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---
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language: en
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tags:
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- sentence correction
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- text2text-generation
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license: cc-by-nc-sa-4.0
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datasets:
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- jfleg
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---
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# Model
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This model utilises T5-base sentence correction pre-trained model. It was fine tuned using a modified version of the [JFLEG](https://arxiv.org/abs/1702.04066) dataset and [Happy Transformer framework](https://github.com/EricFillion/happy-transformer). This model was pre-trained for educational purposes only for correction on local Caribbean dialect. For more on Caribbean dialect checkout the library [Caribe](https://pypi.org/project/Caribe/).
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.
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___
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# Re-training/Fine Tuning
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The results of fine-tuning resulted in a final accuracy of 90%
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# Usage
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```python
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from happytransformer import HappyTextToText, TTSettings
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pre_trained_model="T5"
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model = HappyTextToText(pre_trained_model, "KES/T5-KES")
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arguments = TTSettings(num_beams=4, min_length=1)
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sentence = "Wat iz your nam"
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correction = model.generate_text("grammar: "+sentence, args=arguments)
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if(correction.text.find(" .")):
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correction.text=correction.text.replace(" .", ".")
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print(correction.text) # Correction: "What is your name?".
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```
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_
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# Usage with Transformers
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```python
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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tokenizer = AutoTokenizer.from_pretrained("KES/T5-KES")
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model = AutoModelForSeq2SeqLM.from_pretrained("KES/T5-KES")
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text = "I am lived with my parenmts "
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inputs = tokenizer("grammar:"+text, truncation=True, return_tensors='pt')
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output = model.generate(inputs['input_ids'], num_beams=4, max_length=512, early_stopping=True)
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correction=tokenizer.batch_decode(output, skip_special_tokens=True)
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print("".join(correction)) #Correction: I am living with my parents.
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```
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