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--- |
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license: apache-2.0 |
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datasets: |
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- jfleg |
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language: |
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- en |
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pipeline_tag: text2text-generation |
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tags: |
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- grammar correction |
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--- |
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# Model |
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This model utilizes the [Flan-T5-base](https://huggingface.co/google/flan-t5-base) pre-trained model and has been fine-tuned using the [JFLEG](https://huggingface.co/datasets/jfleg) dataset with the assistance of the [Happy Transformer](https://github.com/EricFillion/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. |
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# Usage with Transformers |
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``` |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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tokenizer = AutoTokenizer.from_pretrained("Sajid030/t5-base-grammar-synthesis") |
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model = AutoModelForSeq2SeqLM.from_pretrained("Sajid030/t5-base-grammar-synthesis") |
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text = "One person if do n't have good health that means so many things they could lost ." |
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inputs = tokenizer("grammar:"+text, truncation=True, return_tensors='pt') |
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output = model.generate(inputs['input_ids']) |
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correction=tokenizer.batch_decode(output, skip_special_tokens=True) |
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print("".join(correction)) #Correction: If one person doesn't have good health, so many things could be lost. |
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``` |
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# Usage with HappyTransformers |
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``` |
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from happytransformer import HappyTextToText, TTSettings |
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happy_tt = HappyTextToText("T5", "Sajid030/t5-base-grammar-synthesis") |
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args = TTSettings() |
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sentence = "Much many brands and sellers still in the market." |
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result = happy_tt.generate_text("grammar: "+ sentence, args=args) |
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print(result.text) # Many brands and sellers are still in the market. |
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``` |
