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
Model
This model utilizes the Flan-T5-base pre-trained model and has been fine-tuned using the JFLEG dataset with the assistance of the Happy Transformer framework. Its primary objective is to correct a wide range of potential grammatical errors that sentences might contain including issues with punctuation, typos, prepositions, and more.
Usage with Transformers
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("Sajid030/t5-base-grammar-synthesis")
model = AutoModelForSeq2SeqLM.from_pretrained("Sajid030/t5-base-grammar-synthesis")
text = "One person if do n't have good health that means so many things they could lost ."
inputs = tokenizer("grammar:"+text, truncation=True, return_tensors='pt')
output = model.generate(inputs['input_ids'])
correction=tokenizer.batch_decode(output, skip_special_tokens=True)
print("".join(correction)) #Correction: If one person doesn't have good health, so many things could be lost.
Usage with HappyTransformers
from happytransformer import HappyTextToText, TTSettings
happy_tt = HappyTextToText("T5", "Sajid030/t5-base-grammar-synthesis")
args = TTSettings()
sentence = "Much many brands and sellers still in the market."
result = happy_tt.generate_text("grammar: "+ sentence, args=args)
print(result.text) # Many brands and sellers are still in the market.
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