
    for batch in tqdm(
        sentence_batches, total=len(sentence_batches), desc="correcting text.."
        # Join the sentences in the batch into a single string
        raw_text = " ".join(batch)

        # Check the grammar quality of the text using the text-classification pipeline
        results = checker(raw_text)

        # Only correct the text if the results of the text-classification are not LABEL_1 or are LABEL_1 with a score below 0.9
        if results[0]["label"] != "LABEL_1" or (
            results[0]["label"] == "LABEL_1" and results[0]["score"] < 0.9
            # Correct the text using the text-generation pipeline
            corrected_batch = corrector(raw_text)

    # Join the corrected text into a single string
    corrected_text = separator.join(corrected_text)

    return corrected_text

Let us see it in action. Enter text to be corrected in the text box and execute the following cells.

import ipywidgets as widgets

text_widget = widgets.Textarea(value="Most of the course is about semantic or  content of language but there are also interesting topics to be learned from the servicefeatures except statistics in characters in documents."
                               "At this point, He introduces herself as his native English speaker and goes on to say that if you contine to work on social scnce",
                               description='your text', layout=widgets.Layout(width="auto"))
Textarea(value='Most of the course is about semantic or  content of language but there are also interesting to…
corrected_text = correct_text(text_widget.value, grammar_checker_pipe, grammar_corrector_pipe)
correcting text..:   0%|          | 0/1 [00:00<?, ?it/s]
print(f"input text:     {text_widget.value}\n")
print(f'generated text: {corrected_text}')
input text:     Most of the course is about semantic or  content of language but there are also interesting topics to be learned from the servicefeatures except statistics in characters in documents.At this point, He introduces herself as his native English speaker and goes on to say that if you contine to work on social scnce

generated text: Most of the course is about the semantic content of language but there are also interesting topics to be learned from the service features except statistics in characters in documents. At this point, she introduces herself as a native English speaker and goes on to say that if you continue to work on social science, you will continue to be successful.