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---
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] |