File size: 3,046 Bytes
f7ec05d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 |
---
dataset_name: grammar-correction
description: >
A refined subset of the liweili/c4_200m dataset, derived from Google's C4_200M Synthetic Dataset for Grammatical Error Correction.
Contains sentence pairs where the input is ungrammatical and the output is grammatical, suitable for training grammatical error correction (GEC) models.
train_size: 100000
validation_size: 25000
data_fields:
- input: "Ungrammatical sentence (string)"
- output: "Corrected, grammatical sentence (string)"
intended_uses: >
Designed for training and evaluating GEC models to automatically identify and correct grammatical errors in text.
limitations:
- "Synthetic data may not fully capture real-world grammatical errors."
- "Filtering may introduce biases based on classifier performance."
- "Focused on English grammar, limiting applicability to other languages or dialects."
---
# grammar-correction
## Dataset Summary
The grammar-correction dataset is a refined subset of the [liweili/c4_200m](https://huggingface.co/datasets/liweili/c4_200m) dataset,
derived from Google's [C4_200M Synthetic Dataset for Grammatical Error Correction](https://github.com/google-research-datasets/C4_200M-synthetic-dataset-for-grammatical-error-correction).
It contains sentence pairs where the input is ungrammatical and the output is grammatical, making it suitable for training grammatical error correction (GEC) models.
## Dataset Structure
- **Train set**: 100 000 entries
- **Validation set**: 25 000 entries
### Data Fields
- **input**: Ungrammatical sentence (string)
- **output**: Corrected, grammatical sentence (string)
## Dataset Creation
### Source Data
This dataset is based on the liweili/c4_200m dataset, which includes 185 million sentence pairs generated from a cleaned English corpus.
### Filtering Methodology
Entries were filtered using the [agentlans/snowflake-arctic-xs-grammar-classifier](https://huggingface.co/agentlans/snowflake-arctic-xs-grammar-classifier)
to retain only those with ungrammatical inputs and grammatical outputs, enhancing the quality of training data for GEC tasks.
## Considerations for Using the Data
### Intended Uses
Designed for training and evaluating GEC models to automatically identify and correct grammatical errors in text.
### Limitations and Biases
1. The dataset is synthetic and may not fully capture real-world grammatical errors.
2. Filtering may introduce biases based on classifier performance.
3. Focused on English grammar, limiting applicability to other languages or dialects.
## Additional Information
### Dataset Curators
- Curated by filtering the liweili/c4_200m dataset, based on Google's C4_200M dataset.
- Please check the licenses of those datasets for usage rights.
- Also acknowledge the use of the agentlans/snowflake-arctic-xs-grammar-classifier for filtering.
### Contributions
This dataset aims to provide high-quality resources for GEC model training and evaluation, building on the efforts of Google Research and the Hugging Face community. |