
# Model

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.

# 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.
```
{"language": ["en"], "license": "apache-2.0", "tags": ["grammar correction"], "datasets": ["jfleg"], "pipeline_tag": "text2text-generation"}
transformers pytorch safetensors t5 text2text-generation grammar correction en dataset:jfleg license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us