Grammar Synthesis
Collection
From nothing; grammar. • 6 items • Updated
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YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
bart-base-grammar-synthesis
This model is a fine-tuned version of facebook/bart-base on an expanded version of the JFLEG dataset.
You can find other grammar-synthesis models by searching for the grammar synthesis tag
Basic Usage Example
Installation
First, make sure you have the transformers package installed. You can install it using pip:
pip install -U transformers
Usage
from transformers import pipeline
# Initialize the text-generation pipeline for text correction
corrector = pipeline("text2text-generation", "pszemraj/bart-base-grammar-synthesis")
# Example text to correct
raw_text = "The toweris 324 met (1,063 ft) tall, about height as .An 81-storey building, and biggest longest structure paris. Is square, measuring 125 metres (410 ft) on each side. During its constructiothe eiffel tower surpassed the washington monument to become the tallest man-made structure in the world, a title it held for 41 yearsuntilthe chryslerbuilding in new york city was finished in 1930. It was the first structure to goat a height of 300 metres. Due 2 the addition ofa brdcasting aerial at the t0pp of the twr in 1957, it now taller than chrysler building 5.2 metres (17 ft). Exxxcluding transmitters, eiffel tower is 2ndd tallest ree-standing structure in france after millau viaduct."
# Correct the text using the text-generation pipeline
corrected_text = corrector(raw_text)[0]["generated_text"]
# Print the corrected text
print(corrected_text)
This example demonstrates how to use the text-generation pipeline to correct the grammar in a given text. The corrector pipeline is initialized with the "pszemraj/bart-base-grammar-synthesis" model, which is designed for grammar correction. The corrector pipeline takes the raw text as input and returns the corrected text. Make sure to install the required dependencies and models before running the code.
Intended uses & limitations
- robust grammar correction
- the model has a license of
cc-by-nc-sa-4.0as it uses the JFLEG dataset + augments it for training
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.02
- num_epochs: 3.0
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