| | --- |
| | license: mit |
| | language: |
| | - en |
| | pipeline_tag: text2text-generation |
| | library_name: adapter-transformers |
| | --- |
| | # 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 |
| | - **Model type:** T5 |
| | - **Language(s) (NLP):** English |
| | - **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 |
| | |
| | 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) |
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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 |
| | learning rate: |
| | 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] |
