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--- |
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license: mit |
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language: |
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- en |
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pipeline_tag: text2text-generation |
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library_name: adapter-transformers |
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--- |
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# Model Card for Model ID |
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<!-- Briefly summarize what the model is/does. --> |
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This is an English grammar correction model. |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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- **Developed by:** Amin Rahmani |
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- **Model type:** T5 |
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- **Language(s) (NLP):** English |
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- **License:** MIT |
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## How to Get Started with the Model |
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from happytransformer import HappyTextToText |
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happy_tt = HappyTextToText("T5", ".\PATH TO MODEL") |
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from happytransformer import TTSettings |
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beam_settings = TTSettings(num_beams=8, min_length=1, max_length=100) |
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input_text_1 = "grammar: hi dear" |
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output_text_1 = happy_tt.generate_text(input_text_1, args=beam_settings) |
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print(output_text_1.text) |
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[More Information Needed] |
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#### Training Hyperparameters |
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
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#### Speeds, Sizes, Times [optional] |
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validation loss: 0.04 |
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learning rate: |
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epochs: 3 |
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## Environmental Impact |
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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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). |
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- **Hardware Type:** RTX 3090 |
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## Technical Specifications [optional] |
