---
license: mit
language:
- en
pipeline_tag: text2text-generation
library_name: adapter-transformers
---
# Model Card for Model ID

<!-- Briefly summarize what the model is/does. -->

This is an English grammar correction model. 


## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->



- **Developed by:** Amin Rahmani
- **Model type:** T5
- **Language(s) (NLP):** English
- **License:** MIT

## How to Get Started with the Model

from happytransformer import  HappyTextToText

happy_tt = HappyTextToText("T5", ".\PATH TO MODEL")

from happytransformer import TTSettings

beam_settings =  TTSettings(num_beams=8, min_length=1, max_length=100)

input_text_1 = "grammar: hi dear"

output_text_1 = happy_tt.generate_text(input_text_1, args=beam_settings)
print(output_text_1.text)


[More Information Needed]


#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

validation loss: 0.04
learning rate:
epochs: 3


## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** RTX 3090


## Technical Specifications [optional]